The authors evaluate the usefulness of customer lifetime value (CLV) as a metric for customer selection and marketing resource allocation by developing a dynamic framework that enables managers to maintain or improve customer relationships proactively through marketing contacts across various channels and to maximize CLV simultaneously. The authors show that marketing contacts across various channels influence CLV nonlinearly. Customers who are selected on the basis of their lifetime value provide higher profits in future periods than do customers selected on the basis of several other customer-based metrics. The analyses suggest that there is potential for improved profits when managers design resource allocation rules that maximize CLV. Managers can use the authors’ framework to allocate marketing resources efficiently across customers and channels of communication.
By: Rajkumar Venkatesan & V. Kumar
Published in Journal of Marketing, Vol. 68, October 2004
Customer lifetime value (CLV) is rapidly gaining acceptance as a metric to acquire, grow, and retain the “right” customers in customer relationship management (CRM). However, many companies do not use CLV measurements judiciously. Either they work with undesirable customers to begin with, or they do not know how to customize the customer’s experience to create the highest value (Thompson 2001). The challenge that most marketing managers currently face is to achieve convergence between marketing actions (e.g., contacts across various channels) and CRM. Specifically, they need to take all the data they have collected about customers and integrate them with how the firm interacts with its customers. In the academic literature, Berger and colleagues (2002) support the allocation of resources to maximize the value of the customer base, and they strongly argue that such resource allocation models are needed.
Some researchers have recommended CLV as a metric for selecting customers and designing marketing programs (Reinartz and Kumar 2003; Rust, Zeithaml, and Lemon 2004). However, there is no empirical evidence as to the usefulness of CLV compared with that of other customerbased metrics. Table 1 compares the CLV framework proposed in this study with the existing literature on CLV and database marketing. A comparison of the studies listed in Table 1 shows that most of the previous studies provide guidelines for calculating CLV and return on investment at the aggregate level. Some recent studies (Reinartz and Kumar 2003; Rust, Zeithaml, and Lemon 2004) provide empirical evidence for the existence of a relationship between marketing actions and CLV at the aggregate level. However, as Berger and colleagues (2002) state, none of the studies has proposed and tested a framework that provides rules for resource allocation across various channels of communication for each individual customer and across customers.
On the basis of the comparisons in Table 1, we summarize the significant contributions of our study as follows: We provide a framework for measuring CLV that links the influence of communications across various channels on CLV. We also evaluate the usefulness of CLV as a metric for customer selection and develop a framework for marketing resource allocation that maximizes CLV. Given the assumed link between CLV and firm profitability (Hogan et al. 2002), these are important issues.
In this study, we use customer data from a large business-to-business (B2B) manufacturer to illustrate the proposed framework empirically. The customer database of the organization focuses on B2B customers. Our analyses show that marketing communications across various channels influence CLV nonlinearly. The results from our analyses suggest that customers selected on the basis of CLV provide higher profits than do customers selected on the basis of other widely used CRM metrics. In addition, there is the potential for substantial improvement in profits when managers design resource allocation rules that maximize CLV. In the next section, we develop the framework for the measurement and maximization of CLV. We then propose hypotheses about the influence of supplier-specific factors and customer characteristics on the various CLV components. In the subsequent section, we explain the models and data we used to estimate CLV. We then discuss the results from our analyses and explain the comparison of CLV with other metrics for customer selection. Specifically, we compare the aggregate profits provided by high-CLV customers with those of customers who score high on several other customer-based metrics. In the section “Resource Allocation Strategy,” we provide details on allocating resources that maximize CLV. Our objective there is to evaluate the extent to which CLV, and thus profits, can be increased by allocating marketing resources across channels of contact for each customer so as to maximize his or her respective CLVs. Finally, we derive implications based on the results, discuss the limitations of our study, and identify areas for further research.
The various components of CLV include purchase frequency, contribution margin, and marketing costs (however, the various CLV components can vary depending on the industry). Some of the antecedents of purchase frequency and contribution margin (e.g., marketing communications) are under management’s control and affect the variable costs of managing customers. We use these antecedents to maximize CLV.
Typically, CLV is a function of the predicted contribution margin, the propensity for a customer to continue in a relationship (customer retention), and the marketing resources allocated to the customer. In general, CLV can be calculated as follows:
In contractual settings, managers are interested in predicting customer retention, or the likelihood of a customer staying in or terminating a relationship. However, in noncontractual settings, the focus is more on predicting future customer activity because there is always a chance that the customer will purchase in the future. Therefore, managers who calculate CLV in noncontractual settings are interested in predicting future customer activity and the predicted contribution margin from each customer. Previous researchers have used the variable P(Alive), which represents the probability that a customer is alive (and thus exhibits purchase activity) given his or her previous purchase behavior (Reinartz and Kumar 2000), to predict future customer activity in noncontractual settings. However, the measure assumes that when a customer terminates a relationship, he or she does not return to the supplier. This is also called the “lost-for-good” scenario (Rust, Zeithaml, and Lemon 2004). If a customer is won back after termination, the company treats the customer as a new customer and ignores its history with the customer.
Another method for predicting future customer activity is to predict the frequency of a customer’s purchases given his or her previous purchases. The assumption underlying this framework is that customers are most likely to reduce their frequency of purchase before terminating a relationship. This assumption is in accordance with theories about the different phases in a relationship and relationship life cycles (Dwyer, Schurr, and Oh 1987; Jap 2001). In addition, such a methodology enables a customer to return to the supplier after a temporary dormancy in a relationship. Thus, in this framework, we measure CLV by predicting the purchase pattern (purchase frequency or interpurchase times) over a reasonable period. This is also called the “always-ashare” scenario. The lost-for-good approach is questionable because it systematically understates CLV (Rust, Zeithaml, and Lemon 2004). Thus, we use the always-a-share approach in this study. Given predictions of contribution margin, purchase frequency, and variable costs, the CLV function we use can be represented as follows:
In addition to accurate measurement of CLV for each customer, our objective is to allocate resources so as to maximize CLV. Thus, we model the purchase frequency and contribution margin of customers as a function of marketing resource variables such as channel contact. We then use the customer responsiveness to marketing actions, obtained from the purchase frequency and contribution margin models, to develop resource allocation strategies that maximize CLV. In summary, the objective is to identify the resource allocation rules across various channels of communication for each individual customer such that the respective CLVs (as provided in Equation 2) are maximized.1 Our objective function is subject to the following constraints: frequency > 0 ∀ i, t, and xi,m,l ≥ 0 ∀ i, m, l.
Discounting contribution margin. We first focus on the discounting of contribution margin over a period of time. Assume that it is currently year l = 1 and that we need to forecast the contribution margin from each customer for the next n years (i.e., until l + n). It is possible that a customer makes several purchases in a given year. Berger and Nasr (1998, Equation 2) and Rust, Zeithaml, and Lemon (2004) provide guidelines for discounting contribution margin from customers when there is more than one purchase occasion (y) per year. In this approach, the discount rate from a customer is scaled according to his or her frequency of purchase (as is shown in Equation 2). For example, consider when the planning horizon is one year and the frequency of purchases is two times (frequency = 2). The first purchase occasion (y = 1) occurs after 6 months; therefore, y/frequency = .5 (in other words, we use the square root of the discount rate). The second purchase occasion (y = 2) occurs after 12 months; therefore, y/frequency = 1.
Discounting cost allocations. The discounting of cost allocations is straightforward if we assume that there is a yearly allocation of resources (as is the case in most organizations) and that the cost allocation occurs at the beginning of the year (the present period). Thus, the cost allocation in the first year need not be discounted, the cost allocation in the second year needs to be discounted for one year, and so on. Thus, we raise the denominator in the cost function calculation to current year – 1 (i.e., l – 1).
Discussion of model constraints. The constraints ensure the nonnegativity of the predicted purchase frequency and communication levels for each customer i during period l.