Using Predictive Modeling to Drive Fundraising Efforts

In preparation for their presentation at our upcoming User Conference, "Using Predictive Modeling to Focus your Fundraising Efforts", I got the chance to chat with Bridget Mendoza and Brianna Lowndes from the Whitney Museum of American Art. 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. 

Here are their thoughts on building their skillsets, modeling challenges, and how the process is going so far:

Bridget Mendoza

What triggered your interest in predictive modeling for the Whitney Museum?

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.

How did you decide internally who would take on the predictive modeling project?

Brianna Lowndes

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.

How did you build your predictive modeling skillset?

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.

What modeling challenges have you found that are unique to a museum?

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. 

Do you have any advice for non-profits who are thinking about predictive modeling in-house?

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.

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. 

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.   

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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, click here. Both users and non-users are welcome to attend.

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 :)

Tips for Charting in Veera

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.

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.

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. 

As you begin to navigate through the Chart Style Editor window, here are some things to keep in mind:

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.

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.

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.

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.

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.

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.

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.

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):

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.

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.

Do you have any tips for charting, or questions about how to use the charting node? Leave them in the comments below :)

Happy charting!

-by Jon MacMillan, Data Analyst at Rapid Insight

Set it and Forget it: The Case for Automated Reporting

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 Veera. Here’s our take on automated reporting, by the numbers; click on the links for full case studies:

The number of days (including interruptions!) it took Gloria Stewart, Director of Institutional Research at Schreiner University, to build an automated report.  

The number of departments within Tulsa County Juvenile Bureau that depend on reports that Shonn Harrold, Assistant Director, has automated. These reports include intake reports, detention center reports, case assignments, and referral reports. 

The number of hours that Scott Alessandro, Assoc. Director of Educational Services at MIT Sloan School of Management, saves each week by editing semi-automated ad hoc reports rather than creating new reports. 

The percentage of Excel spreadsheets that have errors, according to a 2008 study. 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. 

The percent improvement 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."

What could you do with an extra five hours per week?

-Caitlin Garrett, Statistical Analyst at Rapid Insight