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:
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| Bridget Mendoza |
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?
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| 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 :)
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