APRA Summit on Data Mining and Modeling

I would be negligent in my duties as promoting data mining and predictive modeling in the area of fundraising if I didn't promote this upcoming conference. This is a fantastic new forum that will feature many of the brightest and most creative minds in our field, including my boss Josh Birkholz. The conference also coincides with the release of his new book.

I will be there as well, and hope to connect with those who read this blog for in-person discussions about where data mining and modeling is today in fundraising, and where future directions may take us.

Hope to see you there!

Summit on Prospect Data Mining and Modeling April 3 – 4, 2008

Don’t miss the first-ever APRA Summit on Prospect Data Mining and Modeling - the year's best opportunity to interact with prospect researchers and analysts engaged at the cutting edge of the advancement research field. This two-day symposium will be divided into two groups of sessions: a beginners/management track, and an intermediate/advanced track. The beginners/management track will provide a solid grounding in the goals of, methods for and approaches to data mining. The intermediate/advanced track will showcase new technologies and present case studies of effective applications of statistical methods to prospecting and prospect management.

Whether you’re a proficient data miner, or a researcher or manager contemplating a foray into data mining, this summit will provide you with fresh insights, understanding and tools to help you better understand your constituent base. If you are engaged in building your prospect pool, looking for ways to prioritize and bring focus to an unwieldy database, or seeking to discover diamonds hidden in the rough of a broad annual base of support, this event is for you.

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Why Demographic Data Just Won't Die

This is a really interesting perspective on what many, myself included, may now consider one of the relic's of predictive modeling: basic demographic data. This data is basic, sometimes clumsy--the data we used in college to learn the techniques of statistics, regression analysis, and econometrics. As analytics junkies today, we all strive to build models and tools to help us fit the contours of the populations we study and to levels much more precise than a zip code or an age group. In modeling, there is “power in numbers,” but there is also an aggregation danger at play when using broad metrics which capture individual behavior and preferences.

I have been posting for some time now on this blog about the frontiers of text-analytics and the raw potential inherent in such custom data mining approaches, that I fear I may have become too nano in my purview.

Behavioral modeling is definitely one of the sharper tools in our toolbox, but read this article and you may find yourself having a similar reaction that I did: reconsidering the benefits and devising new applications for using demographic data.

Demographics: The Targeting Construct That Wouldn't Die

Recently, our customers have communicated a message to us loud and clear. It is a message that may seem counterintuitive here in the 21st century, in the all-digital, micro-targeting, behavioral targeting, contextual targeting age.

Demographics, they tell us, are of paramount importance.

No, seriously. Demographics. Like age, gender, household income. I know; it’s as if I told you I was converting all my MP3s to 8-track, right?

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Sentiment Analytics Opportunities

A colleague provided a link to this article and I loved the title: Sentiment Analysis. This article is another perspective on a theme I have been posting on this forum for some time—moving fundraising analytics beyond simply “who” and “how much” (which are important questions) into more analysis of giving motivations, or "why.”

Presented here is a more in-depth consideration of some of the inherent challenges in using text analytics. The most basic challenge discussed is that opinions (say for example affinity) are harder to describe than facts (I gave $100). This article touches on some basic concepts that may “boost” fuzzy opinions and statements into data with high utility and function. Some of these strategies include:

*Classifying the source for more tailored analysis (gift officer notes, institutional survey, donor pledge card).
*If you have the appropriate software-lexical choice analysis.
*Bayesian methods to identify matching patterns.
*Hybrids of sentiment and account fielded (primarily numeric) analysis to improve sentiment “accuracy.”
*Making “two passes” at text—using automated tools/software, then a set of human eyes to verify results.

This article poses more questions than answers, but I believe with sentiment analytics relatively absence in the fundraising world, questions are the best place to start.

Sentiment Analysis: Opportunities and Challenges

Sentiment analysis is one of the most exciting applications of text analytics today. It may also be the most challenging. The steps involved in sentiment analysis are easy enough to grasp: use automated tools to discern, extract, and process attitudinal information found in text; apply to sources as varied as articles, blog postings, e-mail, call-center notes, and survey responses that capture facts and opinions. What do customers, reviewers, the business community – thought leaders and the public – think about your company and your company's products and services – and about your competitors? What can you learn that will help you improve design and quality, positioning, and messaging and also respond quickly to complaints?

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