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.

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 🙂

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