Subscription-based service model, where profitability arises from sustained service relationships with consumers, has emerged as the dominant business paradigm across various industries. A notable characteristic of the growth in service markets is the indirect relationship between adoption and monetization. While adoption marks the initial stage of user engagement, monetization occurs gradually as users integrate the service into their routines over time. Consequently, the focus has shifted away from emphasizing adoption rates to prioritizing the total number of users. The difference between adopters and users is due to the fact that not all users integrate the service to their routine and some (or many) of them churn away from the service. The growth of adopters and users, and the ensuing monetary growth, are highly affected by churn, hence the critical issue we investigate in this paper is the valence and size effect of churn on adopters, users and revenues of the firm. We build on the service modeling approach of Libai, Muller, and Peres (2009) to first explore the impact of churn on dynamics of growth for new subscription services. We explain how churn affects key interest topics, such as the size and time to peak for adopters and users, the market potential of those who have not adopted yet, adopter categories, and conversion of users to money. We hope this work can motivate further explorations of this critical area for new product research in marketing. (c) 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Consumer spending inequality is a crucial input for customer management, and the Pareto 80/20 principle of consumer spending is widely accepted as expected regularity. However, research results are inconsistent, and the past focus on brand-level cross-sectional snapshots provides only a partial view of consumer spending inequality. Using 16 years of data (2009–2024) from the U.S. Consumer Expenditure Survey, we provide a first holistic view of category-level consumer expenditure inequality, complementing brand-level evidence and enhancing our understanding of the product-category markets in which brands operate. We find that, in a remarkably consistent pattern over the years, the top 20% of buying households are, on average, responsible for half of the category spending. Consumer packaged goods, which have often been used to study brand-level inequality, do not accurately represent the full spectrum of spending inequality. Consistent with work in economics, we explain why a 10/40 spending ratio may be a complementary measure for managers in this regard and demonstrate that what may appear to be low inequality in a Pareto-based framework still reflects a high level of consumer inequality. We discuss the implications of our results for the broader understanding of consumer inequality.

While older consumers are among the fastest-growing and most affluent demographic segments, they remain largely neglected in marketing practice and research, and firms often struggle to determine the right approach to them. This article highlights how mature influencers and content creators can bridge the gap between firms and older consumers by offering authenticity, credibility, and intergenerational appeal. It introduces a five-step framework for managers to plan influencer marketing campaigns with mature content creators, focusing on selecting the segment, platform, influencer, campaign, and success metrics.

As influencer marketing evolves into a dominant force in the marketing landscape, it necessitates a deeper theoretical exploration to understand its strategic implementations and impacts. This article examines the dynamics of influencer marketing within the growing creator economy, emphasizing the interactions among firms, influencers, followers, and digital platforms. We introduce a novel, equity-driven framework that analyzes how influencers contribute to customer equity, how influencers manage and leverage the value from their followers, and how platforms maximize the value from their users. We detail the complex relationships and value exchanges within the influencer marketing ecosystem, highlighting the challenges of measuring the return on investment and influencers’ strategic use of content to maintain authenticity and influence. By synthesizing diverse academic literature and current industry practices, this manuscript provides a comprehensive overview of the mechanisms of value creation and exchange in influencer marketing, offers strategic implications for marketers aiming to optimize their influencer engagements, and outlines future work in the form of the eleven “INFLUENCERS” research directions.

The overall churn rate is a critical measure for analysts, investors, and executives looking to assess a young venture’s product-market fit and overall brand health. Yet, the dynamics of overall churn are unclear for young brands. To clarify these dynamics, the authors demonstrate how heterogeneity in customer churn at the cohort level, customer acquisition growth, and cross-cohort differences come together to inform the overall churn rate. Analyzing a unique dataset of 25 young, subscription-based brands, the authors reveal that most brands’ overall churn rates tend to decrease significantly over time—even during periods of rapid customer acquisition growth, which increases the overall churn rate on the margin, and despite the fact that cohort-specific churn rates generally tend to increase over time. A simulation analysis supports these conclusions, illustrating the boundary conditions of the results and highlighting that they likely occur infrequently in real-world data. These results expand our understanding of churn in the context of young brands and highlight the perils of simplistically using the overall churn rate to evaluate underlying trends in customer and firm value.

One of the main challenges facing the mobile game industry is an alarming level of satiation, that is, a decline in user engagement and consequently in ad viewing, spending, and retention. Satiation lowers users’ CLV to an extent that renders acquisition from the likes of Facebook and Google untenable, driving game publishers to cross-promote, that is, sell and swap users among themselves. We model this cross-promotion as first, a screening mechanism, in that the fact of playing a game indicates specific preferences that might be suitable to an exchange with similar games; and second, as a resetting mechanism that enables the swapped users to reset their engagement in the new game, thus rendering the swap or sell beneficial to both buyer and seller. We show that there exists an optimal level of satiation with a game, and with this level, we show the conditions under which the game publisher cross promotes, and when it does, what the conditions are for selling rather than swapping. We extend the analysis to the case in which advertising costs and conversion rates are related; explain why they might be negatively correlated, and show that our main results still hold.

Marketers are adopting increasingly sophisticated ways to engage with customers throughout their journeys. We extend prior perspectives on the customer journey by introducing the role of digital signals that consumers emit throughout their activities. We argue that the ability to detect and act on consumer digital signals is a source of competitive advantage for firms. Technology enables firms to collect, interpret, and act on these signals to better manage the customer journey. While some consumers' desire for privacy can restrict the opportunities technology provides marketers, other consumers' desire for personalization can encourage the use of technology to inform marketing efforts. We posit that this difference in consumers' willingness to emit observable signals may hinge on the strength of their relationship with the firm. We next discuss factors that may shift consumer preferences and consequently affect the technology-enabled opportunities available to firms. We conclude with a research agenda that focuses on consumers, firms, and regulators.

Though the mobile app market is substantial and growing fast, most app providers struggle to monetize apps profitably. Monetizing apps is done in two ways: a) selling advertising space within a free version of the app, and b) selling a paid version, termed freemium or in-app purchase strategy. In this paper, we present a framework for monetization of mobile apps, using two central empirical regularities concerning the relationship between users and their mobile apps: a) Sampling: While consumers have some prior knowledge of their fit with the app, they remain uncertain regarding their exact utility until they are using it; and b) Satiation: The utility of using the app may decrease with time. While work on the monetization of digital goods has largely overlooked the role of satiation and the consequent retention issues, we show that in combination with uncertainty, it elucidates the role of the segments of consumers that download the free vs. paid version of the app, and how to balance these two segments so as to monetize mobile apps. We encounter two distinct scenarios: In the first, advertising drives most of the revenues; while in the second, revenues are driven by the paid version of the app. We explain how uncertainty and satiation affect the prevalence of the respective scenarios and impact the share of revenues from the paid vs free version of the app. We also demonstrate that an app provider can profit from offering a free version with ads even if advertisers are not paying for these ads. In other words, the app provider benefits from offering a “damaged good” version of the app that includes ads, even if this version is free to consumers, and the advertisers are not paying for the ads.

We advocate a dynamic view of influencers, considering them in a diffusion-like manner as products adopted over time by their followers. Social media influencers’ growth would be thus analogous to that of a new, continuously consumed product that grows and declines with followers’ acquisition and churn. We further argue that influencers’ growth could be depicted by an asymmetric bell-shaped curve in the number of followers, with a steeper rise than fall. It is consistent with a chain of effects that includes heterogeneity in churn at the follower cohort level, changes in follower duration over the influencer’s life cycle, and a decrease in follower churn over time. We observe these dynamics by examining the life cycle of influencers of a leading social trading platform, where individuals interact and follow others’ trading. Similar to the case of new products, recognizing the shape and dynamics of growth can be essential for predicting, launching, valuing, and managing influencers. Hence, the shift toward a dynamic view should be of much interest both to brands working with the fast-growing world of influencers and to the influencers themselves.

In light of the emerging discourse on AI systems' effect on society, whose perception swings widely between utopian and dystopian, we conduct herein a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM). To do so, we survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development, and retention. We highlight in particular how AI-CRM's improving ability to predict customer lifetime value will generate an inexorable rise in implementing adapted treatment of customers, leading to greater customer prioritization and service discrimination in markets. We further consider the consequences for firms and the challenges to regulators.

The fact that the adoption rate of successful innovations is bell-shaped (cumulative S-shaped) is considered the basis for most insights and analyses of new product marketing. However, these insights have been largely based on the growth of popular durables and services. In contrast, contemporary digitized markets are largely comprised of a long tail of low-popularity products for which we have little evidence on which to base the expected shape of growth. We study the growth of close to 100,000 digital products in two markets; with product size ranging from 50 downloads, to hundreds of millions. We find that across various product categories, while indeed bell-shaped growth is the clear majority among the very popular products, for lower-popularity products, it becomes a minority, with growth dominated by an exponential-like decline (“slide”), or a combination of the first two, i.e., a slide and a bell (S&B). We examine the possible explanations for this phenomenon in the markets we analyze, and discuss some of the wide-ranging implications of our understanding of new product marketing in long-tail markets.

Seeded marketing campaigns (SMCs) have become part of the marketing mix for many fast-moving consumer goods (FMCG) companies. In addition to making large investments in advertising and sales promotions, these firms now encourage seed agents or microinfluencers to discuss brands with friends and acquaintances to create further value. It is thus critical to understand how an FMCG seeding program interacts with traditional marketing tools when estimating the effectiveness of such efforts. However, the issue is still underexplored. The authors present the first empirical analysis of this question using a rich data set collected on four brands from various European FMCG markets. They combine advertising and sales promotion data from FMCG brand managers with sales and retail variables from market research companies as well as firm-created word-of-mouth variables from SMC agencies. The authors analyze the data using several approaches, confronting challenges of endogeneity and multicollinearity. They consistently find that firm-created word of mouth through SMC programs interacts negatively with all tested forms of advertising but positively with promotional activities. This phenomenon has significant implications for understanding the utility of SMCs and how they should be managed. The analysis implies that SMCs may increase total sales by approximately 3%–18% throughout the campaigns.

Though the mobile app market is substantial and growing fast, most app providers struggle to monetize apps profitably. Monetizing apps is done in two ways: a) selling advertising space within a free version of the app, and b) selling a paid version, termed freemium or in-app purchase strategy. In this paper, we present a framework for monetization of mobile apps, using two central empirical regularities concerning the relationship between users and their mobile apps: a) Sampling: While consumers have some prior knowledge of their fit with the app, they remain uncertain regarding their exact utility until they are using it; and b) Satiation: The utility of using the app may decrease with time. While work on the monetization of digital goods has largely overlooked the role of satiation and the consequent retention issues, we show that in combination with uncertainty, it elucidates the role of the segments of consumers that download the free vs. paid version of the app, and how to balance these two segments so as to monetize mobile apps. We encounter two distinct scenarios: In the first, advertising drives most of the revenues; while in the second, revenues are driven by the paid version of the app. We explain how uncertainty and satiation affect the prevalence of the respective scenarios and impact the share of revenues from the paid vs free version of the app. We also demonstrate that an app provider can profit from offering a free version with ads even if advertisers are not paying for these ads. In other words, the app provider benefits from offering a “damaged good” version of the app that includes ads, even if this version is free to consumers, and the advertisers are not paying for the ads.

In today’s turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge—with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention, leaving many managerial problems not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted—as they are not necessarily the same customers. We identify trade-offs between reactive and proactive retention programs, between short- and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management.

In today’s turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge—with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention, leaving many managerial problems not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted—as they are not necessarily the same customers. We identify trade-offs between reactive and proactive retention programs, between short- and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management.

Whether to legally protect original fashion designs against piracy is an ongoing debate among legislators, industry groups, and legal academic circles, which has gained little exposure in the marketing literature. We combine data on the growth of fashion designs, price markups, and industry statistics to develop a formal analysis of the essential questions at the base of the debate. We distinguish between three effects: Acceleration, whereby the presence of a pirated design increases the awareness of the design; Substitution, which represents the loss of sales due to consumers who would have purchased the original design, yet instead buy the knockoff; and loss because of Overexposure of the design resulting from the design's ubiquity. Using data-driven simulation analysis, we find that for the items analyzed (handbags and apparel), overexposure emerged as having a stronger negative effect (on average) on the original's profitability than the positive effect of acceleration. Both effects are considerably larger than that of substitution. This result is of particular interest given that industry groups have consistently focused on the damage caused by substitution. We also show that the effect of a legally mandated postponement on the introduction of a knockoff is non-monotonic for short lag: A short time lag may not affect the original design's NPV, and in fact may even damage it. For the ranges we analyzed, the positive effect of the protection period is observed primarily for time lags of over one year.

In recent years, word-of-mouth (WOM) marketing has been the subject of considerable interest among managers and academics alike. However, there is very little common knowledge on what drives the value of WOM programs and how they should be designed to optimize value. Firms therefore frequently rely on relatively simple metrics to measure the success of their WOM marketing efforts and mainly use rules of thumb when making crucial program design decisions. This article proposes a new method to measure WOM program value that is based on the impact of WOM on the firm’s customer equity. It then provides recommendations for the five main questions managers face when planning a WOM program: Who to target? When to launch the program? Where to launch it? Which incentives to offer? and How many participants to include?