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

Assessing Order Effects in Online Community-based Health Forums

Measuring the quality of health content in online health forums is a challenging task. The majority of the existing measures are based on evaluations of forum users and may not be reliable. We employed machine learning techniques, text mining methods, and Big Data platforms to construct four measures of textual quality to automatically determine the similarity of a given answer to professional answers. We then used them to assess the quality of 66,888 answers posted on Yahoo! Answers Health section. All four measures of textual quality revealed a higher quality for asker-selected best answers indicating that askers, to some extent, have a proper judgment to select the best answers. We also studied the presence of order effects in online health forums. Our results suggest that the textual quality of the first answer positively influences the mean textual quality of the subsequent answers and negatively influences the quantity of subsequent answers

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications

Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda

Although social media use is gaining increasing importance as a component of firms’ portfolio of strategies, scant research has systematically consolidated and extended knowledge on social media marketing strategies (SMMSs). To fill this research gap, we first define SMMS, using social media and marketing strategy dimensions. This is followed by a conceptualization of the developmental process of SMMSs, which comprises four major components, namely drivers, inputs, throughputs, and outputs. Next, we propose a taxonomy that classifies SMMSs into four types according to their strategic maturity level: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. We subsequently validate this taxonomy of SMMSs using information derived from prior empirical studies, as well with data collected from in-depth interviews and a quantitive survey among social media marketing managers. Finally, we suggest fruitful directions for future research based on input received from scholars specializing in the field