Using Social Media Data

Every minute, millions of pieces of social media data are generated around the world. In any given minute*:

Instagram users share 3,600 photos
Brands and organizations on Facebook receive 34,722 “likes”
Twitter users send over 100,000 tweets

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:

• For a 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.
• In a 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.
•  Brands 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. 

Tracking and leveraging these data points has the potential to add value to the predictive models you’re already building.  

So, are you already leveraging your social media data into your models, and if not, why not?

*Source: http://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/
*Photo: http://timothybrand.com/wp-content/uploads/2012/04/like-follow-social-networking.jpg

Five Steps for Data-Driven Strategic Enrollment Management

Establish your goals

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.

Possible goals include:

• Reduce your prospect mailing budget
• Increase accuracy of enrollment yield predictions
• Meet diversity objectives
• Increase your retention rate

Get to know your data

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.

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.

A few suggestions:

• Spot-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.  If you spot any data quality issues, do your best to resolve them sooner than later.
• Check for 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.
• Brainstorm ideas for new variables. If you can’t create new variables from what you have on-hand, spend some time thinking about things that might be worth tracking going forward. 

Analyze your data

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).

Some ideas:

• Look at correlations within your dataset. Are they positive or negative? Large or small?
• Look for the differences between your target and non-target population, variable by variable.
• Visuals help! Graphs are a great way to get a feel for the relationships between your variables.
• Try building a predictive model. The results you get will be more directly applicable to driving decisions. 

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. 

Turn analysis into insight

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.

Assess your decisions

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. 

...Did we miss anything? Have questions about becoming more data-driven? Leave them in the comments below.

-Caitlin Garrett, Statistical Analyst at Rapid Insight

Infographic: How One Small School Improved Student Retention

Here's how Paul Smith's College in upstate New York went about improving student retention:

Paul Smiths College and Student Retention | Create infographics

-Caitlin Garrett, Statistical Analyst at Rapid Insight

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 :)

Rapid Insight at Ellucian Live

If you’re planning on attending Ellucian Live 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^th at 10:50am in Room 204C.

As an Ellucian Community Partner, 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.

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.

We hope you’ll join our session and stop by Booth 102 to say hello!

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For those who aren’t attending Ellucian Live, be sure to check out our case study and webinar with Mike Johnson on his experiences with building predictive models at Dickinson College. 

Customer Webinar: Predictive Modeling for SEM

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 today at 2pm EDT and will be re-broadcasted on Tuesday, March 26th, and Thursday, May 2nd. 

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. 

What types of models have you been building?
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. 

How has model building changed your Enrollment and/or Financial Aid practices?
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. 

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. 

What do you hope attendees will learn from your presentation?
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.