A Bivariate Timing Model of Customer Acquisition and Retention

Two widely recognized components, central to the calculation of customer value, are acquisition and retention propensities. However, while extant research has incorporated such components into different types of models, limited work has investigated the kinds of associations that may exist between them. In this research, we focus on the relationship between a prospective customer\u27s time until acquisition of a particular service and the subsequent duration for which he retains it, and examine the implications of this relationship on the value of prospects and customers. To accomplish these tasks, we use a bivariate timing model to capture the relationship between acquisition and retention. Using a split-hazard model, we link the acquisition and retention processes in two distinct yet complementary ways. First, we use the Sarmonov family of bivariate distributions to allow for correlations in the observed acquisition and retention times within a customer; next, we allow for differences across customers using latent classes for the parameters that govern the two processes. We then demonstrate how the proposed methodology can be used to calculate the discounted expected value of a subscription based on the time of acquisition, and discuss possible applications of the modeling framework to problems such as customer targeting and resource allocation

Modeling Customer Lifetimes with Multiple Causes of Churn

Customer retention and customer churn are key metrics of interest to marketers, but little attention has been placed on linking the different reasons for which customers churn to their value to a contractual service provider. In this paper, we put forth a hierarchical competing-risk model to jointly model when customers choose to terminate their service and why. Some of these reasons for churn can be influenced by the firm (e.g., service problems or price–value trade-offs), but others are uncontrollable (e.g., customer relocation and death). Using this framework, we demonstrate that the impact of a firm's efforts to reduce customer churn for controllable reasons is mitigated by the prevalence of uncontrollable ones, resulting in a “damper effect” on the return from a firm's retention marketing efforts. We use data from a provider of land-based telecommunication services to demonstrate how the competing-risk model can be used to derive a measure of the incremental customer value that a firm can expect to accrue through its efforts to delay churn, taking this damper effect into account. In addition to varying across customers based on geodemographic information, the magnitude of the damper effect depends on a customer's tenure to date. We discuss how our framework can be used to tailor the firm's retention strategy to individual customers, both in terms of which customers to target and when retention efforts should be deployed

Social TV, Advertising, and Sales: Are Social Shows Good for Advertisers?

Television viewers are increasingly engaging in media-multitasking while watching programming. One prevalent multiscreen activity is the simultaneous consumption of television alongside social media chatter about the programming, an activity referred to as “social TV.” Although online interactions with programming can result in a more engaged and committed audience, social TV activities may distract media multitaskers from advertisements. These competing outcomes of social TV raise the question: are programs with high online social TV activity, so called “social shows,” good for advertisers? In this research, we empirically examine this question by exploring the relationship among television advertising, social TV, online traffic, and online sales. Specifically, we investigate how the volume of program-related online chatter is related to online shopping behavior at retailers that advertise during the programs. We find that advertisements that air in programs with more social TV activity see increased ad responsiveness in terms of subsequent online shopping behavior. This result varies with the mood of the advertisement, with more affective advertisements—in particular, funny and emotional advertisements—seeing the largest increases in online shopping activity. Our results shed light on how advertisers can encourage online shopping activity on their websites in the age of multiscreen consumers

Incorporating direct marketing activity into latent attrition models

When defection is unobserved, latent attrition models provide useful insights about customer behavior and accurate forecasts of customer value. Yet extant models ignore direct marketing efforts. Response models incorporate the effects of direct marketing, but because they ignore latent attrition, they may lead firms to waste resources on inactive customers. We propose a parsimonious model that allows direct marketing to impact three relevant behaviors in latent attrition models—the frequency with which customers conduct transactions, the size of the transactions, and the duration for which customers remain active. Our model also accounts for how the organization targets its direct marketing across individuals and over time. Using donation data from a nonprofit organization, we find that direct marketing increases donation incidence for active donors. However, our analysis also shows that direct marketing has the potential to shorten the length of a donor's relationship. We find that our proposed model offers superior predictive performance compared with models that ignore the impact of direct marketing activity or latent attrition. We demonstrate the managerial applicability of our modeling approach by estimating the impact of direct marketing on donation behavior and identifying those donors most likely to conduct transactions in the future

Social TV: How Social Media Activity Interacts With TV Advertising

Social TV is the simultaneous consumption of television alongside social media chatter about the programming. This topic is highly relevant for marketers. Usually it is considered as a bad thing for TV advertisers. While there can be distraction from the ads, marketers can also benefit from positive effects. Consumers’ multiscreen activities can be used to attract more viewers, to leverage TV campaigns and to increase sales. This chatter creates free exposure for the brand online, extends the reach of television ad campaigns to the online space, and offers real-time feedback to advertisers on how their ads are being received. To take advantage of social TV, marketers need to develop a social media and ad design strategy for TV shows. Not every “social show” is good for them. Many programs receive a high volume of program-related chatter at the expense of advertiser-related word-of-mouth, but some programs generate high levels of online conversations that can also benefit their advertisers. Marketers are well served to identify those programs that are conducive to advertiser-related chatter. Also, specific ad designs can further encourage buzz

Social TV: How Social Media Activity Interacts With TV Advertising

Social TV is the simultaneous consumption of television alongside social media chatter about the programming. This topic is highly relevant for marketers. Usually it is considered as a bad thing for TV advertisers. While there can be distraction from the ads, marketers can also benefit from positive effects. Consumers’ multiscreen activities can be used to attract more viewers, to leverage TV campaigns and to increase sales. This chatter creates free exposure for the brand online, extends the reach of television ad campaigns to the online space, and offers real-time feedback to advertisers on how their ads are being received. To take advantage of social TV, marketers need to develop a social media and ad design strategy for TV shows. Not every “social show” is good for them. Many programs receive a high volume of program-related chatter at the expense of advertiser-related word-of-mouth, but some programs generate high levels of online conversations that can also benefit their advertisers. Marketers are well served to identify those programs that are conducive to advertiser-related chatter. Also, specific ad designs can further encourage buzz