Developing a Product Recommendation Model Using Spatial Statistics and Joint Space Mapping

EXTENDED ABSTRACT - A product recommendation model (Ansari, Essegaier, and Kohli 2000) is a key tool in customer relationship management (CRM). An effective recommendation model contributes to the CRM goal of customer expansion by offering high-valued products to promising regular customers. When regular customers purchase multiple products from the same company, long-term customer retention is likely to be stronger because of customers’ increased benefits and high switching costs.

Citation:

Sangkil Moon and Gary J. Russell (2005) ,"Developing a Product Recommendation Model Using Spatial Statistics and Joint Space Mapping", in AP - Asia Pacific Advances in Consumer Research Volume 6, eds. Yong-Uon Ha and Youjae Yi, Duluth, MN : Association for Consumer Research, Pages: 194-195.

Asia Pacific Advances in Consumer Research Volume 6, 2005      Pages 194-195

DEVELOPING A PRODUCT RECOMMENDATION MODEL USING SPATIAL STATISTICS AND JOINT SPACE MAPPING

Sangkil Moon, North Carolina State University, U.S.A.

Gary J. Russell, University of Iowa, U.S.A.

EXTENDED ABSTRACT -

A product recommendation model (Ansari, Essegaier, and Kohli 2000) is a key tool in customer relationship management (CRM). An effective recommendation model contributes to the CRM goal of customer expansion by offering high-valued products to promising regular customers. When regular customers purchase multiple products from the same company, long-term customer retention is likely to be stronger because of customers’ increased benefits and high switching costs.

This research develops a product recommendation model based upon the principle that customer preference similarities stemming from prior response behavior is a key element in predicting their current product purchase. The proposed product recommendation model is dependent upon two complementary methodologies: joint space mapping (placing customers and products on the same pick-any scaling map) and spatial choice modeling (specifically, the autologistic model (Besag 1974)). Briefly stated, spatial choice models (Bronnenberg and Mahajan 2001; Bronnenberg and Sismeiro 2002) assume that choices made by one customer are positively correlated with choices made by other customers with similar preferences. Using a joint space map based upon past purchase behavior, a predictive model is calibrated in which the probability of product purchase depends upon the customer’s relative distance to other customers. This approach allows the analyst to predict a given customers’ purchase probability using the model in which he purchase probability depends upon purchase behavior of similar customers. In this paper, the proposed product recommendation model considers only a single target product, reducing the model objective to finding most promising customers who will buy the given target product. An empirical analysis of this single-product recommendation model compares the proposed autologistic (AL) model along with three reasonable benchmark modelsB(1) the principal components (PC) logit model, (2) Moore-Winer (MW) model (Moore and Winer 1987), and (3) collaborative filtering (CF) (Herlocker, Konstan, and Riedl 1999). In the analysis, the autologistic model provides excellent predictions relative to the three benchmark models for a customer database of an insurance firm.

This study is intended to provide a database marketer with a new tool for identifying promising customers. From a methodological perspective, we demonstrate how ideas from spatial statistics can be used to develop a model that links customer similarity to choice behavior. In particular, the approach promises to be very effective in capturing the effects of variables that drive choice behavior, but are not explicitly included in the customer database available to the researcher. For example, variables such as lifestyle, psychographics, financial resources, and product features often determine choice behavior, but are typically absent from a firm’s database. As long as a psychometric map can be created in which locational proximity is related to product preference, the suggested methodology allows the analyst to construct a model that implicitly contains more information about customer behavior than is apparent from the available data.

The current model is limited to predict customer purchases of only a single target product. In future research, it would be worthwhile to generalize the single-product model to construct a multiple-product recommendation model that allows the researcher to integrate product similarity to deal with a multiple-product recommendation case. Eventually, exploring a further generalization of the extended multiple-product model is suggested for future research to incorporate temporal dynamics, particularly for repeatedly purchased products. In brief, the final model will be able to deal with three dimensions of purchasesB(1) customer similarity, (2) product similarity, and (3) temporal dynamics.

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