Lets not forget about the Annual Fund; Behavioral Targeting

This article comes from a for-profit sector perspective and discusses an optimization technique using web data, commonly referred to as "behavioral targeting." Many of the articles and techniques shared here relate to predictive modeling, primarily for major giving—what about other giving populations who may yield smaller dollar amounts, but have more consistent patterns of philanthropy? Obviously the high-reward potential of accurately identifying transformative or major gift prospects is very attractive. There are other opportunities however, in which to apply predictive modeling techniques to support increasing the effectiveness of your giving programs at all gift levels.

The requirements (significant longitudinal data) and benefits (modeling of consistently stated preferences) of behavioral targeting make it an interesting technique when applied to the examination of annual giving behavior.

For example, if your institution had a well-developed online annual giving program, elements and principles of behavioral targeting could be applied. Inserting one simple, but well-designed affinity question into the process of submitting an online annual giving donation could produce some informative trends. From these trends, the annual giving program could be more specialized in targeting and messaging, as they seek to engage new constituents or increasing giving levels of current annual giving donors by identifying effective priorities and factors for giving.

The other benefit about these strategies is that they are relatively simple, when compared to complex major gift models with cluster analysis, etc.; the time invested may just be appropriately proportional to increase in dollars from focusing on populations of "base givers."


IN MY PAST FEW COLUMNS, I have set out to clarify optimization, a term that is often bandied about and regularly misunderstood.

I first covered testing, the most frequently used method of improving consumer response, which includes A/B testing and multivariate testing. With the targeting article, I covered how systems based on rules can be used to create more relevant experiences with better outcomes.

The third type is perhaps the most seductive -- and misunderstood -- form of optimization, behavioral targeting. (The fourth, social optimization, I will explain in the near future.)
What Is Behavioral Targeting?

The holy grail of direct marketing has been a system that detects consumer behavior and changes offers. The first incarnation of this approach was called data mining, and was focused on using data to drive strategic planning. There is an apocryphal story about Wal-Mart: "By scanning each sale into a data warehouse, grocery stores have determined that men in their 20s who purchase beer on Fridays after work are also likely to buy a pack of diapers. Thus, a display of Pampers or another brand might be set up in the beer aisle, or merchants will put one (but not both) of the products on sale on Friday evenings."

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