The CRM : Choice Models and Customer Relationship Management

Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers’ and vendors’ needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm’s products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplace:

• A shift in focus in many organizations, towards increasing the share of requirements among their current customers rather than fighting for new customers.
• An explosion in data acquired about customers, through the integration of internal databases and acquisition of external syndicated data.
• Computing power is increasing exponentially.
• Software and tools are being developed to exploit these data and computers, bringing the analytical tools to th edecision maker, rather than restricting their access to analysts.

In spite of this growth in marketing practice, CRM research in academia remains nascent. This paper provides a framework for CRM research and describes recent advances as well as key research opportunities. See http://faculty.fuqua.duke.edu/∼mela for a more complete version of this paper.
Keywords: customer relationship management, direct marketing

Introduction
What is CRM?
Analytical customer relationship management (CRM) is the process of collecting and analyzing a firm’s information regarding customer interactions in order to enhance the customers’ values to the firm. Firms exploit such information by designing strategies uniquely targeted to consumer needs. This process enhances loyalty and increases switching costs, as information on consumer preferences affords an enduring competitive advantage. $By integrating various data (e.g. across purchases, operations, service logs, etc.), choice re-searchers can obtain a more complete view of customer behavior. These developments cut across industries, including banking, telephony, Internet, and other areas that have received limited attention in the marketing literature. In addition, each industry likely has unique challenges of its own.

We differentiate between analytical CRM, which is the focus of this paper, and behavioral CRM. Analytical CRM involves using firms’ data on its customers to design longitudinal models of choice over the breadth of the firm’s products and using them prescriptively to increase the revenues from customers over their lifecycle. In contrast, behavioral CRM uses experiments and surveys to focus upon the psychological underpinnings of the service interaction, or the managerial structures that make CRM effective.

A focus on CRM is warranted given the explosive growth of the analytical CRM applications in industry (Market Research.com forecasts analytical CRM revenue to increase from $2.4 billion in 2003, to $43 billion in 2007). Technological enhancement in information technology and increased addressability of customers via new channels has fueled this growth. Thus, it is surprising there are only few papers that seek to assess the state of research in this area, or outline the challenges unique to this area. This paper seeks to address this void.
What Novel Implications of CRM Exist for Choice Modeling?
Choice decisions in the context of CRM include firm choices (whom to target, when and with what) and customer choices (whether, what, when and where to buy). Several aspects make CRM a novel and potentially fruitful domain of inquiry for choice researchers:

* CRM applications typically involve massive amounts of data. These include many observations and many variables.
• CRM applications often involve an inward looking view of the customer, as competitive information is often impossible to obtain.
• Analytical CRM is typically dynamic, as trade-offs in current programs are made against future revenues. In conjunction with large data, this implies that new optimization techniques are needed to cope with such problems.
• Low response rates are often the norm, calling for more flexible response models than the popular logistic regression model.
• Unlike many scanner-panel applications, customers are addressable (Blattberg and Deighton, 1991). As a result, it is common to run large field experiments in these settings and control the sampling approach, affording a greater degree of control over the choice task. Addressability also implies it is easier to target consumers.
A Framework for CRM Research
CRM research can be organized along the customer lifecycle, including customer acquisition, development and retention strategies. Customer acquisition extends from the channels customers use to first access the firm (Ansari et al., 2004) to the promotions that bring them to a firm. The value of a customer can also be enhanced by the firm through appropriate development strategies such as delivering customized products (Ansari and Mela, 2003) and cross-selling (Kamakura et al., 1991, 2003). Finally, early detection and prevention of customer attrition can also enhance the total lifetime of the customer base, if efforts are focused on the retention of valuable customers.

The customer lifecycle implies that each customer has a value over his or her tenure with a firm. Estimating the lifetime value of a customer by itself requires sophisticated modeling, as it involves predictions of both revenues and retention probabilities. Several approaches exist to measure customer lifetime value (CLV). The relative merits of these different approaches is considered in Jain and Singh (2002) and Venkatesan and Kumar (2003). However, scant evidence exists regarding the accuracy of CLV predictions. There may be considerable room for improvement based on criteria such as out of sample validation, especially at the individual level.

The Pareto/NBD model of lifetime value may offer promise in the forecasting of lifetime value (Reinartz and Kumar, 2003), and recent advances by Fader et al. (2004) mitigate the computational burden of this model. Incorporation of covariates using a proportional hazards model and the addition of discounting to the Pareto/NBD could enrich this literature. Other fruitful areas for inquiry include the role of network effects in lifetime value, the effect of time aggregation and aggregation across a household, the role of predictors other than past purchases on lifetime value (such as inbound contacts or marketing), and a better accounting of costs.

An idea closely related to customer lifetime value is consumer lifetime value (Du and Kamakura, 2005). The distinction pertains to the perspective of the decision maker (firm vs. consumer), scope of information, and the approaches used to compute the value of a customer. Customer lifetime value is typically an inward-looking view of the consumer predicated on firms’ internal records for the purpose of determining the value of the customer to the firm. In contrast, consumer lifetime value encompasses all behaviors of a consumer across multiple or competing firms and assumes the perspective of a consumer making inter-temporal choices over categories and time so as to maximize his or her utility.

Consumer lifetime value models, which often combine internal and syndicated data, are generally applicable in industries where customer tenures are long, needs change over time and can be linked to life stages, and consumers trade-off future for current utility (such as savings and consumption). Ideally, consumer lifetime value models can be linked with internal firm records to obtain a better sense of which consumers to target. Consumers with a low share of wallet but a high consumer lifetime value may be especially attractive to a firm.

Our discussion of the state of CRM research proceeds using the customer lifecycle framework (acquisition, development and retention), and we shall describe the issues and methodological challenges unique to each stage.
Acquisition
Issues
The objective of acquisition strategies is to obtain more and profitable customers. For example, new home buyers are targeted for home insurance. In spite of its importance, identifying potential customers for acquisition is an area of scant attention. In general, acquisitions are profitable if the expected value of attaining the customer (over the lifetime) exceeds the cost (Blattberg et al., 2001). However, forecasts of likely response are predicated upon past response, and subject to regression to the mean if based on selection from such past response. Deeper analysis of appropriate probabilistic thresholds for mailing could yield significant advances in this area.

Customer acquisition occurs across an array of channels (e.g., direct television, direct mail, Internet, telemarketing, etc.) and researchers have begun to assess the efficacy of channel acquisition strategies and their effect on subsequent behaviors (Bolton et al., 2004; Verhoef and Donkers, 2005; Thomas, 2001). For example, Bolton et al. (2004) argue that customers acquired through channels with a price emphasis tend to be less loyal. A related issue pertains to referral programs, and there has been little analytical research on the efficacy and design of these programs.

Classic behavioral models of consumer adoption (need recognition followed by information search, purchase, and post-purchase service encounters) are useful in comprehending the effects of multi-channel acquisition strategies, as some channels are likely better for information search, while others are better for service or purchase. Thus, acquisition in a multi-channel environment should consider the interaction between these channels (Blattberg et al., 2004).

WAGNER KAMAKURA
kamakura@duke.edu
CARL F. MELA