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Firms spend substantial resources on marketing and selling. Interpreting this as evidence of frictions in product markets, which require firms to spend resources on customer acquisition, this article develops a search theoretic model of firm dynamics in frictional product markets. Introducing search frictions generates long-term customer relationships, rendering the customer base a state variable for firms, which is sluggish to adjust. This affects: the level and volatility of firm investment, profits, value, sales and markups, the timing of firm responses to shocks, and the relationship between investment and Tobin's q. We document support for these predictions in firm-level data from Compustat, using cross-industry variation in selling expenses to quantify differences in the degree of friction across markets.

This study investigates whether and how customer-base concentration affects supplier firm fundamentals and stock market valuation. I compile a comprehensive sample of supply chain relationships and develop a measure (CC) to capture the extent to which a supplier's customer base is concentrated. In contrast to the conventional view of customer-base concentration as an impediment to supplier firm performance, I document a positive contemporaneous association between CC and accounting rates of return, suggesting that efficiencies accrue to suppliers with concentrated customer bases. Consistent with a cause-and-effect link between customer-base concentration and supplier firm performance, analysis of intertemporal changes demonstrates that CC increases predict efficiency gains in the form of reduced operating expenses per dollar of sales and enhanced asset utilization. Using stock returns tests, I find that investors underreact to the implications of changes in customer-base concentration for future firm fundamentals when setting stock prices. A trading strategy that exploits investors' underreaction yields abnormal stock returns over the 30-year period studied.

Recently, academics have shown interest and enthusiasm in the development and implementation of stochastic customer base analysis models, such as the Pareto/NBD model and the BG/NBD model. Using the information these models provide, customer managers should be able to (1) distinguish active customers from inactive customers, (2) generate transaction forecasts for individual customers and determine future best customers, and (3) predict the purchase volume of the entire customer base. However, there is also a growing frustration among academics insofar as these models have not found their way into wide managerial application. To present arguments in favor of or against the use of these models in practice, the authors compare the quality of these models when applied to managerial decision making with the simple heuristics that firms typically use. The authors find that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for predictions regarding future purchases at the overall customer base level. The authors conclude that in their current state, stochastic customer base analysis models should be implemented in managerial practice with much care. Furthermore, they identify areas for improvement to make these models managerially more useful.

Many businesses track repeat transactions on a discrete-time basis. These include (1) companies for whom transactions can only occur at fixed regular intervals, (2) firms that frequently associate transactions with specific events (e.g., a charity that records whether supporters respond to a particular appeal), and (3) organizations that choose to utilize discrete reporting periods even though the transactions can occur at any time. Furthermore, many of these businesses operate in a noncontractual setting, so they have a difficult time differentiating between those customers who have ended their relationship with the firm versus those who are in the midst of a long hiatus between transactions. We develop a model to predict future purchasing patterns for a customer base that can be described by these structural characteristics. Our beta-geometric/beta-Bernoulli (BG/BB) model captures both of the underlying behavioral processes (i.e., customers' purchasing while \"alive\" and time until each customer permanently \"dies\"). The model is easy to implement in a standard spreadsheet environment and yields relatively simple closed-form expressions for the expected number of future transactions conditional on past observed behavior (and other quantities of managerial interest). We apply this discrete-time analog of the well-known Pareto/NBD model to a data set on donations made by the supporters of a nonprofit organization located in the midwestern United States. Our analysis demonstrates the excellent ability of the BG/BB model to describe and predict the future behavior of a customer base.

Customer base analysis is a key element in customer valuation and can provide guidance for decisions such as resource allocation. Yet extant models often focus on a single activity, such as purchases from a retailer or donations to a nonprofit organization. These models do not consider other ways that an individual may engage with an organization, such as purchasing in multiple brands or contributing user-generated content. In this research, we propose a framework to generalize extant models for customer base analysis to multiple activities. Using the data from a website that allows users to purchase digital content and/or post digital content at no charge, we develop a flexible \"buy 'til you die\" model to empirically examine how the two activities are related. Compared with benchmarks, our model more accurately forecasts the future behavior for both types of activities. In addition to finding evidence of coincidence between the activities while customers are \"alive,\" we find that the latent attrition processes are related. This suggests that conducting one type of activity is informative of whether customers are still alive to conduct another type of activity and, consequently, affects inferences of customer value.

Firms interact with other parties in their supply chains to access the resources necessary to operate efficiently and effectively. While much of the extant research focuses on dyadic relationships between two firms, another unit of analysis that can be utilized to augment the current research in supply chain management is the portfolio (Tokman et al. 2007). Firms engage in a diverse set of relationships at any given point in time, and these sets of relationships can be conceptualized as portfolios, defined as the set of direct interfirm relationships for the focal firm. Portfolios provide a useful unit of analysis for research because a firm must also have a vision for how to manage its portfolio in support of value creation and appropriation, in addition to understanding how individual relationships provide access to resources and help the organization adapt to the changing environment. In this research, we utilize a systematic literature review to explore supply chain portfolio research from a variety of academic fields. Our findings include multiple themes with conflicting results in the extant literature. We then utilize the findings to motivate directions for future research.

We investigate the increasingly common business setting in which companies face the possibility of both observed and unobserved customer attrition (i.e., “overt” and “silent” churn) in the same pool of customers. This is the case for many online-based services where customers have the choice to stop interacting with the firm either by formally terminating the relationship (e.g., canceling their account) or by simply ignoring all communications coming from the firm. The standard contractual versus noncontractual categorization of customer–firm relationships does not apply in such hybrid settings, which means the standard models for analyzing customer attrition do not apply. We propose a hidden Markov model (HMM)-based framework to capture silent and overt churn. We apply our modeling framework to two different contexts—a daily deal website and a performing arts organization. In contrast to previous studies that have not separated the two types of churn, we find that overt churners in these hybrid settings tend to interact more, rather than less, with the firm prior to churning; that is, in settings where both types of churn are present, a high level of activity—such as customers actively opening emails received from the firm—is not necessarily a good indicator of future engagement; rather it is associated with higher risk of overt churn. We also identify a large number of “silent churners” in both empirical applications—customers who disengage with the company very early on, rarely exhibit any type of activity, and almost never churn overtly. Furthermore, we show how the two types of churners respond very differently to the firm’s communications, implying that a common retention strategy for proactive churn management is not appropriate in these hybrid settings. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1057 .

PurposeThe purpose of this study is to show that the use of CAM (cognitive analytics management) methodology is a valid tool to describe new technology implementations for businesses.Design/methodology/approachStarting from a dataset of recipes, we were able to describe consumers through a variant of the RFM (recency, frequency and monetary value) model. It has been possible to categorize the customers into clusters and to measure their profitability thanks to the customer lifetime value (CLV).FindingsAfter comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant of the RFM model.Research limitations/implicationsThe results of this methodology are strictly applicable to the retailer which provided the data.Practical implicationsEven though, this methodology can produce useful information for designing promotional strategies and improving the relationship between company and customers.Social implicationsCustomer segmentation is an essential part of the marketing process. Improving further segmentation methods allow even small and medium companies to effectively target customers to better deliver to society the value they offer.Originality/valueThis paper shows the application of CAM methodology to guide the implementation and the adoption of a new customer segmentation algorithm based on the CLV.

Whereas recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual's decision to contribute these opinions. In this research, we empirically model the individual's decision to provide a product rating and investigate factors that influence this decision. Specifically, we consider how previously posted ratings may affect an individual's posting behavior in terms of whether to contribute (incidence) and what to contribute (evaluation), and we identify selection effects that influence the incidence decision and adjustment effects that influence the evaluation decision. Across individuals, our results show that positive ratings environments increase posting incidence, whereas negative ratings environments discourage posting. Our results also indicate important differences across individuals in how they respond to previously posted ratings, with less frequent posters exhibiting bandwagon behavior and more active customers revealing differentiation behavior. These dynamics affect the evolution of online product opinions. Through simulations, we illustrate how the evolution of posted product opinions is shaped by the underlying customer base and show that customer bases with the same median opinion may evolve in substantially different ways because of the presence of a core group of \"activists\" posting increasingly negative opinions.

Online platforms have become an important channel for sellers to reach potential customers. Although small sellers rely on platforms as their main market channel, large sellers who have direct channels and have loyal customer bases, may not have an incentive to sell on online platforms. We develop a game-theoretic model with an online platform and two sellers that differ in size and sell substitutable products. Comparing equilibria under cooperation and non-cooperation cases, we found that high-value products might be sold at a lower price under non-cooperation, but cooperation between the large seller and the platform can correct this product value-price mismatch. More importantly, the platform’s and large seller’s loyal customer bases and the platform’s commission rate influence the unilateral or joint (with transfer payment) incentive to cooperate. The large seller’s incentive to cooperate can be explained by expanded market coverage or reduced price competition. As its direct channel advantage increases, the large seller’s incentive to cooperate first diminishes, driven by the cannibalization effect resulting from price consistency, but is then enhanced due to the market expansion effect. However, if the smaller seller responds aggressively, both the platform and the large seller may lose their incentives to cooperate as the platform’s loyal customer segment expands. Cooperation, however, can reduce price competition when the commission rate is moderate and the platform’s loyal customer segment is small, thus leading to a win-win-win outcome, benefiting the platform as well as the two sellers.