Modeling Parametric Evolution in a Random Utility Framework

Kim, Jin Gyo; Menzefricke, Ulrich; Feinberg, Fred M.

doi:10.1198/073500104000000550

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…Although our focus is on capturing duration dependence in the parameters, there are more general approaches to modeling parameter dynamics as in Kim et al (2005).…”

Section: Modelmentioning

confidence: 99%

Dynamic Customer Management and the Value of One-to-One Marketing

2009

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The concept of one-to-one marketing is intuitively appealing, but there is little research that investigates the value of individual-level marketing relative to segment-level or mass marketing. In this paper, we investigate the financial benefits of and computational challenges involved in one-to-one marketing. The analysis uses data from an online grocery and drug retailer. Like many retailers, this firm uses multiple promotional instruments including discount coupons, free shipping offers, and a loyalty program. We investigate the impact of customizing these promotions on the two most important consumer decisions: the decision to buy from the store and expenditure. Our modeling approach accounts for two sources of heterogeneity in consumers' responsiveness to various marketing mix elements: cross-sectional differences across consumers and temporal differences within consumers based on the purchase cycle. The model parameter estimates are fed into a dynamic programming model that determines the optimal number, sequence, and timing of promotions to maximize retailer profits. A series of policy simulations show that customizing promotions leads to a significant increase in profits relative to the firm's current practice of uniform promotions. However, the effectiveness of various promotions varies because of both cross-sectional differences in consumers as well within consumer heterogeneity due to purchase cycle factors. For instance, we find that free shipping tends to be the preferred instrument for re-acquiring lapsed customers, whereas an across-the-board price cut (via a discount coupon) is the most effective tool for managing the segment of most active customers. Interestingly, we find that customizing based on within-customer temporal heterogeneity contributes more to profitability than exploiting variations across consumers. This is important because the computational burden of implementing the dynamic optimization to account for cross-sectional heterogeneity is far greater than accounting for temporal heterogeneity. Furthermore, targeting promotions based only on timing rather than the nature and magnitude of the offers across consumers alleviates the public relations risks of price discrimination. Implications for marketing managers are also discussed.CRM, one-to-one marketing, promotions, dynamic programming, heterogeneity, database marketing

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“…Although our focus is on capturing duration dependence in the parameters, there are more general approaches to modeling parameter dynamics as in Kim et al (2005).…”

Section: Modelmentioning

confidence: 99%

Dynamic Customer Management and the Value of One-to-One Marketing

2009

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“…State space models have been used in various marketing modeling areas, such as brand choice modeling (Akcura, Gonul, and Petrova 2004;Kim, Menzefricke, and Feinberg 2005;Lachaab et al 2006), market structure modeling (Van Heerde, Mela, and Manchanda 2004), sales forecasting (Neelamegham and Chintagunta 2004), and advertising effect analysis (Bass et al 2007). State space models have been used in various marketing modeling areas, such as brand choice modeling (Akcura, Gonul, and Petrova 2004;Kim, Menzefricke, and Feinberg 2005;Lachaab et al 2006), market structure modeling (Van Heerde, Mela, and Manchanda 2004), sales forecasting (Neelamegham and Chintagunta 2004), and advertising effect analysis (Bass et al 2007).…”

Section: Predicting New Customers' Risk Type In the Credit Card Marketmentioning

confidence: 99%

Predicting New Customers' Risk Type in the Credit Card Market

Zhao

Ying

Song

2009

Journal of Marketing Research

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Recent studies in marketing have consistently shown that all customers are not equally profitable. In the credit card business, all customers are not equally risky. When a customer misses one payment on a credit card bill, a signal is sent to the credit card company. It is important for the card issuer to interpret the signal and to identify whether the customer is a low-risk one, who will eventually pay back the debt and contribute to the card issuer's profits by paying interest on the overdue balance, or a high-risk one, who will not pay back the debt. The issuer can then customize its policies to deal with these different consumer types. This article develops a dynamic model for debt repayment behavior of new customers in the credit card market that makes it possible to differentiate between low-risk, delinquent customers and high-risk customers. The authors apply the model to a data set of new consumers' monthly spending and repayment records.

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“…Su and Rao (2007) studied the evolution of willingness to pay for different types of attributes and how such changes affect new product adoption. Many of the flexible models developed to capture dynamics in repeated choice (e.g., Kim, Menzefricke and Feinberg 2005;Lachaab, Ansari, Jedidi and Trabelsi 2006) could be applied to preference measurement. Bradlow, Hu and Ho (2004) take a first step in understanding the antecedents of dynamics by studying consumer learning about preferences for missing attribute levels in a partial profile design.…”

Section: Modeling Learning Dynamics and Preference Formationmentioning

confidence: 99%