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Sunday Morning Keynote | Case Studies on the Value of Information
Keynote: Rajkumar Venkatesan, Darden Graduate School of Business, University of Virginia
Developments in technology provide firms with access to a variety of information regarding customers and competition. However, there is uncertainty regarding the incremental value of this information over the investment required for collecting it. Rajkumar Venketasan presents a framework for accessing the value of information and illustrates it through two studies conducted with his co-authors in the pharmaceutical and retail industries, respectively.
PHARMACEUTICAL: In Study 1, the authors evaluate the value of customer attitudinal information in the prediction of customer lifetime value (CLV) and a firm’s customer management strategy. On the basis of monthly sales calls, sales, and survey-based attitude information collected from customers of a multinational pharmaceutical firm over 45 months, they develop a zero-inflated Poisson model framework to simultaneously model retention and sales, and hence CLV.
Models to impute missing attitude data using commonly available behavioral predictors are also developed. Including customer attitude data substantially improves predictions of CLV for customers with observed as well as imputed attitudes. A customer-level resource allocation strategy that includes information on customer attitudes, both observed and imputed, yields the highest profits for the firm. In this study’s empirical context, information derived from customer attitudes is well worth the investment in data collection.
RETAIL: In Study 2, the authors evaluate the value provided by information on customer relationships with the competition on the design of a retailer’s mobile loyalty program and customer retention. Data for this study is provided by a mobile application that offers points based loyalty programs for more than 5,000 retailers in the US.
The authors develop a joint model of store choice, trip spending, and reward coupon redemption that allows for spatial agglomeration benefits among stores, and heterogeneity in consumer preferences. Firms overestimate profits when competitive information is not included in their models. Retailers can leverage benefits from positive spatial agglomeration to provide lesser value rewards on their loyalty programs and thereby improve profits. The study provides ways for intermediaries to calculate the price they can charge retailers for aggregated competitive information.
In both case studies, Dr. Venketesan and his colleagues show that analytical capabilities are necessary for realizing the potential offered by new sources of customer and competitive information.
Study 1 (pharmaceutical) is co-authored with Werner Reinartz, University of Cologne and Nalini Ravishanker, University of Connecticut.
Study 2 (retail) is co-authored with Joseph Pancras and Bin Li, University of Connecticut.