Exciting week - Josh's book is out and the Data Mining Summit

Josh's book, Fundraising Analytics: Using Data To Guide Strategy, has been formally released. He has already received wonderful feedback from people who have purchased it and read it in one afternoon. It is currently sold-out on Amazon last I checked, but you can order a copy that will be delivered once it is in stock again or you can try ordering it directly from the publisher. Pick it up!

Also, I will be in Nashville at the end of the week, attending the inaugural APRA Summit on Data Mining and Modeling. I am very excited to meet people who also have a interest, or even passion, for the work we do. Feel free to say hi, and comments/questions/critiques of this blog are also welcome. Hope to meet you there!

-Alex

P.S. Josh will have a few copies of his book for sale at the APRA Summit.

Can we Build a Better Zip Model?

Lately I have had a keen interest in demographic data and how it best fits with the tools we have and goals we seek in fundraising analytics. Certainly a plethora of affinity metrics and giving behavior makes our statistical mouths “water,” but demographic data still presents relevance and unique relationships (some good and some bad) when attempting to predict giving behavior.

I have recently posted articles suggesting another long look at demographic data (Why Demographic Data Just Won’t Die) and its benefits (Predictive Modeling the 2008 Elections…) in capturing difficult or complex decisions or choices. This article suggests some of the limitations of a zip model. While many of you may not use them regularly, I think zip-driven models may have utility for annual giving segmentation and mailings, and for institutions that rely heavily on a broad base of public and community support (urban public universities for example).

This article discusses some of the largest issues with zip-focused modeling, including aggregation, and the “self-fulfilling prophecy” phenomenon. It also offers some general but effective advice for anyone considering a zip model as an additional analytical tool.

How to Build a Better Zip Model

The May 2007 postal rate increase sent every direct retailer scrambling. It’s hard to argue the hike’s effectiveness as a catalyst for renewed analytical vigor.

Our clients have been analyzing everything from the impact of page count reductions and co-mailing programs to the most appropriate tools to optimize circulation. And for one, preliminary research indicated that a new zip model might be the right solution at the right time.

Zip modeling is not new. It remains a data-based tool that requires in-the-mail validation, but the postal rate increase was as good a time as any for many retailers to test it.

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Predictive Modeling the 2008 Elections...

In my content research for this blog, I look for specific articles relating to fundraising analytics, broader articles on analytics, or theory that provide either lessons or questions transferrable to our work, as well as other examples of creative minds using past behavior to predict future behavior. Without politicizing this blog, I want to share this article on Ken Strasma, a political analytics guru for a current presidential hopeful.

I was generally unaware of the depth and nuance of this pursuit of analytics. Particularly attractive I believe is the ability to model what are fundamentally just opinions (not financial transactions, such as charitable giving or consumer spending as opinions by proxy). I considered the lack of explicit numeric metrics to be a difficult obstacle to overcome, but Strasma and his colleagues have developed techniques to model not only complex preferences, but also predict what is essentially non-regular behavior (ie voting).

Strasma says:
“..there are a number of basic questions predictive analytics tries to answer for any campaign. These include how likely it is a voter is undecided, what issues undecided voters care about, how likely it is that a voter supports a certain candidate and how likely it is that an individual will contribute if asked.”

For our work, I considered this analysis to be similar to who has interest in giving, what causes do they support, how likely are they to support our organization, how much would they contribute to our organization, or more simply, who is a suspect, a prospect, what is the target, and what is the actual ask amount?

I hope this article enlightens your assumptions of predictive modeling, as it did for me.

Candidates Use Predictive Analytics To Seek Votes

As the primary race grinds on, the candidates are turning to predictive analytics tools to help find voters ready to support them.

A company called VisualCalc provides a free Web site that helps citizens analyze the presidential race through a series of dashboards that chart the status and trends of the primary election.

On the flip side, candidates in this year's historical race for the White House—for the first time a woman and a black man are vying for the Democratic Party nomination alongside a single presumptive Republican nominee—have similar tools to provide information that may help them attract those key undecided voters.

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