Why Mathematical Models Just Don't Add Up

This article presents an interesting perspective on quantitative models of prediction vs qualitative models of prediction. Two main themes can be drawn from this article and applied to prospect research and its utilization of predictive modeling:

1) Be good consumers of research and research techniques. Not every model or technique is a good fit for your questions, or the information available to you (your data).
2) Ask questions outside the box. Instead of just "who is giving" and "how much they might give", ask "why are they giving", and "when might they give" (vs "when might we as an organization ask").

Don't be afraid of "what if" questions either. "What if we managed prospects by affinity rather than capacity, how might our campaign's opportunities for success change?"

Prospect research has barely scratched the surface in respect to analytics, and the opportunities it offers to inform and contribute to our abilities to maximize organizational fundraising potential. Being both critical and creative about what we do as researchers, as well as why we do it, is fundamental to this field reaching new frontiers of success.

Assurances by scientists that the outcome of nature's dynamic processes can be predicted by quantitative mathematical models have created the delusion that we can calculate our way out of our environmental crises. The common use of such models has, in fact, damaged society in a number of ways.

For instance, the 500-year-old cod fishery in the Grand Banks, off Newfoundland, was destroyed by overfishing. That happened in large part because politicians, unable to make painful decisions on their own to reduce fishing and throw thousands of people out of work, shielded themselves behind models of the cod population — models that turned out to be faulty. Now the fish are gone, and so are the jobs.

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