The Effects of Attrition on the Growth and Equity of Competitive Services

The growth of a new service is similar to a leaking bucket: There is an influx of new customers and, concurrently, an outflow of customers who either switch to competitors or leave the category. This attrition is a major concern for service providers and significantly affects long-range profits. In this study, the authors investigate the influence of attrition on the growth of service markets. They develop a model of a multifirm growing market, where a firm may acquire customers from the pool of nonusers (which can include new customers as well as customers who disadopted the category in the past) and also acquire customers who switched from competitors. Alternatively, the firm may lose customers who switch or “churn” to a competitor or leave the category entirely. By capturing the complex dynamics of customer acquisition and retention, this model enables an in-depth analysis of the growth of services. The authors use the model to explore the influence of attrition on the service category and on a particular brand. For service categories, they show that ignoring attrition biases the diffusion parameters and hence affects management diagnostics. For the individual brand, they present a brand-level growth model and use it to capture the effect of attrition on the firm’s customer equity: they calculate the customer equity of a growing service and evaluate service firms that operate in competitive industries, including Amazon.com, Barnes&Noble.com, E*Trade, Mobistar, and SK Telecom. For four of the five firms, the results are close to the stock market valuations, which may indicate the role of customer equity in the valuation of growing service firms. The services growth model adds to the customer equity approach not only by explicitly incorporating customer attrition into market growth, but also by allowing for inter-firm churn dynamics to be included in the estimation. Hence, it is especially well suited to dealing with cases where interfirm customer churn is an integral part of the growth process

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a largescale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated1

Satiation and cross promotion: Selling and swapping users in mobile games

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

Seeding as Part of the Marketing Mix:Word-of-Mouth Program Interactions for Fast-Moving Consumer Goods

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

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a largescale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated1

Assessing Value in Product Networks

Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a large-scale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated.networks, product networks, electronic commerce, ecommerce, recommender systems, long tail