Marketing Actions and the Value of Customer Assets: A Framework for Customer Asset Management

This article develops a framework for assessing how marketing actions affect customers’lifetime value to the firm. The framework is organized around four critical actions that firms must take to effectively manage the asset value of the customer base: database creation, market segmentation, forecasting customer purchase behavior, and resource allocation. In this framework, customer lifetime value is treated as a dynamic construct, that is, it influences the eventual allocation of marketing resources but is also influenced by that allocation. By viewing customers as assets and systematically managing these assets, a firm can identify the most appropriate marketing actions to acquire, maintain, and enhance customer assets and thereby maximize financial returns. The article discusses in detail how to assess customer lifetime value and manage customers as assets. Then, it identifies key research challenges in studying customer asset management and the managerial challenges associated with implementing effective customer asset management practices.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction

Many service organizations have embraced relationship marketing with its focus on maximizing customer lifetime value. Recently, there has been considerable controversy about whether there is a link between customer satisfaction and retention. This research question is important to researchers who are attempting to understand how customers' assessments of services influence their subsequent behavior. However, it is equally vital to managers who require a better understanding of the relationship between satisfaction and the duration of the provider-customer relationship to identify specific actions that can increase retention and profitability in the long run. Since there is very little empirical evidence regarding this research question, this study develops and estimates a dynamic model of the duration of provider-customer relationship that focuses on the role of customer satisfaction. This article models the duration of the customer's relationship with an organization that delivers a continuously provided service, such as utilities, financial services, and telecommunications. In the model, the duration of the provider-customer relationship is postulated to depend on the customer's subjective expected value of the relationship, which he/she updates according to an anchoring and adjustment process. It is hypothesized that cumulative satisfaction serves as an anchor that is updated with new information obtained during service experiences. The model is estimated as a left-truncated, proportional hazards regression with cross-sectional and time series data describing cellular customers perceptions and behavior over a 22-month period. The results indicate that customer satisfaction ratings elicited prior to any decision to cancel or stay loyal to the provider are positively related to the duration of the relationship. The strength of the relationship between duration times and satisfaction levels depends on the length of customers' prior experience with the organization. Customers who have many months' experience with the organization weigh prior cumulative satisfaction more heavily and new information (relatively) less heavily. The duration of the service provider-customer relationship also depends on whether customers experienced service transactions or failures. The effects of perceived losses arising from transactions or service failures on duration times are directly weighed by prior satisfaction, creating contrast and assimilation effects. How can service organizations develop longer relationships with customers? Since customers weigh prior cumulative satisfaction heavily, organizations should focus on customers in the early stages of the relationship—if customers' experiences are not satisfactory, the relationship is likely to be very short. There is considerable heterogeneity across customers because some customers have a higher utility for the service than others. However, certain types of service encounters are potential relationship “landmines” because customers are highly sensitive to the costs/losses arising from interactions with service organizations and insensitive to the benefits/gains. Thus, incidence and quality of service encounters can be early indicators of whether an organization's relationship with a customer is flourishing or in jeopardy. Unfortunately, organizations with good prior service levels will suffer more when customers perceive that they have suffered a loss arising from a service encounter—due to the existence of contrast effects. However, experienced customers are less sensitive to such losses because they tend to weigh prior satisfaction levels heavily. By modeling the duration of the provider-customer relationship, it is possible to predict the revenue impact of service improvements in the same manner as other resource allocation decisions. The calculations in this article show that changes in customer satisfaction can have important financial implications for the organization because lifetime revenues from an individual customer depend on the duration of his/her relationship, as well as the dollar amount of his/her purchases across billing cycles. Satisfaction levels explain a substantial portion of explained variance in the durations of service provider-customer relationships across customers, comparable to the effect of price. Consequently, it is a popular misconception that organizations that focus on customer satisfaction are failing to manage customer retention. Rather, this article suggests that service organizations should be proactive and learn from customers they defect by understanding their current satisfaction levels. Managers and researchers may have underestimated the importance of the link between customer satisfaction and retention because the relationship between satisfaction and duration times is very complex and difficult to detect without advanced statistical techniques.Customer Satisfaction, Durations, Retention, Defensive Strategy, Proportional Hazards Model

Optimal Pricing of New Subscription Services: Analysis of a Market Experiment

There are now available a number of new subscription services that comprise a dual pricing system of a monthly access fee (rental) and a per-minute usage charge. Examples include cellular phones, the Internet, and pay TV. The usage and retention of such services depend on the absolute and relative prices of this dual system. For instance, a moderate access fee but a low-usage charge might initially appeal to customers, but later a low-usage customer might find the monthly fee unjustified and thereby relinquish the service. Providers of such services, therefore, usually offer several pricing packages to cater to differing customer needs. The purpose of this study is to derive a revenue-maximizing strategy for subscription services, that is, the combination of access and usage price that maximizes revenue over a specified time period. An additional objective is to determine access and usage price elasticities because they have historically played an important role in theoretical pricing models. The application area is the cellular phone market, but for a new rather than an existing product. To help gauge the likely usage rates and customer retention, a field experiment is conducted in which several alternative price combinations are used. Specifically, a sample of potential residential customers (most of whom did not have an existing cell phone) were divided into four treatment groups. The first group were not charged an access fee but did have to pay a small per-minute usage charge. The second group also paid a small usage charge but in addition had three access price increases over the duration of the trial. The third group paid no access fee but had usage charge increases, while the fourth group had both access fee and usage charge increases. Usage levels for each respondent are recorded, as is their month of dropout if they discontinue the service. An initial examination of the data shows that higher access fees result in higher customer attrition, and higher usage cost results in lower usage. Furthermore, usage and retention are related in that declining usage levels over time often signal impending customer attrition. Hence, two phenomena need to be modeled: usage of the service and customer retention conditional on usage. Some seasonal effects are also observed and are allowed for in the model. Modeling customer attrition simultaneously with usage is important because ignoring customer attrition will likely result in an underestimate of price sensitivity. This results from a censoring effect, whereby respondents who remain in the trial tend to be wealthier, and hence, less price sensitive. Given the known problems of ignoring customer attrition, we develop a theoretical model of usage, which explicitly incorporates attrition by extending a time-series model introduced by Hausman and Wise (1979). We make two extensions of the Hausman and Wise model. The first is to generalize it from two to many time periods and the second is to allow for respondent heterogeneity by incorporating latent classes. We fit the model by maximum likelihood and find that a two-segment model is best. In addition, we examine the predictive validity of our model and find it to be reasonably good. In general, the results show that access and usage prices have different relative effects on demand and retention. There are five key results. First, access price has some effect on usage but a much stronger effect on retention. Second, usage price has a strong effect on usage and a moderate effect on retention, in that if usage price increases so much that usage declines, then lower usage levels results in higher attrition. Third, access price elasticity is about half that of usage price, with both elasticities generally being much smaller than 1, indicating relative inelasticity for this particular service. Fourth, customer attrition rate (churn) is much more sensitive to access than usage price and, last, if just observed usage is examined and customer attrition is ignored, then price sensitivity is very likely to be substantially underestimated (on the order of 45% in our case). Finally, when developing the revenue-maximizing price combination we allow for the cost of customer acquisition by using some typical advertising-to-sales ratios for the telecommunications industry. We find that the revenue maximizing price is $27.70 per month for the access fee and$0.81 per minute for the airtime charge. These values are in line with current access fees and usage costs in the given market.Attrition, Field Experiment, Pricing, Price Elasticity, Subscription, Telecommunications