Essays in direct marketing : understanding response behavior and implementation of targeting strategies




Sinha, Shameek

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In direct marketing, understanding the response behavior of consumers to marketing initiatives is a pre-requisite for marketers before implementing targeting strategies to reach potential as well as existing consumers in the future. Consumer response can either be in terms of the incidence or timing of purchases, category/ brand choice of purchases made as well as the volume or purchase amounts in each category. Direct marketers seek to explore how past consumer response behavior as well as their targeting actions affects current response patterns. However, considerable heterogeneity is also prevalent in consumer responses and the possible sources of this heterogeneity need to be investigated. With the knowledge of consumer response and the corresponding heterogeneity, direct marketers can devise targeting strategies to attract potential new consumers as well as retain existing consumers.

In the first essay of my dissertation (Chapter 2), I model the response behavior of donors in non-profit charity fund-raising in terms of their timing and volume of donations. I show that past donations (both the incidence and volume) and solicitation for alternative causes by non-profits matter in donor responses and the heterogeneity in donation behavior can be explained in terms of individual and community level donor characteristics. I also provide a heuristic approach to target new donors by using a classification scheme for donors in terms of the frequency and amount of donations and then characterize each donor portfolio with corresponding donor characteristics.

In the second essay (Chapter 3), I propose a more structural approach in the targeting of customers by direct marketers in the context of customized retail couponing. First I model customer purchase in a retail setting where brand choice decisions in a product category depend on pricing, in-store promotions, coupon targeting as well as the face values of those coupons. Then using a utility function specification for the retailer which implements a trade-off between net revenue (revenue – coupon face value) and information gain, I propose a Bayesian decision theoretic approach to determine optimal customized coupon face values. The optimization algorithm is sequential where past as well as future customer responses affect targeted coupon face values and the direct marketer tries to determine the trade-off through natural experimentation.




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