tag:blogger.com,1999:blog-81910304184748483862024-03-13T11:41:14.819-04:00Rapid Insight: Data AnalyticsUnknownnoreply@blogger.comBlogger77125tag:blogger.com,1999:blog-8191030418474848386.post-38011965607338994712013-09-12T14:57:00.001-04:002013-09-12T15:43:31.676-04:00Crossing Party Lines with Predictive Modeling<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">With the rise of Nate Silver and the emergence of mainstream data science, we've seen many uses for predictive analytics, including the entrance of predictive modeling into the political arena. Actually, although predicting election results is a booming business now, it has been around for quite some time. </span></div>
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<a href="http://2.bp.blogspot.com/-O5c6PAd5qeY/UjH-7ezNoeI/AAAAAAAAAfY/dtqFI3ulwhU/s1600/hennessy.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="150" src="http://2.bp.blogspot.com/-O5c6PAd5qeY/UjH-7ezNoeI/AAAAAAAAAfY/dtqFI3ulwhU/s200/hennessy.jpg" width="200" /></a><span style="font-family: Georgia, Times New Roman, serif;">I recently got the chance to talk to Matt Hennessy, Managing Director at <a href="http://www.tremontpublicadvisors.com/index.htm" target="_blank">Tremont Public Advisors</a>, about a campaign he worked on for Joe Lieberman in 2006, and how they implemented predictive modeling for a successful Senate election. For those who are interested, we'll be discussing this and other examples of predictive modeling in action in a<a href="https://www2.gotomeeting.com/register/932671106" target="_blank"> webinar</a> on Tuesday, September 17th. </span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Can you give us some
background on the 2006 Senate election?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">In 2006 in Connecticut, Joe Lieberman was up for reelection
to the Senate as a Democrat. He had been the Vice Presidential nominee in the
2000 election and had taken a position supporting the Iraq war which upset a
lot of the Democratic base. He wound up losing the Democratic primary to Ned
Lamont who won on a big anti-war push. Once Lieberman lost the primary
election, he lost access to a considerable amount of infrastructure – union
support, door to door field workers, and all of the other boots on the ground
that he would have had were all gone. He lost most of his staff except for the
people who had been there for a decade or two. He needed to figure out how to
replace some of the advantages he’d had with other resources out there. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">As someone advising him, I saw that we had a problem:
without a field operation and all of those bodies, we didn’t know exactly who
we wanted to get out the vote and who the likely voters for Lieberman were. We
had a very expensive polling operation going which <span class="msoDel"><del cite="mailto:Caitlin%20Garrett" datetime="2013-09-12T13:42"> </del></span>was using the conventional method
to reach some conclusions about which demographics were most likely to vote,
but we decided that we needed something more. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How was the decision
made to use predictive analytics in the campaign?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">The resources that normally would be used for generating
‘get out the vote’ or direct voter contact were gone the day after the primary.
Usually we’d go out and try to visit all of the potential voters, but this just
wasn’t possible anymore. We needed to figure out a way to work smarter to
compensate for a new lack of resources. We wondered if there was a way to
determine which characteristics indicated a likelihood of voting for Lieberman
so that we could figure out exactly who to pull out on Election Day. After a
conversation with Mike Laracy about performing this type of analysis, we
decided to give predictive modeling a try. Our goal was to score every registered voter on their likelihood of voting for Lieberman,
and we used <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a> to build a model to do that. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">We knew what data we had, the voter file, and determined
which additional information we would need to build a model, like demographic
information. Then we hired a polling company to call about 10,000 named voters in
a random phone pull so that we’d have a statistically significant result. The
poll question was a very simple yes/no question on likelihood to vote and who
each voter planned on voting for. We weren’t trying to persuade people at this
point; all of this polling was meant to influence the field side, not the
messaging side. This approach was different than what we’d been doing
before because we were calling named voters – people who actually existed and
were registered to vote and had demographic information that we could attach to
them – and polling them. Using this poll, we scored each of the 1.9 million registered
voters in <span class="msoDel"><del cite="mailto:Caitlin%20Garrett" datetime="2013-09-12T13:42"> </del></span>Connecticut
on their likelihood of voting for Senator Lieberman. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How did predictive
analytics help the campaign?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Predictive modeling allowed us to optimize our limited resources. As opposed to working with pure assumptions, we now had an
actual score attached to each individual voter, which allowed us to spend our
resources on the voters with the highest propensity to vote for
Lieberman. At the time, it was quite a cutting-edge use of analytics – it was
the first time anyone had ever scored an entire state’s voters for the purposes
of an election. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Another thing the predictive model did was to disprove our
assumptions about who was likely to vote for Lieberman. Some of the key
indicators that we were getting from the traditional pollsters were proven to be incorrect by the model results. Based on this we changed some of our campaign messaging The model allowed us to
re-allocate our resources more efficiently and it challenged some of the notions
we held. In the end, the model did a good job of predicting who the voters
would be. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Do you think
predictive modeling affected the outcome of the election?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">It’s difficult to say, but I can say that the resources that
were deployed based on the predictive model were effective. Once we started
deploying based on the modeling, the polling margins started to increase; this
was toward the end of the race, which is when this model was implemented. I
think it increased the margin of victory. The polling was showing a very tight
race, but the predictive model was showing there was a margin of victory for
Lieberman that was already there, and it was actually ahead of the polling in
this case. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How do you see
predictive modeling being used in future elections?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">If you look at what the Obama campaign did with predictive
modeling – taking different factors and a complex web of data points to
pinpoint individuals who are likely to vote – it’s here. Predictive modeling is
here, it’s now; that’s the future of elections. The complexity of the work
they’re doing in this field is truly amazing. I don’t think it will be as
focused on many of the smaller races – like those below governor, but it can be
very, very effective. I think this last election confirmed that it’s a major
part of any political campaign that’s being conducted on scale. This is here to
stay.</span><o:p></o:p></div>
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<span style="font-family: Georgia, Times New Roman, serif;">This example of predictive modeling in action is one of three that we'll be co-presenting in a webinar with <a href="http://www.rapidinsightinc.com/tableau" target="_blank">Tableau</a> on Tuesday, September 17th, "Turbocharge your Predictive Models with Visualizations". For more information, or to register, <a href="https://www2.gotomeeting.com/register/932671106" target="_blank">click here</a>. </span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">*</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><a href="http://www.tremontpublicadvisors.com/about.htm" target="_blank">Matt Hennessy</a> has over two decades of experience in federal, state, and city government. He has built a reputation as a trusted and effective advisor to leading elected officials on public policy, communications and campaign issues. He has served as a trusted political advisor and fundraiser for candidates and political campaigns ranging from Mayor to U.S. Senator to President. Matt is an alumnus of Harvard Business School and the Kennedy School of Government where he was a Wasserman Fellow. He also holds degrees from the Catholic University of America and trinity College in Hartford.</span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-46213996123223194792013-09-04T16:41:00.000-04:002013-09-04T16:56:06.097-04:00#TCC13<div class="MsoNormal">
<a href="http://1.bp.blogspot.com/-dVkGky32Czg/UieZZ0uW0HI/AAAAAAAAAfE/4g6qujHVZI8/s1600/Ric+JJ+Profile.JPG" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://1.bp.blogspot.com/-dVkGky32Czg/UieZZ0uW0HI/AAAAAAAAAfE/4g6qujHVZI8/s200/Ric+JJ+Profile.JPG" width="151" /></a><span style="font-family: Georgia, Times New Roman, serif;">In honor of the upcoming <a href="http://tcc13.tableauconference.com/" target="_blank">Tableau Customer Conference</a> and <a href="http://www.rapidinsightinc.com/tableau" target="_blank">our recent partnership</a>, I sat down with Rapid Insight President & COO, Ric Pratte, to talk about the partnership, what to expect from Rapid Insight at the conference, and how predictive analytics and data visualization go together. </span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Can you talk about
the partnership between Rapid Insight and Tableau?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">It was an observation that a number of our very successful
clients were using Tableau, and it was this observation that led us to build a
partnership. We have strengths in massaging, analyzing, and predicting data,
and we’ve gained a partner who helps communicate that data to executives and
decision makers in a way that’s easily understood. It’s a very complementary
relationship. We both share a common position focused on empowering business
users to make data-driven decisions by using their data to look forward. Essentially,
we’re working together to help people visualize the future. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">We now have a <a href="http://www.rapidinsightinc.com/tableau" target="_blank">Tableau page</a> on our website that we’re constantly updating so that visitors can
continue to learn about the power of combining predictive modeling and
visualization, and that’s a great place
to get more information. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How do the two
products interface?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Actually our interfaces focus on the same methodology – no coding,
and the user manipulates graphical objects, places them where they need to, and
literally connects the dots to perform an analysis. We’re able to natively
connect the output of data analysis from our tools directly into the Tableau
system as a .tde system, as well as the ability to output to the cloud. The
process is very smooth. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How can visualization
enhance predictive analytics work?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Visualization provides the end user a better way to see the
results of a predictive model applied in the context of a business problem. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">For example, a heat map overlay onto a geographic region of
customers who are most likely to renew, purchase, or enroll tells a much more
powerful story than summary statistics or a table containing the same
information. Visualizations are great for storytelling and physically seeing
things in your data that you may have missed in a more black and white
analysis. By adding a visual component to their predictive analytics work,
users can make data-driven decisions faster. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">What value does predictive analytics add to your visualizations?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">It’s one thing to know where your current customers are, but it’s a different thing to know where your future customers will be coming from. If your visualizations are based on traditional data analytics, you can think of them as looking at what’s in your rearview mirror. Useful, but driving your car while looking in your rearview may not get you where you want to go. Using your data to look at what’s coming down the road will help you set a clear path based on data-driven decisions. Once you’ve predicted the probability of future outcomes, you can focus your resources accordingly.</span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">What can attendees
expect to see from Rapid Insight at TCC13?<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">We’ll be doing some short sessions with attendees so that
they can see the entire process – all the way from data extracting and
federating through the data cleanup and modeling phase, and ending with how to
bring the data into Tableau for visualizations. We’ll show the contrast between
predictive and non-predictive visual outputs to demonstrate the power of predictive
analytics. We’ll have a few examples and datasets to play with. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">We’ll be sending part of our executive team – Mike, Sheryl,
and myself – and our booth will be fully stocked with chocolate, which are also
great reasons to stop by. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Mike Laracy is
co-presenting with Yale at TCC13. What do you expect from their presentation?<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Yale has lots of and lots of data and needed to find the
most efficient way to analyze it to predict donor behavior. Their presentation, "Fusing Predictive Analytics and Data Visualization", will be on Tuesday September 10th at 3pm in Annapolis 3-4. They’ll be
presenting a case study on their successful use of predictive modeling and
discuss how they are sharing and communicating the results through
visualization. This will be another expanded example of the full process with
the added viewpoint of the end customer and their experience in starting this,
the iterations they’ve went through, and how they’ve reached success.</span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How can I learn about
the partnership if I’m not attending the conference?</span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">After the conference, we have a follow-up webinar on September 17th for
everyone who can’t attend. It’s a co-hosted webinar between Rapid Insight and
Tableau where we have some data analysts who will show some new examples of the
process. It will be a good way to gain an understanding of how you can put
predictive analytics to use to gain a competitive advantage for your business. [For more information, or to register, <a href="https://www2.gotomeeting.com/register/932671106" target="_blank">click here</a>.]</span><o:p></o:p></div>
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<span style="font-family: Georgia, Times New Roman, serif;">*</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Ric Pratte, President and COO of <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a>, is a longtime entrepreneur with a history of building innovative software companies. He was previously the CEO/Co-founder of JitterJam, a pioneer of Social CRM that was acquired by the Meltwater Group in 2011. He is a father of two, an avid skier and backpacker, and devotes time and energy to numerous non-profit organizations including Girls, Inc. and the Boy Scouts. You can follow him on Twitter at <a href="https://twitter.com/ricpratte" target="_blank">@ricpratte</a>.</span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-23485766127867502612013-08-30T10:15:00.002-04:002013-08-30T14:59:27.234-04:00Here's to the Skeptics: Addressing Predictive Modeling Misconceptions<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="http://2.bp.blogspot.com/-BINuDkpIQiA/UiCoi7upxFI/AAAAAAAAAe0/jx_EWwuH_0s/s1600/2546693421_3ef3d3284d.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="160" src="http://2.bp.blogspot.com/-BINuDkpIQiA/UiCoi7upxFI/AAAAAAAAAe0/jx_EWwuH_0s/s200/2546693421_3ef3d3284d.jpg" width="200" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><a href="http://www.flickr.com/photos/jonnygoldstein/" target="_blank">Photo credit: Jonny Goldstein</a></td></tr>
</tbody></table>
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<span style="font-family: Georgia, Times New Roman, serif;">As a full-time analytics professional, I have a hard time
conceiving of people who have not fully embraced the power of predictive
analytics, but I know they’re out there and I think it’s important to address
their concerns. In doing so, I’m not here to argue that predictive analytics is
a perfect fit for every organization. Predictive analytics requires investment:
in your data, in infrastructure and technology, and of your time. It’s also an
investment in your company, your internal knowledge base, and your future. I’m
here to argue that the investment is worth it. </span></div>
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<span style="font-family: Georgia, 'Times New Roman', serif;">To do so, I’ve presented a few
clarifications to address predictive modeling concerns that I’ve heard from
skeptics. If you have anything to add, or if there are any big concerns I’ve
missed, let me know in the comments.</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">You don’t need to be
a PhD statistician to build predictive models<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">A working knowledge of statistics will help you to better
interpret the results of predictive models, but you don’t need ten years’
experience or a doctorate degree to glean insight or utilize the output from a
model. There are software packages out there with diagnostics that can help you
understand which variables are important, which are not, and why. Knowing your data is equally important as statistical knowledge, and both will serve you well in the long run. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">A predictive model
shouldn’t be a black box<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">There are plenty of companies and consultants whose
predictive models could fall into the <a href="http://en.wikipedia.org/wiki/Black_box" target="_blank">“black box”</a> category. The model building process, in this case, involves sending your data to an
outside party who analyzes it and returns you a series of scores. On the
surface, this may not seem like a bad thing, but once you’ve built your first
model, you’ll understand why this is not nearly as valuable as doing it
yourself. While the output scores are important, you also want to know about
the variables used, how the model handled any missing or outlying variables,
and glean insight beyond a single set of scores so that you can change or
monitor specific behaviors going forward.</span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Even if you know your data, modeling can help</span></b><br />
<span style="font-family: Georgia, 'Times New Roman', serif;">A finished predictive model will do one of two things: confirm what you’ve always believed, or bring new insights to light. In our office, we refer to this idea as “turn or confirm” – a model will either turn or confirm the things you’ve thought to be true. Most of the time, models will do both. This allows you to both validate any anecdotal evidence you might have (or realize that correlations might not be as strong as you thought) and take a look at new variables or connections that you may not have picked up on before. </span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><b>Predictive models can be implemented quickly</b></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">I've heard some horror stories about a model taking months, or even years, to implement. If this is the case at your institution, you're doing it wrong. At this point, predictive modeling software has become incredibly efficient - usually able to turn out models within seconds or minutes. The bulk of time spent working on a model is typically spent on the data clean-up, which will vary from company to company. In any case, this is time well spent. Clean data is just as good for reporting, dashboarding, and visualizing as it is for predictive modeling.</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">Predictive models enhance human judgment, not replace it<o:p></o:p></span></b></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">If models were meant to replace human judgment, I too would
be uncomfortable and suspicious of the idea. However, 99% of the time, the aim
of predictive modeling is to enhance and expand human expertise to allow us
(the end users) to be better-informed and more data-driven in our decision
making. <o:p></o:p></span></div>
<div class="MsoNormal">
<br />
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett, Statistical Analyst at <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a></span></div>
Unknownnoreply@blogger.com23tag:blogger.com,1999:blog-8191030418474848386.post-69827172725644748352013-08-14T15:00:00.002-04:002013-08-14T15:00:35.520-04:00Big Data and New Methods<a href="http://2.bp.blogspot.com/-4wlCpFlDAV8/UgvL9kGN-gI/AAAAAAAAAec/1HFB_BTytmM/s1600/chuck.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" src="http://2.bp.blogspot.com/-4wlCpFlDAV8/UgvL9kGN-gI/AAAAAAAAAec/1HFB_BTytmM/s1600/chuck.jpg" /></a><span style="font-family: Arial, Helvetica, sans-serif;"><b>Guest post by Chuck McClenon, Fundraising Scientist from University of Texas at Austin </b></span><br />
<span style="font-family: Arial, Helvetica, sans-serif;"><b><br /></b></span>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">When I went to my first APRA Data Analytics Symposium in
2010, the use of analytics in support of philanthropic fundraising was a
novelty. “Analysis”, for most
organizations, consisted of descriptive statistics in Excel. A few pioneers had built regression models,
and the Symposium faculty pretty much consisted of those who could explain the
differences between Ordinary Linear and Logistic Regression. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">What a difference three years has made! At this year’s Symposium in Baltimore we
considered keyword analysis, hierarchical linear modeling, visualization, and
the use of financial industry formulae for portfolio optimization. We have progressed beyond regression and now
have the critical mass of practitioners throwing ideas at each other. And at many of our institutions we are also
accumulating the critical mass of data to support serious mining, and try these
new approaches.<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Alan Schwartz, formerly with ESPN and more recently the New
York Times, gave the keynote address.
Alan had written a series of article for the Times, over several years,
examining the incidence of concussions among NFL players, and their long-term
effects, including early-onset dementia.
One retired player with dementia at age 50 does not tell a story and the
pushback was that there wasn’t enough data, but most of the data is buried in
medical records and team records. The
demand for more data was a case of the “better” being the enemy of the
“good”. This one didn’t really require
Big Data, it just needed Enough Data.
Early onset dementia is normally extremely rare. When you have five cases, in a population of
only 2000+ retired NFL players, it’s hardly chance. Schwartz’ exposition is leading to real changes
in how head injuries are being regarded in football, down to college, high
school, and youth leagues. Tenacity with
data, that’s what analytics is about.<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Divah Yap of the University of Minnesota offered an
intriguing presentation on scoring the free text in contact reports for words
or phrases which may tend to indicate attitude toward the organization. We have a lot of usable data around us, if we
know how to decompose it and connect dots.
When we have enough data, well-organized, we can understand it in ways
we never could before.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Visualization may be coming of age as part of analysis. One
of our fundraising projects here at UT which we have mostly failed at so far is
to find donors for the Texas Advancement Computing Center (TACC) and its
visualization lab. But if we can’t help
them, maybe they can help us. In a few
weeks we’re going to get together with them, and hand them the keys to our data
warehouse, and see if they can paint it in colors we never imagined, and help
us to see it in ways that the numbers alone don’t tell us.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">In my college class on linear methods, we were warned
strictly against correlation fishing. In
your typical experiment in human psychology, p < .05 is the standard, and if
you run your experiments on twenty or fifty or even a hundred subjects, getting
past p < .05 can be a challenge. And
of course the measurement “P = .05” means that there is a one in twenty chance
that the conclusion is wrong. Run ten
such studies, and there’s a 40% likelihood that at least one of your
conclusions, if not more, will be wrong.
<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Taken a different way, and this is where the dictum against
correlation fishing comes in, if you have a file with ten independent
variables, and you threw it into a correlation matrix, there would be 45 pairs
of variables to correlate, and if you set your standard going in as p < .05,
then from those 45 pairings you could expect to draw two false
conclusions. Try it on a file of twenty
variables or more, with hundreds of combinations to test, and there is a real
risk that the apparent correlations are simply the random noise in the sample,
and are as much a reflection of tides and astrology as they are of anything
causative within the population. And
with more variables thrown into the mix, there is also the increasing risk of
multi-collinearity if your variables are in fact numerically related in their
derivations. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">But when we study donor behavior in large organizations, we
move beyond the realm of the psychology lab and limited sample sizes. The University of Texas at Austin has a
constituent database of over 500,000 alumni and friends. I have decades of gift history, and I have
acquired consumer behavior information, derived from point-of-sale and other
sources. People with cats give more to
the arts, people with dogs give more to athletics, but in the end their total
giving is similar. I can say this “with
confidence”, when p < .0001. Big Data tells us stories, and illustrates
them in color. This doesn’t mean that I
can operationalize any strategy dependent on dogs and cats -- especially never depend on cats – but it does
give us new insights.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Coming back to the conference, if there are a half-dozen
presenters offering totally novel approaches to analysis, then the probability
is fairly high that any one of them may be a total waste of time, but there’s a
pretty good chance that at least one or two of them contain real nuggets. That’s the nature of data mining, and it’s
also why we go to conferences, to look for new insights, which may or may not
be usable. Coming away from this year’s
Symposium, many of us are feeling almost overwhelmed by new ideas, and just
wishing we had the time needed to explore all of them. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Big Data? How Big is
big enough, and how much is too big?
That’s becoming a difficult question, and the boundaries of privacy will
be a philosophical argument for years to come.
I’ve reached the unscientific conclusion that market segmentations such
as Claritas or PersonicX clusters are dead on the money 85% of the time, a
little bit off 10% of the time, and absolutely wrong 5% of the time. When there’s so much data around, and They
seem to have such a complete picture of the individual, is it comforting to
know that some of it is probably wrong, and so the picture that They have of us
isn’t as accurate as we’re afraid? When
I talk about cat owners and dog owners, should you be shocked that I know so
much about my constituents, or shocked that I draw conclusions from such
imperfect data? Perhaps both, but Big
Data is becoming reality, and so we will learn to use it for what it is, to use
it wisely and respectfully.<br /></span></div>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Organize, transform, restructure, build a systematic
repository. Mine for connections. And if a you don’t have a supercomputer for
your visualization, <a href="http://www.rapidinsightinc.com/tableau" target="_blank">Tableau</a> may take you a long way.</span><o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
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<span style="font-family: Georgia, Times New Roman, serif;">*</span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">About Chuck: </span><strong style="background-color: white; font-family: 'Helvetica Neue', 'Lucida Grande', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 22.390625px; margin: 0px; padding: 0px;"> </strong><span style="background-color: white; line-height: 22.390625px;"><span style="font-family: Georgia, Times New Roman, serif;">Chuck McClenon arrived at the University of Texas at Austin in 1975, earned a PhD in linguistics, dabbling in the nascent technology of pattern recognition. After a year teaching in English in China, he returned to UT to work in administrative information management, searching for patterns and meaning in data ranging from student course registrations to library book titles to the bit-paths of room keys. He joined the advancement operation as an IT manager in 1996 at the start of UT’s first comprehensive capital campaign. After a brief tour of duty managing the gift processing and donor records operation, he retired to a cave and immersed himself in phonathon results and gift officer contact reports. Now he spends his days acquiring, constructing, managing and analyzing data representing the full spectrum of advancement activity. Since 2006, he has held the official title of Fundraising Scientist.</span></span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-31961828801652694762013-07-30T10:02:00.000-04:002013-07-30T16:32:25.573-04:00Why Nonprofits Should Be Building Predictive Models<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Last fall, the Whitney Museum of American Art decided to take a different approach when deciding which of their prospective donors to mail. They <a href="http://rapidinsight.blogspot.com/2013/05/using-predictive-modeling-to-drive.html" target="_blank">built their first in-house predictive model</a> from the ground up, and felt ready to use it. They shifted their focus away from some of their prospects who "made sense" but had never given, and used the model to inform a large part of their mailing list. Within the first six months of modeling, they received a $10k donation from a donor they would not have mailed using their previous methodology.</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">...And they aren't the only ones. More and more nonprofits are turning to predictive modeling to drive their fundraising. For a more in-depth look at the 'hows' and 'whys', I sat down with a man who founded his own company to provide software so that nonprofits and for-profits alike could start building their own predictive models in-house. He also happens to be my boss and one of the smartest people I know - Mike Laracy:</span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;"><br /></span></b></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">Why would a nonprofit use predictive modeling? How can it
drive fundraising?</span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<a href="http://1.bp.blogspot.com/-1Tz-Ou8KnWM/UfZ7Hv-RfXI/AAAAAAAAAeI/NHg0wLhVcsU/s1600/Mike.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="178" src="http://1.bp.blogspot.com/-1Tz-Ou8KnWM/UfZ7Hv-RfXI/AAAAAAAAAeI/NHg0wLhVcsU/s200/Mike.png" width="200" /></a><span style="font-family: Georgia, Times New Roman, serif;">The quest for any organization,
whether a for-profit or non-profit, is to figure out how to achieve its goals and
to do so in the most efficient and cost-effective manner possible. Predictive
modeling allows an organization to make better decisions and become more
efficient with its use of what are often limited resources. By using analytics, an organization can
better determine who to contact, how often to contact, how much to ask for and
how best to achieve their desired fundraising results. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Although driven by very
different motivations, the relationship between a nonprofit and its donors is
very similar to the relationship between a for-profit company and its
customers. Customers choose whether to
buy a product or not buy a product. They
can become loyal customers or non-loyal customers. They can buy a lot or they can buy very
little. It is much the same story for
nonprofits and their donors. Donors can
be loyal or not loyal. A prospect can
choose to be a donor or not be a donor. They can give large gifts or small
gifts. With accurate data and a modeling
process that is easy to implement, a non-profit can begin to model a donor’s
behavior using the exact same methodologies that are used to model a customer’s
behavior.<o:p></o:p></span></div>
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<span style="color: red; font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">What kinds of resources are needed to start building
predictive models in-house? </span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Without
quality data, predictive modeling isn’t possible. So let’s start with that. There needs to be a system in place that is
capturing an organization’s historical data.
Almost every organization is already capturing their data, so that’s
usually not a problem. The data doesn’t
necessarily need to be organized in a data warehouse. In fact, the data needs to be available in
its raw form, so sometimes having data pre-aggregated in a warehouse can be a
disadvantage. What’s important is that
the data is accessible.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">From a
staffing perspective, you will need a person or people to collect information
on the data, build the models, communicate the results and make sure the models
are being used. There needs to be
someone who is making sure the right information is being collected and the
right information is being communicated.
This can be a single person, but that person needs to make sure that
others in the organization are on board with an understanding of why the models
are being built and how they will be used.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="color: #990000; font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">What are good first steps for an institution looking to get
into predictive modeling?</span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Like any
new initiative, it’s vital to the success of your predictive modeling efforts
that there is universal buy-in across the organization. If there isn’t buy-in, the models won’t be
utilized. To get buy-in, start
small. Go for the early win by building
and implementing a single model. Make
sure others in the organization have an understanding of what the model will
do, how it will be utilized, and most importantly, how the model will benefit
the organization. Once you get that first
win, the interest and buy-in will usually spread quickly across the
organization. As you share the results
of those first few successes, begin to identify who the champions for this
initiative will be. Work with them to
help them communicate the success of the project organization-wide. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">In your experience, how should an institution decide who
should build the predictive models?</span><o:p></o:p></b></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Ideally,
you want someone who has an understanding of the data. If you don’t already have someone with that
knowledge, you want a person who is willing to learn the data. Some understanding of statistics is a plus,
but with current analytic software technology, there is no longer a need to
rely on someone with programming skills or a PhD in statistics to be your data
expert. The people you want to dedicate
as resources for predictive modeling should be creative problem solvers who are
willing to learn. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">What modeling challenges might be unique to different types
of nonprofits? </span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">There are
definitely different needs and different challenges depending on what type of
fundraising entity you are. A college
advancement office, for example, has an advantage in that they have information
on the students who graduated with them.
For example, age comes up as a predictor in many giving models. Whereas an organization like a museum might
not have good info on the age of all of its members and donors, a college or
university will at the very least have each student’s year of graduation, which
is a great proxy for age. A college will
also have great information like the major each student graduated with and
whether or not the current year is a major reunion year. While a non higher-ed entity won’t have this
type of information, they will have information that a college advancement
office won’t have. A museum will have
info on its members, how many times someone has visited the museum, and a lot
of other great information for modeling that a college won’t have.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Another
challenge that people may encounter is how spread out their data is. Some organizations have more sophisticated
computer systems with everything centralized and others may have the
information spread across multiple spreadsheets, databases and even outside
sources. As you determine what your data
needs to look like, keep in mind that you will need to pull it together and do
cleanup before you can begin to model with it.
This was actually one of the reasons we originally created our Veera
product. People were looking for an
easier way to clean up and merge their data before they created their models.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">Are there any common mistakes to avoid when gearing up to
build a model? </span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">I think
the biggest mistake to avoid is building a model without buy-in from the rest
of the organization. Another mistake is
building a model without an implementation/utilization plan. Building and scoring a model is great, but by
itself the model doesn’t do anything for you.
Before building the model you should have a plan for how you are going
to use the model. For example, if you
are a nonprofit and you build a model to predict each donor’s probability of
giving to the annual fund, you need to utilize the model in your annual fund
outreach. You will need a plan to
mail/call the top X% of your donors with the highest probability of giving, or
you should have a plan to not mail donors that are below some probability
threshold. Or perhaps you only want to
mail to donors who are likely to give at least a $500 gift. There are many ways that these models can be
used, but the key is that they have to be used.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Once you
begin to use them, you can also begin the process of refining and measuring the
effectiveness of your models. Then you
can refine them to make them even better.
<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">What kinds of resources/learning opportunities are out there
for those looking to get started with predictive modeling? </span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">In the
fundraising world, APRA and the Data Analytic Symposium have a lot of extremely
useful sessions. I’d also recommend
Prospect DMM, which is a listserv where a lot of really smart people discuss
modeling topics. We (Rapid Insight) put
on a predictive modeling class not too long ago with Brown University and Chuck
McClenon from the University of Texas – Austin.
Classes like those are a great place to get started and we’re thinking
about doing one again soon.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b><span style="color: #990000;">What strategies can you recommend so that a customer gets
the most mileage possible out of their predictive modeling efforts? </span></b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">To borrow
a phrase, I’d say reduce, reuse, recycle.
<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Once
you’ve set up a process for organizing, cleansing and analyzing your data for
one model, you can use that same process for all of your models. In fact, you can even use that same process
for scoring and testing all of your models.
There’s no reason to reinvent the wheel each time. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Another
important strategy is to make sure you set up a system for knowledge
capture. Modeling is an iterative
process; you don’t just build one and you’re done. You can learn a tremendous amount as you’re
building models. A lot of that knowledge
is actually knowledge about your data.
That knowledge will accumulate very quickly over time and will make you
smarter and smarter as an organization.
This is one of the biggest advantages to bringing predictive modeling
in-house: if you are not doing predictive
modeling yourself, you run the risk of that knowledge escaping from your
organization. Once it escapes, you miss
out on an opportunity to grow your organization’s analytic intelligence. <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Remember
the old proverb about giving a man a fish and feeding him for a day versus a
lifetime? The same thing is true with
predictive modeling. If you give an
organization a model; you’ve made them smart for a day. When you give them the tools to build their
own models they become smarter and more competitive for a lifetime.</span><span style="color: #4f81bd;"><o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">**</span><br />
<span style="font-family: Georgia, Times New Roman, serif;">Besides being the <a href="http://rapidinsight.blogspot.com/2013/01/defining-rapid-insight.html" target="_blank">Founder and CEO</a> of <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a>, Mike Laracy is a devoted Birkenstock fan, recently ran up Mount Washington, has an <a href="http://rapidinsight.blogspot.com/2013/07/playlists-for-analysis.html" target="_blank">eclectic taste in music</a>, loves <a href="http://rapidinsight.blogspot.com/2013/01/valuing-analytics-predictive-modeling.html" target="_blank">talking about predictive modeling</a>, is a sap for his two kids, and has pretty much always been a <a href="http://4.bp.blogspot.com/-Vku2iZXCW6s/Ud7tgWXp2TI/AAAAAAAAAdc/SMJVTom1nAE/s1600/mikeeeeee.jpg" target="_blank">nerd</a>. For those of you attending APRA, <a href="http://www.aprahome.org/p/cm/ld/fid=374" target="_blank">he'll be giving a presentation</a> - "Preparing Your Data for Modeling" - on Wednesday, August 7th at 1:30 pm. </span></div>
Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-8191030418474848386.post-57903174772996099122013-07-26T11:39:00.002-04:002013-07-26T11:54:56.330-04:00Predicting Retention for Online Students: Where to Start<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">With the rise of enrollment in online programs and MOOCs,
we’re seeing more and more students forego traditional classroom experiences in
favor of more flexible online programs. With this shift comes a whole new set
of guidelines for enrollment management, financial aid, and retention programs.
Retention, in particular, has seen a significant downward trend as learning
moves from in-person to online classrooms. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<div class="separator" style="clear: both; text-align: center;">
<a href="http://3.bp.blogspot.com/-mIHkrGBs_A4/UfKQ7FY7AgI/AAAAAAAAAd0/inhk2rhkRmI/s1600/280876926_26a1c3db1a.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="212" src="http://3.bp.blogspot.com/-mIHkrGBs_A4/UfKQ7FY7AgI/AAAAAAAAAd0/inhk2rhkRmI/s320/280876926_26a1c3db1a.jpg" width="320" /></a></div>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">My interest lies in figuring out what variables might be
worth including in an analysis attempting to predict online student retention. I did a bit of research and was hoping to find a list of variables online that
had worked in the past but couldn’t find any comprehensive resource, so I’ve
started to build my own. In the sections below, I’ve listed the type of
information that I think would be worth analyzing broken out into four separate
categories. Some of these are variables in and of themselves, and some can be
broken down different ways; for example, “age” can be used by itself, but
creating a “non-traditional age” flag is useful as well. Realistically, not all
schools will have all of this information, so this list is meant to be a good
starting point of what to shoot for when collecting data. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Also, if you have any variables to add (and I’m sure there
are some I’ve missed), I’d love to hear about them in the comments. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">Student Demographic Information</span></b></div>
<div class="MsoNormal">
</div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Socioeconomic status / financial aid information</span></li>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">FAFSA info, Pell eligibility, any scholarship or award info</span></li>
</ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Ethnicity</span></li>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Minority Status</span></li>
</ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Gender</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Home state</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Distance from physical campus (if applicable)</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Age; traditional or non-traditional?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Military background?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Have children?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Currently employed full-time?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">First generation college student?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Legacy student? (Did a parent/grandparent/sibling attend?)</span></li>
</ul>
<div>
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b>
<b><span style="font-family: Georgia, Times New Roman, serif;">Student Online Learning History</span></b></div>
<div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Registered for classes online or in person?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">How many days did they register before the start of the term?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Ever attended a class on-campus?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Do they plan to attend both online and on-campus classes?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Did they attend any type of orientation?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Number of previous online courses taken</span></li>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">First-time online learner?</span></li>
</ul>
</ul>
<div>
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b>
<b><span style="font-family: Georgia, Times New Roman, serif;">Student Academic History</span></b></div>
</div>
<div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">GPA</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">SAT/ACT scores</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Degree hours completed</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Degree hours attempted</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Taking developmental courses?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Transfer student?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Degree program / major </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Program level (Associate, Bachelors, Masters, etc.)</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Number of program or major changes (if applicable)</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Any previous degrees?</span></li>
</ul>
<div>
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b>
<b><span style="font-family: Georgia, Times New Roman, serif;">Course- and Program- Related</span></b></div>
</div>
<div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Amount of text vs. interactive content </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Lessons with immediate feedback?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Any peer-to-peer forum for interaction?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Lessons in real time or recorded?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Amount of teacher interaction with students</span></li>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Chat, email exchange, turn-around time on assignments</span></li>
</ul>
</ul>
<div>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div>
<span style="font-family: Georgia, Times New Roman, serif;"><b>Closing notes:</b></span></div>
</div>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Getting course-related data might be difficult, but the
variables I listed above are derived from studies about how to improve online
courses as being areas to focus on; my thinking is that the more engaged a student
is, both with peers and instructors, the better their chances of online success
are. If you have the data available, it would be worth trying to incorporate it
into your model dataset to see whether or not it is predictive. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Rather than using retention as a y-variable when building these models, we typically create an attrition variable (exactly the opposite of retention) and use that as our y instead. This way, we're getting more directly at the characteristics of a student who is likely to leave rather than stay.</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">Typically when building attrition models, I create separate
models for freshmen and upperclassmen. I’d suggest doing that here as well,
since previous online coursework will probably be a good indicator of future
online coursework. In that case, you’d want to take out many of the variables
listed above when modeling freshmen retention. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Finally, it’s important to keep in mind that student success
has different meanings for different institutions. You could be basing success
on # of credits completed, transitions from semester to semester, or a
particular GPA cutoff, among other indicators. When building these different
types of student success models, you will probably need to tailor some of these
variables to fit the model you're building.</span><o:p></o:p></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett is a Statistical Analyst at <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a></span></div>
</div>
Unknownnoreply@blogger.com5tag:blogger.com,1999:blog-8191030418474848386.post-33346387431262913552013-07-16T13:54:00.002-04:002013-07-26T11:20:24.794-04:00Playlists for Analysis<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">At our recent User Conference, I had a really interesting
conversation with some of our customers about listening to music at work which
got me thinking about the <a href="http://cdn-3.lifehack.org/wp-content/files/2013/06/Sonos-WorkMusic-final-c5.jpg?36b58d" target="_blank">types of music that people listen to in the office</a>. I know that different music works for different people, but I also know from personal experience that different music works for different situations. I listen to different music when I'm doing things like answering emails (or writing blog entries) than I do when I'm in the midst of an analysis. </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">Depending on your office, protocol for
listening to music may be different, but in our office, it’s safe to say that
the analysts are generally working with one ear to their music and one to the
general office sounds. So my question became: "When you're working hard on an analysis, what’s coming from the headphones?" I asked each of the analysts in our office to come up with a playlist that
reflects the type of music they generally listen to when they want to get down
to business. Here’s what our office is listening to:<o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: Georgia, 'Times New Roman', serif;"><b>Mike Laracy, Founder, CEO, and Data Geek:</b></span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><b><br /></b></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">"Within the calmness of these songs, there's a rhythmic intensity that I find helpful for thinking and analyzing (and occasionally for napping). But the songs in my selection also have bits and pieces that are extremely 'rock-out-to-able'. Case in point, Beethoven's 9th (4th movement). Don't be afraid to blast it!!"</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<iframe allowtransparency="true" frameborder="0" height="380" src="https://embed.spotify.com/?uri=spotify:user:1216605385:playlist:6h46kLPiiBGJcPFgG8kyVZ" width="300"></iframe>
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<br />
<span style="font-family: Georgia, Times New Roman, serif;"><b><br /></b></span>
<span style="font-family: Georgia, Times New Roman, serif;"><b>Jeff Fleischer, Director of Client Operations:</b><o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><b><br /></b></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">"I’m a soundtrack guy. I find vocals distract me if I’m
trying to concentrate, so I stick with instrumentals. Here are some of the
things I listen to." [Note: Some of Jeff's tracks weren't on Spotify, like the soundtracks to the <a href="http://www.youtube.com/playlist?list=PLE566427E6E8900F5" target="_blank">Flower</a> and <a href="http://www.youtube.com/watch?v=6LyyLYxdpUQ&list=PL61BEACFAA6FC4E04" target="_blank">Journey</a> video games.]<o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<iframe allowtransparency="true" frameborder="0" height="380" src="https://embed.spotify.com/?uri=spotify:user:1216605385:playlist:0JI4Wpumdo57dQpUwJCRQq" width="300"></iframe>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b>Caitlin Garrett, Statistical Analyst:</b></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><b><br /></b></span>
<span style="font-family: Georgia, Times New Roman, serif;">"This playlist is a pretty balanced representation of the music I listen to when I'm knee-deep in analysis mode. Most of these songs are pretty upbeat, but there are a few mellow ones thrown in (mostly Poolside tracks). The single thing I need in a playlist is a steady beat, which you'll find throughout this list. Bands like Ratatat and Javelin get a lot of airtime on here because I like their genre of instrumental. I only took a handful of songs from each of them but their full albums make good standalone playlists as well."</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<iframe allowtransparency="true" frameborder="0" height="380" src="https://embed.spotify.com/?uri=spotify:user:1216605385:playlist:3yjVDoDoU5z2hZyvb6eQ0W" width="300"></iframe>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span><span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><b>Jon MacMillan, Data Analyst:</b></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><b><br /></b></span>
<span style="font-family: Georgia, Times New Roman, serif;">"This playlist is all over the place, but that's typically how I am when I really get down to work. The only prerequisiste for a song to make my playlist is that it maintains an upbeat tempo and catchy beat. This includes most notably Ratatat, Explosions in the Sky, and a little Daft Punk sprinkled in. As the title ['Forget the Words'] suggests, forget the words and just listen to the music. The first track, All My Friends by LCD Soundsystem, is one of my favorite songs. I can't tell you how many times I've listened to this song and yet still don't know the lyrics, yet I can't help but get excited when I hear that piano riff."</span><br />
<br /></div>
<iframe allowtransparency="true" frameborder="0" height="380" src="https://embed.spotify.com/?uri=spotify:user:jomacm04:playlist:1uuVQViQDV0qiAO9FtAy1A" width="300"></iframe>
<br />
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">If listening to music at work isn’t your thing, there’s
been some <a href="http://www.theatlantic.com/health/archive/2012/06/study-of-the-day-why-crowded-coffee-shops-fire-up-your-creativity/258742/">research</a>
which shows that ambient sounds can increase creativity. If working at a coffee
shop isn’t an option, <a href="http://coffitivity.com/">Coffitivity</a> has you
covered. Their website provides the same ambient noises that you’d hear at your
local coffee shop without the distractions. </span><o:p></o:p><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">We'd love to know: what's on your at-work playlist?</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett is a Statistical Analyst at <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a></span></div>
Unknownnoreply@blogger.com3tag:blogger.com,1999:blog-8191030418474848386.post-80117847578761835932013-07-11T13:42:00.000-04:002013-07-11T15:10:03.504-04:00#RIUC13<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">For those of you who weren’t able to attend the 2013 Rapid
Insight User Conference, we set a new record for most attendees and largest
number of customer presentations. With two full days of dual track programming,
the presenters covered a lot of ground. While we wait for some of the video
recordings of customer presentations to be formatted, I thought it would be
good to do a quick recap here. </span><o:p></o:p></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="http://4.bp.blogspot.com/-Vku2iZXCW6s/Ud7tgWXp2TI/AAAAAAAAAdc/SMJVTom1nAE/s1600/mikeeeeee.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="320" src="http://4.bp.blogspot.com/-Vku2iZXCW6s/Ud7tgWXp2TI/AAAAAAAAAdc/SMJVTom1nAE/s320/mikeeeeee.jpg" width="240" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Mike Laracy, Data Geek (at right)</td></tr>
</tbody></table>
<span style="font-family: Georgia, Times New Roman, serif;">The conference opened with a keynote from our Founder and
CEO, Mike Laracy, who talked a bit about the future of predictive analytics.
With a mass public education on the value of analytics (from people like Nate
Silver and Billy Bean, with a little help from Brad Pitt), as well as
significant advances in data storage and processing power, a stronger need for
predictive analytics is emerging. The market is shifting towards the view that
more data access is better than restricted access, and that given the right
tools along with access, smart people – data scientists – can turn raw data
into actionable information. Given these changes, the data scientist – that’s
you – will be in increasingly higher demand over the next decade and beyond, as
will predictive analytics. </span><o:p></o:p><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<span style="font-family: Georgia, Times New Roman, serif;">The user presentations covered lots of different topics, and
we’ve made all of their slide decks available <a href="http://www.rapidinsightinc.com/media/pdfs/UC2013_slides/2013_User_Conference_Session_Slides.pdf">here</a>;
I’d highly recommend checking them out. In addition to what’s there, I’d also
recommend checking out some of the interviews we’ve done with customers on <a href="http://rapidinsight.blogspot.com/2013/06/campaign-pyramids-brick-by-brick.html">building
campaign pyramids</a> and <a href="http://rapidinsight.blogspot.com/2013/05/using-predictive-modeling-to-drive.html">using
predictive modeling to drive fundraising efforts</a>. The RI staff team also gave
a few presentations, including topics
like <a href="http://www.rapidinsightinc.com/media/pdfs/UC2013_slides/RI_New_Tools_New_Tricks.pdf">Tips
and Tricks in Veera</a>, <a href="http://www.rapidinsightinc.com/media/pdfs/UC2013_slides/Predictive_Modeling.pdf">Techniques
for Improving Your Predictive Models</a>, and <a href="http://www.rapidinsightinc.com/media/pdfs/UC2013_slides/Intro_Reporting_Dashboarding_Veera.pdf">An
Introduction to Reporting and Dashboarding with Veera</a>.</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">Another thing worth mentioning is that we announced our
partnership with </span><a href="http://www.tableausoftware.com/" style="font-family: Georgia, 'Times New Roman', serif;">Tableau</a><span style="font-family: Georgia, 'Times New Roman', serif;"> to
provide a complete solution for both predictive modeling and visualization.
Now users can use Veera to clean up their data, Analytics to build their
predictive models, and Tableau’s visualizations to turbocharge their presentations.
For more information, check out our </span><a href="http://www.rapidinsightinc.com/tableau" style="font-family: Georgia, 'Times New Roman', serif;">partner page</a><span style="font-family: Georgia, 'Times New Roman', serif;">.</span><br />
<div class="MsoNormal">
<blockquote class="twitter-tweet">
I am now a scientist according to <a href="https://twitter.com/RapidInsightInc">@RapidInsightInc</a>. I feel super smart! <a href="https://twitter.com/search?q=%23RIUC13&src=hash">#RIUC13</a><br />
— Dustin Mayfield (@dlm0078) <a href="https://twitter.com/dlm0078/statuses/350239736683696128">June 27, 2013</a></blockquote>
</div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">My favorite part of the User Conference has always been
talking to customers about the cool data projects that they’ve been tackling,
and this year was no different. Kudos to our users for being so creative and smart with the ways they use our software. We also owe a big thanks to the folks at Yale for
hosting us, and to all who were able to attend. Here’s to the best User
Conference so far and to making next year’s even better!</span><o:p></o:p></div>
<script async="" charset="utf-8" src="//platform.twitter.com/widgets.js"></script>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-697527139731294282013-06-25T09:08:00.000-04:002013-07-26T11:21:14.599-04:00Data Scientists: The Next Generation<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">As I’m sure you all have noticed, the data business is
booming right now. (Are you tired of the term “big data” yet?) The fact that
90% of the data in world today has been created in the last two years is a
great example of the growth trajectory of data. All of this data provides new
opportunities for discovery for those who are willing to analyze it. Enter the
data scientist. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"> “Data Scientist” isn’t
even listed as a career by the US Government’s <a href="http://data.bls.gov/search/query/results?cx=013738036195919377644%3A6ih0hfrgl50&q=data+scientist"><span style="color: #cc0000;">Bureau
of Labor Statistics</span></a> yet, but it’s already been named the sexiest job of the
21<sup>st</sup> century by <a href="http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1"><span style="color: #cc0000;">Harvard
Business Review</span></a>. With a growth pattern similar to that of data itself, it’s
safe to say that data scientists are going to be in high demand. Among other
skills, being a practitioner of data science requires analytical thinking, mathematical/statistical
ability, a knack for communicating results to non-data people, and creativity. This
combination of business acumen and technical skill isn’t easy to come by, and
new graduate programs with an emphasis on data science seem to be cropping up
daily to fill the gaps. One <a href="http://www.nytimes.com/2013/04/14/education/edlife/universities-offer-courses-in-a-hot-new-field-data-science.html?pagewanted=all"><span style="color: #cc0000;">article</span></a>
from the New York Times recently asserted that the United States will need to
increase the number of graduates with data science skills by as much as 60% to
keep up with demand. So, when you’re
looking for new data scientists, where do you turn? To a generation who’s grown
up with data science all around them – through Netflix recommendations, Google
search results, and even at the movie theater à la <i>Moneyball</i>. <o:p></o:p></span></div>
<div class="MsoNormal">
<a href="http://4.bp.blogspot.com/-E7mM7w13jXs/UcmR5I5AgpI/AAAAAAAAAas/e9C5NO0fva0/s1600/JobHop1.JPG" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="240" src="http://4.bp.blogspot.com/-E7mM7w13jXs/UcmR5I5AgpI/AAAAAAAAAas/e9C5NO0fva0/s320/JobHop1.JPG" width="320" /></a><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">I was recently asked to participate in a “Job Hop Day” for a
local elementary school. The idea was to expose 4-6 graders to different jobs
that are available in the Mount Washington Valley in NH. It was a good
opportunity to spend a fund day with elementary school students while exposing
them to world of data science (and the idea that people actually get paid for
doing it!). In preparing for our session, I realized that as thrilling as an
hour-long lecture on data science might be for some, 10-year-olds probably
wouldn’t be so interested. After ruling out a product demo and a slideshow, my
coworkers and I thought about other ways to engage them. We decided the best
approach for them to learn about being a data scientist was to do it themselves
(in the guise of a game). <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">When creating the game, we thought about some of the skills
we wanted to reinforce, which were things like data mining, basic math, and the
ability to make predictions. From there, we got creative – we wanted to pick a
subject that kids would be interested in, and since vampires are on the brink
of cliché, we settled on werewolves. The game we came up with was a variation
of a Family Feud board that involved an initial data-mining phase to glean the
characteristics of a werewolf. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">To start, I gave the kids ten descriptions of people on color-coded
index cards, five of which were designated as “werewolves” and five of which
were “non-werewolves”. (Coming up with the descriptions was a good exercise for
us as well, we tried to make sure the
clues weren’t too obvious, and had to plan them so that some characteristics
were more popular than others. An example: three of the werewolves were
vacationing in <a href="http://www.youtube.com/watch?v=iDpYBT0XyvA"><span style="color: #cc0000;">London</span></a>
this summer, but all five of them played some kind of sport). Each data
scientist had a whiteboard to write down their descriptions as they went, and
we stopped the “data mining” portion of the game once they all felt like they
had come up with as many characteristics as they could. The Family Feud board I
mentioned earlier had the ten characteristics listed in order of the number of
times they came up, and the kids took turns guessing what was on the board. <o:p></o:p></span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Over the course of the day, three groups of students played
the game, and all three groups seemed to really enjoy it. After we finished the
game, we talked about the different uses of data and predictive modeling,
covering examples spanning test scores to baseball. They were knee-deep in
baseball season and pretty excited when I told them about a baseball scout’s
presentation I saw at DRIVE, and how they used statistics to predict what might
happen in each game. It was evident from our conversations that the kids had
some knowledge of the amount of data around them and were interested in
examining the world from a data-driven viewpoint. (I should probably mention
here that the kids who chose to attend our session knew it would be
math-related, so our sample was a bit biased.) Most of them had never heard of
a data scientist or a statistical analyst before, but they were interested in the
type of thinking we’d done. A few days later, a student’s mom told me that her
son “loved the game” and “was so excited that it was an actual job that he
could shoot for”.</span></div>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Overall, our ad hoc approach to the data scientist
experience seemed to go over well, but there’s always room for improvement. I’m
interested in any ideas or experiences you guys have might regarding young data
scientists, and would love to hear about them in the comments below. In the
meantime, if you’ve had a sneaking suspicion about a certain neighbor around a
full moon, or just want to have a little fun, I’d recommend trying out your own
version of the game. </span><br />
<br />
<br />
<br />
<o:p></o:p></div>
<embed flashvars="host=picasaweb.google.com&hl=en_US&feat=flashalbum&RGB=0x000000&feed=https%3A%2F%2Fpicasaweb.google.com%2Fdata%2Ffeed%2Fapi%2Fuser%2F109260401831792430020%2Falbumid%2F5893404102095472833%3Falt%3Drss%26kind%3Dphoto%26hl%3Den_US" height="267" pluginspage="http://www.macromedia.com/go/getflashplayer" src="https://static.googleusercontent.com/external_content/picasaweb.googleusercontent.com/slideshow.swf" type="application/x-shockwave-flash" width="400"></embed><br />
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett is a Statistical Analyst at <a href="http://www.rapidinsightinc.com/home" target="_blank">Rapid Insight</a></span>Unknownnoreply@blogger.com7tag:blogger.com,1999:blog-8191030418474848386.post-14268020162417398162013-06-11T09:00:00.000-04:002013-06-13T16:08:08.082-04:00Campaign Pyramids: Brick by Brick<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Recently, I got to chat with Chelsea Drake and James Dye, who are both Data Analysts </span><span style="font-family: Georgia, 'Times New Roman', serif;">at the College of William & Mary</span><span style="font-family: Georgia, 'Times New Roman', serif;">, about the work they've been doing on campaign pyramids. For a more in-depth look at the functions that their campaign pyramids serve, and their process for building them, be sure to check out their presentation at our user conference or stay tuned for a webinar rebroadcast in July</span><span style="font-family: Georgia, 'Times New Roman', serif;">. </span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">What is a campaign
pyramid’s function in your office?<o:p></o:p></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<a href="http://4.bp.blogspot.com/-buEk_i-Rq5I/UbcrdHWEyJI/AAAAAAAAAaA/-89nW4rwWXw/s1600/rapid+insight+pic.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://4.bp.blogspot.com/-buEk_i-Rq5I/UbcrdHWEyJI/AAAAAAAAAaA/-89nW4rwWXw/s200/rapid+insight+pic.jpg" width="198" /></a><span style="font-family: Georgia, Times New Roman, serif;">CD: Right now we’re using the pyramids as a donor-centric
list of prospects. To give some background on the pyramids, we did a massive
data mining project to determine where our donors’ interests were. The end
result is a dynamic pyramid that updates as new gifts come in and as we get new
information about where their philanthropic interest lie. We use them as
accurate prospect lists. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: We had a bunch of people in our prospect pool and needed
to know where their interests were. For example, if they’re into Athletics but
graduated from the Business school, do we want to go after a split gift, or do
we say that their primary interest is athletics, so they should be doing the
ask? The pyramids help us decide which one we should try to raise money for.
They also help to set goals for each department and each school. So we’ll set a
goal and ask a question like ‘how many gifts do we need at different levels,
and prospects do we need to make up that pool and reach our goal?’<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">How do you set the
goals for each pyramid?<o:p></o:p></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<a href="http://1.bp.blogspot.com/-lQQ-OYqQ4so/UbcrdD650BI/AAAAAAAAAaE/y_X43QRJ5Yk/s1600/Dye,+James.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://1.bp.blogspot.com/-lQQ-OYqQ4so/UbcrdD650BI/AAAAAAAAAaE/y_X43QRJ5Yk/s200/Dye,+James.jpg" width="180" /></a><span style="font-family: Georgia, Times New Roman, serif;">CD: We’re able create pyramids to test high, medium, and low
goals to see which one is most feasible for each unit and each campaign
overall. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: Each unit has three pyramids – they have a high goal,
say 120M if 100M is the medium or mid-range goal, and a low goal, which might be
something like 80M. The mid-range goal should be something they can accomplish
without too much effort and the low goal is what we think they’d get if they
only asked people we already knew. This allows us to see how much stretch we
need to do and how many people we need to identify in order to hit certain
monetary goals. The idea behind the project was to figure out where our
prospect pool’s interests were and where we need to do work and identify new
prospects to fill in gaps and holes. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">What triggered your
interest in campaign pyramids?<o:p></o:p></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">CD: We started last summer, our Assistant VP of Operations wanted
to make sure we were being as donor-centric as possible. She knew we had some
information on interests but that we didn’t have a reporting tool that
identified which prospects should go with each interest. She knew I had an
analytical background and that’s how she chose to bring the project to me. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: Previous pyramids had been done at the university level.
For the college, we wanted to know who we had out there and how much money that
would bring in with specific gift ratings. But we were also asking things like
‘How much can we get for athletics?’ and ‘Who are the people who are interested
in athletics?’. That’s where it spawned into a donor-centric thing. We wanted
to know what our donors’ interests were, what they’ve given to in the past, and
on a program and unit based levels, who were the donors for each area. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">Who builds the
pyramids in your office? How did you decide that?<o:p></o:p></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">CD: James and I do, and that was decided based on our
backgrounds. James has a programming and computer science background and I have
a background in research and analytics. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: We’re the programming and analytic people in our office
and were already working on data pools, but were brought onto this project
based on our skillset. Within our department, we’re the ones who generally work
with the data. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">What’s your
administration’s take on the pyramids?<o:p></o:p></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: They like them a lot. It gives them an idea of monetary
goals for each unit and school to stretch for and concrete lists of names. We
can show them the people we’ve identified, and if we sum up all of things we
have in a pyramid, we can see if the goal set for a department is realistic. It
helps them to see who’s out there and who’s in our database. They also use it
to present to a board of visitors in a slideshow on where we stand in a
campaign and how our numbers are at any given point. They can tell how many
people we’ve already identified and how many new people we need to identify to
meet a goal.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;">What advice would you
have for someone looking to undertake a project like this?<o:p></o:p></span></b></div>
<div class="MsoNormal">
<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">CD: One of the things that was really helpful for us as the
project started was having a good relationship with IT to fine tune what the
data files we get from them would look like. The key to doing this type of
analysis effectively is to have the best data that you’re able to get from your
system in the most consistent way possible. Also, you should absolutely plan
out what your goals are for the project before you get started. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: You have to know which data points out there you can
pull from and what would be relevant for your goal. Depending on the size of
the school, you might want to focus on a single unit pyramid to narrow down the
scope of what you want to do. You could start with a major gifts or annual fund
pyramid, for example. It’s about first defining your question, then looking at
the data to figure out which people to target and looking at the numbers to
establish what your monetary g</span><span style="font-family: Georgia, Times New Roman, serif;">oals should be. </span><span style="font-family: Georgia, 'Times New Roman', serif;">It helps to nail out a template of what you want the end
result to look like before you start programming.</span><span style="font-family: Georgia, 'Times New Roman', serif;"> </span><span style="font-family: Georgia, 'Times New Roman', serif;">We knew what we wanted our end result to be,
so then when we were programming forward, the question became ‘how do I fill out
these blanks where these numbers should be?’. This way, when you start
building, you’re able to visualize how to compile everything correctly
according to your template. Also make sure that you have a good team working on
the project, and that team members know what their role in the project is.</span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">CD: Anytime you’re taking on a project like this, you want
to have the ability to talk to the managers or executives of your department to
make sure that your end result matches what they feel they need. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">JD: Make sure it’s helpful for them. We’re numbers people.
We can make a page full of numbers and look at it and understand it, but
management might need something a little bit more nice looking. So the sheet we
create for them outputs to a single page with colors so that when we turn it
over to them, the information is logical and easy to read. It comes down to
knowing your audience. </span><o:p></o:p></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-8191030418474848386.post-16157702374534172742013-06-04T08:42:00.000-04:002013-06-04T08:42:42.799-04:00Bookworming<div class="separator" style="clear: both; text-align: left;">
<span style="font-family: Georgia, Times New Roman, serif;">Looking for a book to read this summer? We're proud to present our second annual list of Rapid Insight staff-recommended books for your perusal:</span></div>
<div class="separator" style="clear: both; text-align: center;">
<br /></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://1.bp.blogspot.com/-pWUROhGoCVU/UaTG_mqaKEI/AAAAAAAAAZI/ZHle3FcUrAs/s1600/walden.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="http://1.bp.blogspot.com/-pWUROhGoCVU/UaTG_mqaKEI/AAAAAAAAAZI/ZHle3FcUrAs/s200/walden.jpg" width="123" /></a></div>
<i style="font-family: Georgia, 'Times New Roman', serif;"><a href="http://www.amazon.com/gp/product/0451532163/ref=s9_psimh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-2&pf_rd_r=16DGWZV2C9P8KVNCFF9B&pf_rd_t=101&pf_rd_p=1389517282&pf_rd_i=507846" target="_blank">Walden</a></i><span style="font-family: Georgia, 'Times New Roman', serif;"> by Henry David Thoreau (Mike Laracy, CEO)</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">When I first read this I started highlighting all of the
sentences and paragraphs that were brilliant. When I realized that I was
highlighting mostly everything I put away my highlighter. <o:p></o:p></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/--_SKDssRfBI/UaTGstpnH7I/AAAAAAAAAYc/nXDuAk6U6do/s1600/booklist2.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="200" src="http://2.bp.blogspot.com/--_SKDssRfBI/UaTGstpnH7I/AAAAAAAAAYc/nXDuAk6U6do/s200/booklist2.jpg" width="131" /></span></a></div>
<span style="font-family: Georgia, Times New Roman, serif;"><a href="http://www.amazon.com/Naked-Statistics-Stripping-Dread-Data/dp/0393071952/ref=sr_sp-atf_title_1_1?s=books&ie=UTF8&qid=1369753641&sr=1-1&keywords=naked+statistics" target="_blank"><i>Naked Statistics</i></a> by Charles Wheelan (Jon MacMillan, Data
Analyst)</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">This book focuses on statistical analysis including
inference, correlation, and regression analysis. As boring as that sounds to
some, Charles Wheelan does an amazing job of keeping the book engaging and
interesting. </span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-5AxJ1T51x74/UaTGs1RhPTI/AAAAAAAAAYo/ir39Cwcq6t4/s1600/booklist4.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="200" src="http://2.bp.blogspot.com/-5AxJ1T51x74/UaTGs1RhPTI/AAAAAAAAAYo/ir39Cwcq6t4/s200/booklist4.jpg" width="130" /></span></a></div>
<span style="font-family: Georgia, Times New Roman, serif;"><i><a href="http://www.amazon.com/Inferno-Dan-Brown/dp/0385537859/ref=sr_sp-atf_title_1_1?s=books&ie=UTF8&qid=1369753737&sr=1-1&keywords=inferno" target="_blank">Inferno</a></i> by Dan Brown (John Paiva, Account Management Team)</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">For any lovers of Florence, Italy, this is a definite
must-read. While not reaching the level of some of his previous groups, Inferno
is definitely a page turner. It’s not a scientific read but there are some
great questions posted by the characters that force you to consider the basic
math behind humanities’ survival or demise. <o:p></o:p></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://4.bp.blogspot.com/-SFxlnJDufHM/UaTGtM1l7kI/AAAAAAAAAYw/MaEt_MOSDHE/s1600/booklist5.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="200" src="http://4.bp.blogspot.com/-SFxlnJDufHM/UaTGtM1l7kI/AAAAAAAAAYw/MaEt_MOSDHE/s200/booklist5.jpg" width="129" /></span></a></div>
<span style="font-family: Georgia, Times New Roman, serif;"><i><a href="http://www.amazon.com/Train-Your-Mind-Change-Brain/dp/0345479890/ref=sr_sp-atf_title_1_1?s=books&ie=UTF8&qid=1369753757&sr=1-1&keywords=train+your+mind+change+your+brain" target="_blank">Train Your Mind, Change Your Brain</a></i> by Sharon Begley (Jeff
Fleischer, Director of Client Operations)</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<br />
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Though it sounds like a self-help book, it’s actually a
non-fiction work for the layman describing recent discoveries in the field of
neuroscience. The author spoke at a Behavioral Healthcare conference I attended
a few years ago. I liked the talk so much, I bought the book! Fascinating, yet
easy read. <o:p></o:p></span></div>
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</div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://3.bp.blogspot.com/-JfdilwosRVU/UaTGssvM2rI/AAAAAAAAAYY/cX7XDCGszes/s1600/booklist1.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="200" src="http://3.bp.blogspot.com/-JfdilwosRVU/UaTGssvM2rI/AAAAAAAAAYY/cX7XDCGszes/s200/booklist1.jpg" width="144" /></span></a></div>
<a href="http://3.bp.blogspot.com/-PAcM6rFq0To/UaTGtJXO_BI/AAAAAAAAAYs/nbJ2o_EPv24/s1600/booklist6.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="200" src="http://3.bp.blogspot.com/-PAcM6rFq0To/UaTGtJXO_BI/AAAAAAAAAYs/nbJ2o_EPv24/s200/booklist6.jpg" width="131" /></span></a><br />
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<span style="font-family: Georgia, Times New Roman, serif;"><i><a href="http://www.amazon.com/gp/product/1612430295/ref=s9_psimh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-2&pf_rd_r=1YDE6XFRTYSXF69GBZSN&pf_rd_t=101&pf_rd_p=1389517282&pf_rd_i=507846">Poisson
Un Poisson Deux Poisson Rouge Poisson Blue</a></i> by Theodor Geisel (Scott
Steesy, Chief Software Architect)<o:p></o:p></span></div>
<br />
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<span style="font-family: Georgia, Times New Roman, serif;">For some light mathematics.<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
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<br />
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<span style="font-family: Georgia, Times New Roman, serif;"><i><a href="http://www.amazon.com/Sell-Human-Surprising-Moving-Others/dp/1594487154/ref=sr_sp-atf_title_1_1?s=books&ie=UTF8&qid=1369753782&sr=1-1&keywords=to+sell+is+human" target="_blank">To Sell is Human</a></i> by Daniel H. Pink (Sheryl Kovalik, Director
of Sales & Business Development – Higher Ed)<o:p></o:p></span></div>
<br />
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<span style="font-family: Georgia, Times New Roman, serif;">Being a big fan of sales, I was naturally excited to read a
book that gave me excellent perspective on how access to information has
changed the buyer/seller relationship; I also enjoyed Pink’s march through the
history of sales and his advice on how to adapt for the new environment. But
the best part of the book was the way in which the author helps one to see that
all of us are sales folks in some way. If you’re in fundraising, admissions,
technology, or service positions, there is something here for you – we’re all
trying to sway others to our side!</span><o:p></o:p><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<br />
<div class="separator" style="clear: both; text-align: left;">
<a href="http://4.bp.blogspot.com/-MtHE-ivpKIw/UaX786X6KrI/AAAAAAAAAZY/wZPWWMI3UT0/s1600/signalnoise.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="200" src="http://4.bp.blogspot.com/-MtHE-ivpKIw/UaX786X6KrI/AAAAAAAAAZY/wZPWWMI3UT0/s200/signalnoise.jpg" width="131" /></a></div>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><i><a href="http://www.amazon.com/dp/159420411X" target="_blank">The Signal and the Noise</a></i> by Nate Silver (Caitlin Garrett, Statistical Analyst)</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">Named Amazon's #1 Best NonFiction Book for 2012, I'd say this book is proof positive that predictive analytics is going more mainstream. As a thought leader in the field, Silver's book is chalk full of examples of the practical applications of predictive modeling. A great read for the technical and non-technical alike.</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">Have you read anything good lately? We're always looking for recommendations - tell us about it in the comments. </span></div>
Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-8191030418474848386.post-20986452648506616702013-05-21T10:44:00.000-04:002013-05-30T16:36:28.005-04:00Using Predictive Modeling to Drive Fundraising Efforts<br />
<span style="font-family: Georgia, 'Times New Roman', serif;">In preparation for their presentation at our upcoming <a href="http://www.rapidinsightinc.com/conference.php" target="_blank">User Conference</a>, "Using Predictive Modeling to Focus your Fundraising Efforts", I got the chance to chat with Bridget Mendoza and Brianna Lowndes from the <a href="http://whitney.org/" target="_blank">Whitney Museum of American Art</a>. Bridget, the Director of Development Records, and Bri, Director of Membership and Annual Fund, have been working together for the past year and a half to bring predictive modeling in-house for the Whitney Museum. </span><br />
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Here are their thoughts on building their skillsets, modeling challenges, and how the process is going so far:</span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; margin-left: 1em; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="http://1.bp.blogspot.com/-k0ng2wosq7g/UZuHa_nyVwI/AAAAAAAAAYA/BsVQPO01vio/s1600/BMendoza.jpg" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="http://1.bp.blogspot.com/-k0ng2wosq7g/UZuHa_nyVwI/AAAAAAAAAYA/BsVQPO01vio/s1600/BMendoza.jpg" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-family: Georgia, Times New Roman, serif;">Bridget Mendoza</span></td></tr>
</tbody></table>
<b><span style="font-family: Georgia, Times New Roman, serif;">What triggered your
interest in predictive modeling for the Whitney Museum?<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">BM – We started by thinking about how to enhance our prospecting
as we lead up to our new building. Our research team routinely identifies
‘hidden’ people with higher capacities giving at an entry levels. Anecdotally
we compared these prospects to active upper level donors and started seeing
patterns in some of their giving and membership histories. We’d previously completed
a modeling exercise with an outside company, but the problem with outsourcing
was that once we got the model we didn’t have ownership and couldn’t adjust it.
We know our data better than anyone else, and when we looked at some of the
underlying information, we wanted the ability to alter and refine the model. As
our goals are ambitious, we needed to continually grow our prospect base and
predictive modeling helps us create a solid foundation for doing so.<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How did you decide
internally who would take on the predictive modeling project?<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: right; text-align: right;"><tbody>
<tr><td style="text-align: center;"><a href="http://3.bp.blogspot.com/-UlL1M1RVXmY/UZuHbMASxaI/AAAAAAAAAYE/3wsBZngcme0/s1600/BLowndes.JPG" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" height="200" src="http://3.bp.blogspot.com/-UlL1M1RVXmY/UZuHbMASxaI/AAAAAAAAAYE/3wsBZngcme0/s200/BLowndes.JPG" width="150" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><span style="font-family: Georgia, Times New Roman, serif;">Brianna Lowndes</span></td></tr>
</tbody></table>
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<span style="font-family: Georgia, Times New Roman, serif;">BL - We formed a committee of about ten who were involved in
conversations on what we hoped to get out of a predictive modeling software or
service and what our goals would be. As conversations progressed and we decided
on the Rapid Insights tools it made sense from a resource perspective to deploy
Bridget and I, who already work closely with the data and provide different
perspectives. Being close to the exports
and metrics and being aware of nuances in member lifecycles has played really
nicely into the work we’re doing in predictive modeling. The larger group meets
quarterly and that cross-departmental approach helps keep us on track and
engaged with the bigger picture. <b><o:p></o:p></b></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How did you build
your predictive modeling skillset?<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">BM – We started by attending conferences like MARC and the
Rapid Insight modeling course at Brown. Once we made the decision to work with
Rapid Insight we had the opportunity to work closely with their team and to
become more familiar with their software and with basic modeling
practices. Bri and I also took a Business
Statistics for Management class as a refresher. <o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">What modeling
challenges have you found that are unique to a museum?</span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">BM –Museums are not as far along in leveraging predictive
modeling as our Higher Education counterparts.
While attending the RI User Conference, we heard a really interesting
presentation about student retention which sparked our thinking on how to apply
what they’ve done to the museum setting. Like
many museum membership programs, our acquisitions in a given year are connected
to the exhibition schedule. These cyclical patterns make it more complicated to
isolate the data around the health of the program. We are excited to leverage
predictive modeling tools to better understand those trends. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Do you have any
advice for non-profits who are thinking about predictive modeling in-house?<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">BM – There’s a learning curve, but don’t let that discourage
you. That’s what a lot of our webinar will be about. Even though we haven’t
been modeling for five or ten years, there’s a lot that that can be
accomplished in that first year especially with the help from a partner like
Rapid Insight.<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">BL – It’s important to have senior leadership support and take
a cross-departmental approach. This ensures we are always thinking about the
larger institutional needs. I’d also say that taking the stats class was helpful
for us. The software does a lot of the heavy lifting for you so it’s important
to get up to speed so that you feel like you’re engaging critically and asking
good questions. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">BM – Having a vendor who had built this type of model before
and had reliable expertise in the both the non-profit and for-profit fields has
been really helpful for us. It was good to be able to collaborate with our
software’s support team to build up our own knowledge of data prep and
predictive modeling. Rapid Insight has been a real partner through this first
year of modeling and we are excited to continue and expand this great
work. </span><o:p></o:p></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">**</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">If you're interested in hearing more about how to use predictive modeling to focus your fundraising efforts, Bridget and Bri are presenting at our upcoming User Conference. For more information, or to register, <a href="http://www.rapidinsightinc.com/conference.php" target="_blank">click here</a>. Both users and non-users are welcome to attend.</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">If you have a tip you'd like to share on using predictive models to drive your fundraising efforts, please leave it as a comment below :)</span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-61695131159352053512013-05-14T09:43:00.000-04:002013-05-15T11:15:42.468-04:00Tips for Charting in Veera<span style="font-family: Georgia, 'Times New Roman', serif;">Being able to collect and identify valuable data is
important for making sound decisions to meet long-term goals, but collection
and identification are only part of the process. Just as important is the
ability to provide visual representations to transform your raw data into
actionable information. This guide will help to create basic graphs in Veera
and to improve them by making them more clear and eye-catching.</span><br />
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">First and foremost, before you get started, you’ll need to
identify what data you’d like to represent in your chart. You may need to whittle down your data so that it
isn’t overwhelming but still fairly represents your population. For this
purpose, you might consider using a Filter node to filter down to just the
entries you’re truly interested in, or using a Cleanse node to create an
“other” category to concatenate some of the smaller categories. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Once you have the dataset you’d like to use and have opened
the chart node, your first step will be to select the chart type (pie chart,
bar chart, etc.). Once you’ve done that, you’ll want to fill in all necessary
fields on the right side of the Chart Data window, such as deciding which
variables should be your x and y axes. Once everything has been labeled, you
can edit the look of your chart by clicking on the colored icon in the top
right of the window. <o:p></o:p></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://1.bp.blogspot.com/-8jqWuElEl6c/UZI8hnGasdI/AAAAAAAAAXk/UcieOABcO-Y/s1600/charting2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="254" src="http://1.bp.blogspot.com/-8jqWuElEl6c/UZI8hnGasdI/AAAAAAAAAXk/UcieOABcO-Y/s320/charting2.png" width="320" /></span></a></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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</div>
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<span style="font-family: Georgia, Times New Roman, serif;">As you begin to navigate through the Chart Style Editor
window, here are some things to keep in mind:<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">1. The chart type does not automatically identify the chart
type that was selected in the previous window – you’ll want to update this and
select the appropriate chart type. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">2. The sample chart you see on the right will update to reflect
any changes you make in the chart editor. Use it to evaluate your aesthetic
decisions as you go. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">3. Below the sample chart you can choose whether or not to
include data labels and adjust the font, size, style, color, and chart
background. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">4. When charting binary or categorical variables, you may not
see a need to include a legend. If you select the ‘Legend’ tab and uncheck the
‘Display Legend’ box, you can remove the legend. If you do remove the legend,
be sure the chart is labeled accurately in the first window. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">5. If you’d like to make any of your charts 3D, you can do so
by going to the ‘3D’ tab and checking the ‘Display in 3D’ box. There you can
also change the inclination, depth, rotation, etc. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">6. Once you’ve edited the background color and text color of
your chart in (below the sample chart), you can choose a color palette for the
rest of the chart by going to the ‘Color Palette’ tab. There you can select a
pre-programmed palette or create your own custom palette. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">7. One of the more subtle functions in the chart node is the
ability to shade ranges within a chart – found in the ‘Axis’ tab. Here you can
choose to assign colors to ranges within a chart to better visualize certain
areas of interest in your data. This is especially nice when looking at
retention rates or variables represented as percentages because the ranges are
easy to define & interpret.<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">8. Another use of the ‘Axis’ tab is to help label your data.
Too many times I’ve gone to chart my data but found that half my data points
are unlabeled in my graph. Here, choose the axis that should contain those
missing labels and unselect the ‘Auto Label’ box. Now under the ‘Major Tick
Marks’ heading, unselect the ‘Auto Interval’ box and set the interval to be 1
to insert all missing data point labels. It should look something like this (note
that I’ve chosen the y-axis):<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="separator" style="clear: both; text-align: center;">
<a href="http://1.bp.blogspot.com/-karcFBVhpvs/UZI-qCTEWaI/AAAAAAAAAXw/JiX3CGqj7sg/s1600/chart3.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="400" src="http://1.bp.blogspot.com/-karcFBVhpvs/UZI-qCTEWaI/AAAAAAAAAXw/JiX3CGqj7sg/s400/chart3.png" width="316" /></a></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<br />
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<br /></div>
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</div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">9. Now that you know how to edit each chart individually, you
might find that you like a particular style and want to save it to use later.
You can do this within the Chart Node by clicking on the ‘…’ button next to the
‘Chart Style’ drop-down. Here, click the green plus button to add a new chart
style – give it a name and you’ll find yourself in the ‘Chart Style Editor’
window. Once you’ve finished editing, you’ll be able to access your saved chart
style within the Chart Node from the Chart Style drop-down menu. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">10. Once you’ve created a chart type that you love, you can also
export it to the Collaborative Cloud so that others can use it – if you have a
minute, check out the cloud and search for chart styles for the opportunity to
download styles that other users have created.</span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">Do you have any tips for charting, or questions about how to
use the charting node? Leave them in the comments below :)<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, 'Times New Roman', serif;">Happy charting!</span></div>
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<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, 'Times New Roman', serif;">-by Jon MacMillan, Data Analyst at Rapid Insight</span></div>
<br />
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<br /></div>
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-56130314424524594732013-05-07T08:00:00.000-04:002013-05-07T10:25:35.573-04:00Set it and Forget it: The Case for Automated Reporting<span style="font-family: Georgia, 'Times New Roman', serif;">During my time at Rapid Insight, I’ve found that regardless
of the business, school, or non-profit we’re working with, reporting is a
necessity. Anyone who has built a report knows that pulling data from an
original source, cleaning it up, and transforming it into actionable
information can be a clunky and time-consuming process. One of the best ways
we’ve come up with to lighten the reporting load is to automate reports using
<a href="http://www.rapidinsightinc.com/veera" target="_blank">Veera</a>. Here’s our take on automated reporting, by the numbers; click on the
links for full case studies:</span><br />
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<o:p></o:p></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<a href="http://1.bp.blogspot.com/-qiwiZZFGrBg/UX7SdtRaFCI/AAAAAAAAAWo/EQk5crdSwf8/s1600/345.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://1.bp.blogspot.com/-qiwiZZFGrBg/UX7SdtRaFCI/AAAAAAAAAWo/EQk5crdSwf8/s1600/345.png" /></a><span style="font-family: Georgia, Times New Roman, serif;"></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, 'Times New Roman', serif;">The number of days (including interruptions!) it took Gloria Stewart, Director of Institutional Research at Schreiner University, <a href="http://www.rapidinsightinc.com/media/pdfs/schreiner_case_study.pdf" target="_blank">to build an automated report</a>. </span></div>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">The <a href="http://www.rapidinsightinc.com/media/pdfs/tulsa_county_case_study.pdf" target="_blank">number of departments within Tulsa County Juvenile Bureau</a> that depend on reports that Shonn Harrold, Assistant Director, has automated. These reports include intake reports, detention center reports, case assignments, and referral reports. </span><br />
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<a href="http://4.bp.blogspot.com/-6eOqRjXHOMA/UX7Sdv7Dr-I/AAAAAAAAAWs/f7sowYztf8A/s1600/88568.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://4.bp.blogspot.com/-6eOqRjXHOMA/UX7Sdv7Dr-I/AAAAAAAAAWs/f7sowYztf8A/s1600/88568.png" /></a></div>
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<span style="font-family: Georgia, 'Times New Roman', serif;"><br />The number of hours that Scott Alessandro, Assoc. Director of Educational Services at MIT Sloan School of Management, saves each week by <a href="http://www.rapidinsightinc.com/media/pdfs/mit_sloan_case_study.pdf" target="_blank">editing semi-automated ad hoc reports</a> rather than creating new reports. </span></div>
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<span style="font-family: Georgia, 'Times New Roman', serif;">The percentage of Excel spreadsheets that have errors, according to <a href="http://panko.shidler.hawaii.edu/ssr/Mypapers/whatknow.htm" target="_blank">a 2008 study</a>. Creating automated reports is a great way to catch spreadsheet errors as you're building a report and avoid them in the future by reducing the likelihood of human error. </span></div>
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<span style="font-family: Georgia, 'Times New Roman', serif;"><span style="font-size: x-large;"><br /></span>The <a href="http://www.rapidinsightinc.com/media/pdfs/dallas_baptist_case_study.pdf" target="_blank">percent improvement</a> that Dallas Baptist University saw in terms of time spent to prepare a report by using Veera: "It took over 3.5 hours to prepare a report using our old system. It took 30 minutes in Veera."</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">What could you do with an extra five hours per week?</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett, Statistical Analyst at <a href="http://www.rapidinsightinc.com/" target="_blank">Rapid Insight</a></span></div>
Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-8191030418474848386.post-2158270686130349672013-04-30T08:30:00.000-04:002013-04-30T08:30:06.290-04:00Using Social Media Data<span style="font-family: Georgia, 'Times New Roman', serif;">Every minute, millions of pieces of social media data are
generated around the world. In any given minute*:</span><br />
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<a href="http://2.bp.blogspot.com/-aVGRFS1s3rk/UX7Pt-qTZ2I/AAAAAAAAAWY/K4Jh_nMBSD8/s1600/like-follow-social-networking.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://2.bp.blogspot.com/-aVGRFS1s3rk/UX7Pt-qTZ2I/AAAAAAAAAWY/K4Jh_nMBSD8/s200/like-follow-social-networking.jpg" width="194" /></a></div>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Instagram users share 3,600 photos<br />
Brands and organizations on Facebook receive 34,722 “likes”<br />
Twitter users send over 100,000 tweets<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">With millions of people sharing more information each day,
we are witnessing a shift in how information is being produced online as it
becomes more and more user-generated. The web is moving away from being a static
library and becoming a more open, more connected place to share content. There
are plenty of opportunities to mine new variables from the constantly
increasing expanse of social media data. Here are a few:<o:p></o:p></span></div>
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<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">For a <span style="line-height: 115%;">college
or non-profit organization’s advancement department, tapping into these
variables can provide insight into how connected an individual is with the
institution. If a constituent is following you (whether on Facebook, LinkedIn,
Twitter, or another forum), they are actively choosing to be connected to you,
which conceivably may make them more likely to donate.</span></span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;"><span style="line-height: 115%;">In a </span><span style="line-height: 115%;">college
enrollment office, tracking which high school students have liked their
Facebook page can give them an idea of students who are very interested in
their institution. This information could be used to qualify prospects or to
help shape the pool of students being marketed to.</span></span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;"><span style="line-height: 115%;"> Brands </span><span style="line-height: 115%;">looking
to decide who to market to can analyze both the source and content of social
media data to determine who might be most likely to buy a product or respond to
a campaign. </span></span></li>
</ul>
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<span style="font-family: Georgia, Times New Roman, serif;"> <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Tracking and leveraging these data points has the potential to
add value to the predictive models you’re already building. <o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">So, are you already leveraging your social media data into your
models, and if not, why not?<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /><span style="font-size: xx-small;">
*Source: <a href="http://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/">http://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/</a></span></span><o:p></o:p><br />
<span style="font-family: Georgia, Times New Roman, serif; font-size: xx-small;">*Photo: <a href="http://timothybrand.com/wp-content/uploads/2012/04/like-follow-social-networking.jpg">http://timothybrand.com/wp-content/uploads/2012/04/like-follow-social-networking.jpg</a></span></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-41617758815085277732013-04-23T09:00:00.000-04:002013-04-23T09:33:04.103-04:00Five Steps for Data-Driven Strategic Enrollment Management<br />
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<b><span style="font-family: Georgia, Times New Roman, serif;">Establish your goals<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">This first step is crucial for mapping out a course to your
end goal. Try to envision where you’d like to end up and formulate a specific goal
to help get you there. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Possible goals include:<o:p></o:p></span></div>
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</div>
<ul><a href="http://2.bp.blogspot.com/-hiTC0sCoVYM/UXWadaoDYHI/AAAAAAAAAU0/hoAgptUHFIU/s1600/decision.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://2.bp.blogspot.com/-hiTC0sCoVYM/UXWadaoDYHI/AAAAAAAAAU0/hoAgptUHFIU/s200/decision.jpg" width="200" /></a>
<li><span style="font-family: Georgia, Times New Roman, serif;">Reduce your prospect mailing budget</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Increase accuracy of enrollment yield predictions</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Meet diversity objectives</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Increase your retention rate</span></li>
</ul>
<div>
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Get to know your data<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">The first step to getting to know your data is gaining
access to your data, which is trickier for some people than others. If you have
to go through IT to access your data, it helps to have a clear goal in mind and
a good idea of what fields or tables you’ll need. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Once you have your data, you’ll need some time to get
well-acquainted. A good starting point is to make sure you understand what each
field represents and how things are coded. If you have questions about how data
is being recorded or stored, this is the time to ask. Once you have a handle on
what your data represents, you’ll want to thoroughly review it. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">A few suggestions:<o:p></o:p></span></div>
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</div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Spot-<span style="text-indent: -0.25in;">check the accuracy of your data. Double-checking
things like the mean, min, and max for each variable is a quick way to verify
accuracy. </span><span style="text-indent: -0.25in;"> </span><span style="text-indent: -0.25in;">If you spot any data quality
issues, do your best to resolve them sooner than later.</span></span><div class="MsoListParagraph" style="mso-list: l0 level1 lfo1; text-indent: -.25in;">
<span style="font-family: Georgia, Times New Roman, serif;"><o:p></o:p></span></div>
</li>
<li><span style="font-family: Georgia, Times New Roman, serif;"><span style="text-indent: -0.25in;">Check for </span><span style="line-height: 115%;">missing
values. If you have a variable with a high number of missings, you’ll need to
decide whether or not to use that variable and if there’s a way to fill in what’s
not there.</span></span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;"><span style="line-height: 115%;">Brainstorm ideas</span><span style="line-height: 115%;"> for new variables. If you can’t <a href="http://rapidinsight.blogspot.com/search/label/Creating%20Variables%20Series" target="_blank">create new variables</a> from what you have
on-hand, spend some time thinking about things that might be worth tracking
going forward. </span></span></li>
</ul>
<div>
<span style="font-family: Georgia, Times New Roman, serif;"><span style="line-height: 18px;"><br /></span></span></div>
<div>
<span style="line-height: 17px;"><b><span style="font-family: Georgia, Times New Roman, serif;">Analyze your data</span></b></span></div>
<div>
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<span style="font-family: Georgia, Times New Roman, serif;">I realize that the word “analyze” represents a whole
spectrum of techniques and applications – and that’s okay. In a general sense,
you’ll want to see if fields in your dataset can give you some insight that you
can relate back to your initial goal(s).<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Some ideas:<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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</div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Look at correlations within your dataset. Are they positive or negative? Large or small?</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Look for the differences between your target and non-target population, variable by variable.</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Visuals help! Graphs are a great way to get a feel for the relationships between your variables.</span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Try building a predictive model. The results you get will be more directly applicable to driving decisions. </span></li>
</ul>
<div>
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<span style="font-family: Georgia, Times New Roman, serif;">You may get some surprising results during the analysis
phase. I’ve worked on projects where the end insight was the exact opposite of
what was expected. Although sometimes the results can be surprising, it’s
important to let your data tell its story. The other side of analysis is that
your data can confirm what you’ve long-suspected to be the truth – whether it’s
that students from Montana are more likely to enroll, or that the number of
first term credits impacts a student’s likelihood of attrition – embrace these
confirmations and continue to rely on that information. </span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Turn analysis into
insight<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Keep your initial question in mind, take what you’ve learned
from your analysis, and apply it going forward. The idea here is to replace
outdated anecdotal evidence with insights from our data. If your goal was to
save money on prospect marketing efforts, use the factors that correlate to a
higher response rate to drive your decisions about who will receive the next
round of direct mail. If you’re trying to improve retention rate, target those
students who look most like previously dropped students and reach out to help
keep them on campus. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Assess your decisions<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Last, but certainly not least, don’t forget to circle back
and re-assess your decisions. If you feel like you’re not making progress
toward your initial goal, consider re-framing it or breaking it down into more
manageable phases. If you feel good about the progress you’re making, start
working on new goals. A data-driven decision should be sustainable under
conditions similar to the past. Don’t be afraid to revisit past goals if you
feel like you can improve or add something to your initial recommendation. </span><o:p></o:p><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">...Did we miss anything? Have questions about becoming more data-driven? Leave them in the comments below.</span></div>
<br />
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<span style="font-family: Georgia, 'Times New Roman', serif;">-Caitlin Garrett, Statistical Analyst at </span><a href="http://rapidinsightinc.com/" style="font-family: Georgia, 'Times New Roman', serif;" target="_blank">Rapid Insight</a></div>
</div>
</div>
</div>
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-81206860344777531042013-04-10T09:52:00.004-04:002013-04-29T16:40:34.888-04:00Infographic: How One Small School Improved Student Retention<span style="font-family: Georgia, Times New Roman, serif;">Here's how Paul Smith's College in upstate New York went about improving student retention:</span><br />
<br />
<iframe frameborder="0" height="2243" scrolling="no" src="//infogr.am/paulsmiths-708748" style="border: none;" width="550"></iframe><br />
<div style="border-top-color: rgb(172, 172, 172); border-top-style: solid; border-top-width: 1px; padding-top: 3px; text-align: center; width: 550px;">
<div style="font-family: Arial; font-size: 10px;">
<a href="http://infogr.am/paulsmiths-708748" style="color: #acacac; text-decoration: none;" target="_blank">Paul Smiths College and Student Retention</a> | <a href="http://infogr.am/" style="color: #acacac; text-decoration: none;" target="_blank">Create infographics</a></div>
<div style="font-family: Arial; font-size: 10px;">
<a href="http://infogr.am/" style="color: #acacac; text-decoration: none;" target="_blank"><br /></a></div>
<div style="font-family: Arial;">
<a href="http://infogr.am/" style="color: #acacac; text-decoration: none;" target="_blank"><br /></a></div>
<div style="text-align: left;">
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett, Statistical Analyst at <a href="http://www.rapidinsightinc.com/" target="_blank">Rapid Insight</a></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">We'd love to hear about your strategies for tackling retention - what works, what doesn't, and what you'd like to try. Please feel free to share your perspective in the comments section :)</span></div>
</div>
Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-8191030418474848386.post-26934709693128264362013-04-02T09:30:00.000-04:002013-04-02T09:30:00.577-04:00Rapid Insight at Ellucian Live<br />
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<a href="http://www.ellucian.com/EllucianLive/" target="_blank"><img border="0" height="86" src="http://2.bp.blogspot.com/-MATb-d544L4/UVmV53qH2vI/AAAAAAAAAUc/EYJcKJyerNM/s320/ellucian+live.png" width="320" /></a></div>
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<span style="font-family: Georgia, Times New Roman, serif;">If you’re planning on attending <a href="http://www.ellucian.com/EllucianLive/" target="_blank">Ellucian Live</a> next week, we’d
like to invite you to check out our session “Bringing Predictive Analytics In-House:
A Case Study with Dickinson College”, co-presented by Michael Johnson, Director
of Institutional Research at Dickinson College, and Michael Laracy, Founder and
CEO of Rapid Insight. The session will take place on April 9<sup>th</sup> at
10:50am in Room 204C. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">As an <a href="http://www.ellucian.com/controls/handlers/search.router.ashx?id=3614" target="_blank">Ellucian Community Partner</a>, Rapid Insight provides
Ellucian customers with predictive modeling solutions that are easy to
implement. Join this session to see what is possible when you bring the right
kind of predictive modeling in-house. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Attendees will discover how they can successfully use
predictive modeling in all parts of the enrollment management process,
including student retention, as well as to boost fundraising effectiveness. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">We hope you’ll join our session and stop by Booth 102 to say
hello!<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">*</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">For those who aren’t attending Ellucian Live, be sure to check
out our <a href="http://www.rapidinsightinc.com/media/pdfs/dickinson_case_study.pdf" target="_blank">case study</a> and <a href="http://www.rapidinsightinc.com/media/demos/rapid_insight_lessons_johnson_web/rapid_insight_lessons_johnson_web.php" target="_blank">webinar</a> with Mike Johnson on his experiences with building
predictive models at Dickinson College. </span><o:p></o:p></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-55926145167454444062013-03-20T12:05:00.002-04:002013-03-20T12:06:40.730-04:00Customer Webinar: Predictive Modeling for SEM<span style="font-family: Georgia, Times New Roman, serif;">Our next customer webinar, "Strategic Enrollment Management: St. Michael's College and Predictive Analytics" will be given by Bill Anderson, CIO of Saint Michael's College <a href="https://www2.gotomeeting.com/register/611762562" target="_blank">today at 2pm EDT</a> and will be re-broadcasted on <a href="https://www2.gotomeeting.com/register/766586106" target="_blank">Tuesday, March 26th</a>, and <a href="https://www2.gotomeeting.com/register/197880058" target="_blank">Thursday, May 2nd</a>. </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">I got the chance to ask him a couple of questions about his session, which will describe the ways in which Veera and Analytics are utilized on campus to produce predictions and other analyses for the scoring team. </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><b>What types of models have you been building?</b></span><br />
<span style="font-family: Georgia, Times New Roman, serif;">Almost entirely enrollment management - mostly apply to enroll. We've been building them on and off for about five years now. I have someone on campus that I collaborate with and when we first started, she was using SPSS for the statistical analysis, but we've since abandoned that. </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><b>How has model building changed your Enrollment and/or Financial Aid practices?</b></span><br />
<span style="font-family: Georgia, Times New Roman, serif;">There have been a number of ways that we've used the models - one as a sort of verification of what our consultant has been doing, two to be able to do some sensitivity and what-if analysis (and suggest different practices or emphases on where the aid awards should go), and three to help confirm in-semester and in-process prediction on where the class is going to end up. </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">In some occasions, this has impacted size of waiting list or the way we thought about awarding wait list spots, including the total number of admits. This last year, our model suggested that we could be more selective than we had been in the past. </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"><b>What do you hope attendees will learn from your presentation?</b></span><br />
<span style="font-family: Georgia, Times New Roman, serif;">One thing is that you can do it on your own - it's not that hard. You have to have a background that supports responsible interpretation of the results, but you can sit down and do it. That's one element: just do it. I think there's another element that says once you start thinking this way, it can become infectious. In our enrollment management meetings, we have the opportunity to appeal to the data or look at a Veera job that identifies the applicants we could avoid accepting. This changes the internal conversation - from a culture of anecdote, you can change the conversation with data. The use of the products has been fabulous in terms of making the data accessible to people. </span>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-18479736506146152702013-03-12T13:49:00.000-04:002013-03-12T13:49:44.123-04:00Rapid Insight's 5th Annual User Conference<iframe allowfullscreen="" frameborder="0" height="315" src="http://www.youtube.com/embed/9jK-NcRmVcw" width="420"></iframe><br />
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">Let the countdown to the <a href="http://www.rapidinsightinc.com/conference.php" target="_blank">5th annual Rapid Insight User Conference</a> begin! Here’s what you need to know about this fun and
informative event:<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">We are making one big change this year: we’ve outgrown our
space here in NH and are hosting the conference on the campus of Yale
University in New Haven, Connecticut. It will kick off at 9am on Thursday, June
27<sup>th</sup> and wrap up by 4pm on Friday, June 28<sup>th</sup>. The cost of
the conference is $150 per attendee. In
addition to the presentations and hands-on labs, we’ll be providing a continental
breakfasts and an evening reception to all registrants. <o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">For User Conference lodging, we recommend the Omni New Haven
Hotel at Yale. We have arranged a special rate of $169/night + tax available
through 5/26. You’ll find the dedicated Conference link to guarantee this rate,
along with additional travel information, on the <a href="http://www.rapidinsightinc.com/conference.php" target="_blank">official User Conference webpage</a>.
<o:p></o:p></span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Be sure to check the <a href="http://www.rapidinsightinc.com/conference.php" target="_blank">Conference webpage</a> frequently for
updates on specific sessions and activities as the date draws near. We look
forward to seeing you there!</span><o:p></o:p></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-32389054564976865332013-03-05T14:08:00.004-05:002013-04-29T16:39:56.340-04:00Six Predictive Modeling Mistakes<span style="font-family: Georgia, 'Times New Roman', serif;">As we mentioned in our post on </span><a href="http://rapidinsight.blogspot.com/2012/12/five-data-preparation-mistakes-and-how.html" style="font-family: Georgia, 'Times New Roman', serif;" target="_blank">Data Preparation Mistakes</a><span style="font-family: Georgia, 'Times New Roman', serif;">, we've built many predictive models in the Rapid Insight office. During the predictive modeling process, there are many places where it's easy to make mistakes. Luckily, we've compiled a few here so you can learn from our mistakes and avoid them in your own analyses:</span><br />
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Failing to consider
enough variables<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">When deciding which variables to audition for a model, you
want to include anything you have on-hand that you think could possibly be
predictive. Weeding out the extra variables is something that your modeling
program will do, so don’t be afraid to throw the kitchen sink at it for your
first pass. <o:p></o:p></span></div>
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<a href="http://1.bp.blogspot.com/-4BPUkDsjLco/UTZCYLYllTI/AAAAAAAAAS8/s47bXoJW3pA/s1600/6001119320_c39d69ea81.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="320" src="http://1.bp.blogspot.com/-4BPUkDsjLco/UTZCYLYllTI/AAAAAAAAAS8/s47bXoJW3pA/s320/6001119320_c39d69ea81.jpg" width="240" /></a><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Not hand-crafting
some additional variables<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Any guide-list of variables should be used as just that – a
guide – enriched by other variables that may be unique to your institution. If there are few unique variables to be had,
consider <a href="http://rapidinsight.blogspot.com/search/label/Creating%20Variables%20Series" target="_blank">creating some</a> to augment your dataset. Try adding new fields like
“<a href="http://rapidinsight.blogspot.com/2012/02/creating-variables-distance-from-campus.html" target="_blank">distance from institution</a>” or creating riffs and derivations of variables you
already have. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Selecting the wrong
Y-variable<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">When building your dataset for a logistic regression model,
you’ll want to select the response with the smaller number of data points as
your y-variable. A great example of this from the higher ed world would come
from building a retention model. In most cases, you’ll actually want to model
attrition, identifying those students who are likely to <i>leave</i> (hopefully the smaller group!) rather than those who are
likely to <i>stay</i>. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Not enough Y-variable
responses<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Along with making sure that your model population is large
enough (1,000 records minimum) and spans enough time (3 years is good), you’ll
want to make sure that there are enough Y-variable responses to model. Generally,
you’ll want to shoot for at least 100 instances of the response you’d like to
model. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Building a model on
the wrong population<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">To borrow an example from the world of fundraising, a model
built to predict future giving will look a lot different for someone with a
giving history than someone who has never given before. Consider which
population you’d eventually like to use the model to score and build the model
tailored to that population, or consider building two models, one for each
sub-group. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Judging the quality
of a model using one measure<o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">It’s difficult to capture the quality of a model in a single
number, which is why modeling outputs provide so many model fit measures.
Beyond the numbers, graphic outputs like <a href="http://rapidinsight.blogspot.com/2013/01/how-to-interpret-decile-analysis.html" target="_blank">decile analysis</a> and lift analysis can provide
visual insight into how well the model is fitting your data and what the gains
from using a model are likely to be. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">If you’re not sure which model measures to focus on, ask
around. If you know someone building models similar to yours, see which ones
they rely on and what ranges they shoot for. The take-home point is that with
all of the information available on a model output, you’ll want to consider
multiple gauges before deciding whether your model is worth moving forward
with. </span><o:p></o:p></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett, Statistical Analyst at <a href="http://rapidinsightinc.com/" target="_blank">Rapid Insight</a></span><span style="font-family: Georgia, 'Times New Roman', serif;"><br /><span style="font-size: xx-small;">Photo Credit: http://www.flickr.com/photos/mattimattila/</span></span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><span style="font-size: xx-small;"><br /></span></span>
<span style="font-family: Georgia, Times New Roman, serif;">Have you made any of the above mistakes? Tell us about it (and how you found it!) in the comments. </span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-85838968866555625242013-02-05T09:00:00.000-05:002013-02-06T16:29:08.444-05:00Facebook's Graph Search and Prospect Research<div class="MsoNormal">
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<i style="font-family: Georgia, 'Times New Roman', serif;">“Facebook’s
mission is to make the world more open and connected. The main way we do this
is by giving people the tools to map out their relationships with the people
and things they care about. We call this map the graph. It’s big and constantly
expanding with new people, content, and connections. There are already more
than a billion people, more than 240 billion photos, and more than a trillion
connections. Today we’re announcing a new way to navigate these connections and
make them more useful.”</i><span style="font-family: Georgia, 'Times New Roman', serif;"> </span><span style="font-family: Georgia, 'Times New Roman', serif;"> </span><span style="font-family: Georgia, 'Times New Roman', serif;">[</span><a href="http://newsroom.fb.com/News/562/Introducing-Graph-Search-Beta" style="font-family: Georgia, 'Times New Roman', serif;">Facebook</a><span style="font-family: Georgia, 'Times New Roman', serif;">]</span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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</div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Introducing Graph
Search<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Last week, Facebook unveiled their new Graph Search tool,
which allows users to search for Facebook users by interests, likes,
relationship status, and location, among other qualifiers. Examples of searches
include “Friends who like yoga who live in Chicago”, “Pictures of friends taken
before 1998”, or “Friends who like Make-A-Wish”. The results of these searches
can reveal full names, addresses, employers, friends and family, and photographs.
Creative searching can yield some very telling results, as evidenced by a
popular Tumblr site’s <a href="http://actualfacebookgraphsearches.tumblr.com/">investigation
into search possibilities</a>. Currently,
graph search is still in beta, and you can join the waiting list <a href="https://www.facebook.com/about/graphsearch">here</a>. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Specifics on Graph
Search Data<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<a href="http://4.bp.blogspot.com/-RSb3yMrrtXA/UREWm2wak9I/AAAAAAAAASk/3oHjgCfKXe4/s1600/facebook.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://4.bp.blogspot.com/-RSb3yMrrtXA/UREWm2wak9I/AAAAAAAAASk/3oHjgCfKXe4/s200/facebook.jpg" width="200" /></a><span style="font-family: Georgia, Times New Roman, serif;">In truth, all of the data gathered by Graph Search has been
available for quite some time. But the
lack of an all-encompassing search feature made this data fairly obscure and
hard to collect - until now. So far,
researchers aren’t sure how users will react to their personal data being more easily
mined. Many users are likely to get a bit freaked out by their inclusion in
these “big net” searches. They’ll respond
by making their information more private using Facebook’s existing privacy
settings. Chances are that most will passively
accept this feature as an acceptable part of living in an age of social
connectivity. A few may even begin sharing
more information in an effort to provide and receive more of the purported
benefits. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">Potential users of Graph Search need to remember the caveats. Facebook’s information can be incomplete, deceptive,
and even fictitious (“ironic likes” for example). Then there are the obvious limitations – users
need to like pages to generate searchable connections. But the
breadth and depth of data Facebook offers can’t be found anywhere else. Leveraging the interlacing interests of individuals,
businesses, and organizations into some very powerful insights is simply too
valuable to ignore.<o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Using Graph Search
for Prospect Research<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">So what does Graph Search mean for prospect
researchers? It means effectively mining
the 8+ years of data that Facebook has been collecting just got a whole lot
easier. There are several ways that I
see it helping immediately. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">The ease of collecting data makes it easier to patch holes
in current constituent datasets. With a little creativity, leveraging the new search
options may make more imputation of variables possible, particularly by
examining constituent relationships and interests. For example, age can be
imputed by graduation year, which will become searchable. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">There will be better opportunities for identifying new
constituents based on searches. Possible search ideas include: friends of those
who are already involved with the organization, people who live nearby, people whose
interests coincide with your institution’s mission, or any combination of the
above. Finding friends of users who like a page is a quick search, and
aggregating this list to people who live nearby will become a piece of cake. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Your Institution’s Facebook
Page<o:p></o:p></span></b></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;"><br /></span></b></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">On the flip side, the interest and ability of others to find
you through a Graph Search should not be overlooked. Information about fundraising organizations
is about to become a whole lot more visible. The number of channels by which organizations
can be searched will also greatly increase, which can mean more traffic for
your page. Here are a few steps to take in preparation for the widespread
release:<o:p></o:p></span></div>
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<br /></div>
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</div>
<ul>
<li><span style="font-family: Georgia, Times New Roman, serif;">Fill out the basic information section of your page, and include as many relevant keywords as needed. This includes selecting a category and sub-categories if you haven't already. </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Make sure your address is up to date. Because users can search by address, you'll want this information to be as accurate as possible. </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Got photos? Label them with descriptive text, tag the people in them, and add a location to them. Photos are fair game for searches, and the more information you can provide at a glance, the better. </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">If you haven't already, update your page's URL to be customized, preferably containing the name of your organization. This will also improve your SEO on Google. </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Check your content. Gathering and retaining followers is more important than ever. Make sure to keep things relevant and interesting to keep people engaged. </span></li>
<li><span style="font-family: Georgia, Times New Roman, serif;">Once Graph Search becomes available to the whole Facebook community, try constructing searches that you would hope your page would appear in. If it doesn't, look to those whose pages did appear and imitate what they did to list so well - the sincerest form of flattery!</span></li>
</ul>
<div>
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<span style="font-family: Georgia, Times New Roman, serif;">For more information on Graph Search, visit <a href="https://www.facebook.com/about/graphsearch">https://www.facebook.com/about/graphsearch</a>
. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">For follow-up questions, or help working with your data for
this purpose, contact Caitlin Garrett at <a href="mailto:caitlin.garrett@rapidinsightinc.com">caitlin.garrett@rapidinsightinc.com</a>. Our next exploration will be on using Graph Search for Enrollment and Recruiting. Please feel free to comment if you have thoughts on additional ways to use the tool. </span><o:p></o:p></div>
</div>
<br />
<span style="font-family: Georgia, Times New Roman, serif;">Caitlin Garrett, Statistical Analyst at <a href="http://www.rapidinsightinc.com/" target="_blank">Rapid Insight</a></span></div>
<div class="MsoNormal">
<o:p></o:p></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-39690476365998057752013-01-29T10:17:00.000-05:002013-03-12T09:52:20.277-04:00Four Years of Predictive Modeling and Lessons Learned<a href="http://4.bp.blogspot.com/-tEKwTjXNazY/UJQeka96CQI/AAAAAAAAAPc/VOl_MeLegOM/s1600/johnsonm.jpg" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="200" src="http://4.bp.blogspot.com/-tEKwTjXNazY/UJQeka96CQI/AAAAAAAAAPc/VOl_MeLegOM/s200/johnsonm.jpg" width="133" /></a><span style="font-family: Georgia, 'Times New Roman', serif;">I recently got the chance to talk with Dr. Michael Johnson, Director of Institutional Research at Dickinson College, about his experiences with predictive modeling over the past four years. Dr. Johnson will be presenting a free </span><a href="https://www2.gotomeeting.com/register/253302778" style="font-family: Georgia, 'Times New Roman', serif;" target="_blank">webinar</a><span style="font-family: Georgia, 'Times New Roman', serif;">, "Four Years of Predictive Modeling and Lessons Learned", on Thursday, January 31st at 2pm EST in which he'll provide a more in-depth look at his experiences. </span><br />
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<span style="font-family: Georgia, Times New Roman, serif;"><b><br /></b></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><b>Can you give us an example of a lesson you’ve learned
through your experiences with predictive modeling?<o:p></o:p></b></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">I’ve learned that predictive modeling is good but predictive
modeling in real time is just more extremely beneficial. When we picked up
Rapid Insight, we moved a five day turnaround time to an eight minute
turnaround. That’s one of the biggest changes we’ve made, and the effects have
been very apparent. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">If there was another thing I’ve learned, it is to automate
absolutely every process possible to remove the opportunity for human error. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b>What types of predictive models will you be discussing
during your webinar?</b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">The enrollment management model is our primary model but a
close cousin to that is the one we’ve been using for retention. The dataset is
basically the same only slightly enhanced. It’s good to use essentially the
same dataset to solve two different problems. <o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b>How has predictive modeling changed the way you operate?</b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">It is the primary tool that we use to make decisions on the
incoming class. This last week has been an incredible example of that. We’re
taking a look at our early action pool and asking questions: What does it look
like? How does it compare with previous years? What if we swap out some people;
how does that change our incoming class?<o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><b>What do you hope attendees will take away from your webinar?</b><o:p></o:p></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">There’s really no need to reinvent the wheel, so I’ll share
some ideas that I’ve picked up. I hope that others come on board and share
their successes as well. We all have the same problem set, so it will be nice
for others to take away a few things that I’ve seen that have given me success.
It would be great if they had ideas that they wanted to share with others as
well. </span><o:p></o:p></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">To register for Dr. Johnson's free webinar, "Four Years of Predictive Modeling and Lessons Learned", or for more information, please <a href="https://www2.gotomeeting.com/register/253302778" target="_blank">click here</a>. </span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
<div class="MsoNormal">
<span style="font-family: Georgia, Times New Roman, serif;">To read a case study about how Dickinson College uses predictive modeling for strategic enrollment management, please <a href="http://www.rapidinsightinc.com/media/pdfs/dickinson_case_study.pdf" target="_blank">click here</a>. </span></div>
Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-34781620042436571642013-01-22T10:00:00.000-05:002013-01-22T10:00:08.856-05:00Dealing with Nulls in Veera Transform Formulas<span style="font-family: Georgia, Times New Roman, serif;">This next post comes from <a href="http://www.rapidinsightinc.com/about" target="_blank">Jeff Fleischer</a>, our Director of Client Operations, <a href="http://rapidinsight.blogspot.com/2012/11/customer-tips-from-jeff-fleischer-rapid.html" target="_blank">support wiz</a>, and analyst extraordinaire: </span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">Working out the logic of a new variable you want to create with a TRANSFORM node can be challenging. But when missing data ("nulls") get into the mix, it can be especially confusing and frustrating. For example, if you'd written the conditional formula...</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;"> IF ([A]='Freshman', 'UG', 'Grad')</span><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">...and some of the fields under column [A] were null, you would get nulls as an output for those rows rather than the desired 'Grad'. This is because trying to equate something with "nothing" confuses Veera as to what you would <i>really</i> want as a result. So here are some suggestions on how best to deal with those gaps and still get to the outcome you need...</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">1. </span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;">The most obvious way to deal with gaps in data is to replace them with something. This may not always be desirable, but when it is, using a CLEANSE ahead of your TRANSFORM is your best bet. Select the "Is Missing" operator and use Alt-Left Mouse to select all the columns that need their data fields filled in with that new value, like 'unknown'. </span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">Of course, you could instead place a CLEANSE </span><u style="font-family: Georgia, 'Times New Roman', serif;">after</u><span style="font-family: Georgia, 'Times New Roman', serif;"> your TRANSFORM, using it to fill in any missing values appearing in the new column. </span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">2.</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;">If filling in those data holes using a cleanse is not preferable, maybe just a temporary patch will do. Look for the "Treat Missings in Formula as Zeros" checkbox just above the "New Variable Name" field in the TRANSFORM. Just as the name suggests, this will <i>temporarily</i> replace any missing data with a zero, allowing most operations to function. Be careful, though, if the column you're evaluating already contains zeros - the output may not be what you intended!</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">3.</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;">If even temporarily replacing nulls with something else isn't an option, then change your TRANSFORM formula to deal with them ahead of everything else. To do this, you'll likely need to use one of two built-in Veera functions - IS NULL or IS NOT NULL. We might change our example to include another condition, such as...</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">IF ([A] IS NULL, 'Withdrawn', </span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;">IF ([A]='Freshman', 'UG', 'Grad'))</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">The idea here is to catch any nulls before they affect the rest of the logic by putting that condition first. </span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">4.</span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;">Finally, another (if more specialized) option might be to use the "Missings:" TRANSFORM feature. Unlike the "Treat Missings in Formula as Zeros" checkbox, this control changes nulls that appear as the final result of a formula. The replacement options offered by this feature are limited (0 or 1), but it may be an easy way to fix a problem with absent data appearing in a new numeric field. </span><br />
<span style="font-family: Georgia, 'Times New Roman', serif;"><br /></span>
<span style="font-family: Georgia, 'Times New Roman', serif;">-Jeff Fleischer</span>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-8191030418474848386.post-16009003042312077512013-01-16T09:00:00.000-05:002013-01-31T08:18:51.151-05:00How to Interpret a Decile Analysis<br />
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<span style="font-family: Georgia, Times New Roman, serif;">After building a predictive model, there are several ways to
determine how well the model is describing your data. One visual way to get an
idea of how well a model is fitting your data is by taking a look at the decile
analysis. Here we’ll take a look at what the decile analysis represents, how it’s
created, and how to spot a good model. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">What a Decile
Analysis Represents<br /><br /><o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">After building a statistical model, a decile analysis is
created to test the model’s ability to predict the intended outcome. Each
column in the decile analysis chart represents a collection of records that
have been scored using the model. The height of each column represents the
average of those records’ actual behavior. <o:p></o:p></span></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">How the Decile
Analysis is Calculated<br /><br /><o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">1. The hold-out or validation sample is scored according to the
model being tested. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">2. The records are sorted by their predicted scores in descending
order and divided into ten equal-sized bins or deciles. The top decile contains
the 10% of the population most likely to respond and the bottom decile contains
the 10% of the population least likely to respond, based on the model scores. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">3. The deciles and their actual response rates are graphed on
the x and y axes, respectively. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">After the decile analysis is built, you’ll want to take a
look at the height of the bars in relation to one another. Deciding whether a
model is worth moving forward with depends on the pattern you see when viewing
the decile analysis. </span><o:p></o:p></div>
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<b><span style="font-family: Georgia, Times New Roman, serif;">Ideal Situation: The
Staircase Effect<br /><br /><o:p></o:p></span></b></div>
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<span style="font-family: Georgia, Times New Roman, serif;">When you’re looking at a decile analysis, you want to see a
staircase effect; that is, you’ll want the bars to descend in order from left
to right, as shown below. <o:p></o:p></span></div>
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<a href="http://3.bp.blogspot.com/-z2QVBbo3vLk/UKK2y_GVuAI/AAAAAAAAAQY/rV_OAfY24zk/s1600/decile+analysis.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="246" src="http://3.bp.blogspot.com/-z2QVBbo3vLk/UKK2y_GVuAI/AAAAAAAAAQY/rV_OAfY24zk/s320/decile+analysis.png" width="320" /></span></a></div>
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<span style="font-family: Georgia, Times New Roman, serif;">This is telling you that the model is “binning” your
constituents correctly from most likely to respond to least likely to respond. A
model exhibiting a good staircase decile analysis is one you can consider
moving forward with. <o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><br /></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><b>Not-So-Ideal
Situations</b><o:p></o:p></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;"><b><br /></b></span></div>
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<span style="font-family: Georgia, Times New Roman, serif;">In contrast, if the
bars seem to be out of order (as shown below), the decile analysis is telling
you that the model is not doing a very good job of predicting actual responses.<o:p></o:p></span></div>
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<a href="http://1.bp.blogspot.com/-_290j5C2BOo/UKK22JXigOI/AAAAAAAAAQg/uVTmQrVqneg/s1600/bad+decile2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><span style="font-family: Georgia, Times New Roman, serif;"><img border="0" height="249" src="http://1.bp.blogspot.com/-_290j5C2BOo/UKK22JXigOI/AAAAAAAAAQg/uVTmQrVqneg/s320/bad+decile2.png" width="320" /></span></a></div>
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<span style="font-family: Georgia, Times New Roman, serif;"> If the bars seem to
be the same height, or the decile analysis looks “flat”, the decile analysis is
telling you that the model isn’t performing any better than randomly binning
people into deciles would. In both cases, your model should be improved before
moving forward with it. </span><o:p></o:p><br />
<span style="font-family: Georgia, Times New Roman, serif;"><br /></span>
<span style="font-family: Georgia, Times New Roman, serif;">-Caitlin Garrett, Statistical Analyst at <a href="http://www.rapidinsightinc.com/" target="_blank">Rapid Insight</a></span></div>
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<br />Unknownnoreply@blogger.com0