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Can TV advertising affect societal outcomes beyond traditional marketing outcomes such as sales and brand awareness? The authors address this question in the context of the COVID-19 pandemic by analyzing daily advertising and mobility data for 2,194 counties across 204 designated market areas in the United States. By employing a border identification strategy that exploits discontinuities across television markets, the authors find a significant positive causal relationship between TV ads from brands containing COVID-19 narratives and people's social distancing behavior, while controlling for government policy interventions (e.g., shelter-in-place, mask mandates). The estimated effects are almost 11 times larger in counties without government policy interventions compared with counties with policy interventions. Notably, while the overall impact of government ads on social distancing behavior is nonsignificant, the effect becomes significantly negative (positive) in the presence (absence) of policy interventions. The results are robust to alternative model specifications, variable operationalizations, and other data considerations. The findings underscore the critical role that spillover effects from brand-sponsored TV ads can play during major public crises, including mitigating the lack of local governments' policy interventions. The findings bear substantive implications for managers and policy makers regarding how advertising strategies may help improve public health outcomes or advance social good.

Digital marketing communication, that is, communication through digital or electronic media among businesses and consumers, is growing rapidly, especially during the COVID-19 era. We propose a framework for analyzing digital marketing communication along four major dyads, business-to-consumer (B2C), business-to-business (B2B), consumer-to-consumer (C2C), and consumer-to-business (C2B). We review and summarize, for researchers and practitioners, the literature during 2000-2021 in these dyads along four major components: goals; channels, media, and platforms; content; and responses. We find that extant research in digital marketing communication pertains mostly to a specific, national level rather than a global level, despite the porousness of national boundaries for digital marketing. We derive important insights, identify key research gaps and questions in each of the dyads along these dimensions. We suggest approaches to address these research questions under three major components: substantive issues, data, and methods. These approaches can offer the insights that managers need to better formulate digital marketing strategies in local and global contexts.

This research investigates the impact of usage restriction in South Korea on both gamers and the industry using individual-level game usage and spending data.

Consumers’ postpurchase evaluations have received much attention due to the strong link between ratings and sales. However, less is known about how herding effects from reference groups (i.e., crowd and friends) unfold in online ratings. This research examines the role of divergent opinions, rater experience, and firm product portfolio in attenuating/amplifying herding influences in online rating environments. Applying robust econometric techniques on data from a community of board gamers, we find that herding effects are significant and recommend a more nuanced view of herding. Highlighting the role of rater experience, the positive influence of the crowd is weakened and friend influences are amplified as the rater gains experience. Furthermore, divergent opinions between reference groups create herding and differentiation depending on the reference group and the rater’s experience level. Finally, firms can influence online opinion through their product portfolio in profound ways. A broad and deep product portfolio not only leads to favorable quality inferences but also attenuates social influence. Implications for online reputation management, rating system design, and firm product strategy are discussed.

Salesperson turnover can have a negative overall effect on a firm. Research on salesperson turnover has conceptually studied the consequences of voluntary turnover on a firm. However, little empirical research has investigated the antecedents of salesperson turnover—specifically, the role of own effects (relative performance, customer satisfaction, and goal realization) and peer effects (peer performance variance and turnover). Therefore, the authors propose a framework to assess the influence of own factors (through identity theory) and of peer factors (through social identity theory) on salesperson turnover. Using a proportional hazard model implemented on data consisting of 6,727 salespeople over two years, the results suggest that in addition to own behaviors, managers need to pay attention to peer behaviors because peer turnover (voluntary and involuntary) greatly increases a salesperson's turnover probability. Furthermore, the results indicate that peer effects have a greater impact than own effects. This research has implications for sales force management because it helps managers (1) identify a salesperson's turnover risk, (2) diagnose the drivers of turnover behavior, and (3) build strategies to prevent salesperson turnover.

Supplemental Material, DS_10.1177_0022242919875688 for What Drives Herding Behavior in Online Ratings? The Role of Rater Experience, Product Portfolio, and Diverging Opinions by Sarang Sunder, Kihyun Hannah Kim and Eric A. Yorkston in Journal of Marketing

In this study, the authors propose a flexible framework to assess customer lifetime value (CLV) in the consumer packaged goods (CPG) context. They address the substantive and modeling challenges that arise in this setting, namely, (1) multiple discreteness, (2) brand switching, and (3) budget-constrained consumption. Using a Bayesian estimation, the authors are also able to infer the consumer's latent budgetary constraint using only transaction information, thus enabling managers to understand the customer's budgetary constraint without having to survey or depend on aggregate measures of budget constraints. Using the proposed framework, CPG manufacturers can assess CLV at the focal brand level as well as at the category level, a departure from CLV literature, which has mostly been firm centric. The authors implement the proposed model on panel data in the carbonated beverages category and showcase the benefits of the proposed model over simpler heuristics and conventional CLV approaches. Finally, they conduct two policy simulations describing the role of the budget constraint on CLV, as well as the asymmetric effects of pricing in this setting, and develop managerial insights in this context.

Despite the rise of emerging markets as lucrative destinations for business expansion, marketing literature in this area is largely anecdotal and conceptual. Further, owing to the largely unorganized retail structure in emerging markets, managers tend to make sub-optimal marketing-mix decisions by taking an aggregate view of their distribution network. In this study, we develop an econometric model to help firms develop a multichannel distribution strategy in emerging markets while accounting for (a) own-marketing mix, (b) competitive actions, (c) brand-level heterogeneity, and (d) dependencies that may arise between product offerings. The proposed model is tested on longitudinal data from a large Indian CPG manufacturer. The results indicate that firms must consider store format-specific distribution elasticities (as opposed to aggregate effects), especially in an emerging market, where the role of distribution is critical in brand success. Further, depending on the offering, price (own- and cross-) and advertising elasticities could vary even though the brand is essentially the same. Also, we find that there are significant dependencies between product forms that need to be considered when designing the marketing mix. Finally, we provide re-allocation recommendations to help managers choose the level of store format distribution in order to maximize profits. The proposed distribution re-allocation strategy resulted in an average of 7.7% increase in profits across three product forms for the focal firm.

Research on sales force evaluation has mostly relied on reflective metrics such as sales volume, revenue, and manager evaluations to assess and manage a sales force. However, businesses are moving from a product-centric to a customer-centric view and from a backward-looking to a forward-looking strategic perspective, so sales organizations must adapt to the ever-changing marketplace to maximize performance. The authors propose a forward-looking and profit-oriented metric to evaluate and demonstrate the effects of training type and incentive type on a salesperson's future value. Using a latent class modeling approach, they identify two distinct segments in the sales force that exhibit different responses to varying levels of training and incentives. This suggests that a one-size-fits-all approach to sales force management may be suboptimal. Finally, the authors also evaluate the magnitude of the proposed effects in the short run as well as the long run and show that the magnitudes of the effects could vary depending on the time horizon being considered. The authors close with a discussion of the implications for research and practice, including sales force evaluation through customer relationship management–based heuristics and optimal training and incentive management.

Most recent research on customer relationship management (CRM) has been restricted to developed economies such as the United States. Researchers have done little to study the growth of CRM in developing markets such as Asia and South America, which are becoming increasingly relevant to business today. With the changing business climate, firms are beginning to embrace the concept of managing customers rather than products. This leads managers down the path of customer centricity, and here lies the relevance of truly global CRM (GCRM). The authors conduct a qualitative study to understand the concept of GCRM and its relevance. Their results quantify the growth of CRM on a global scale using a diffusion modeling framework. The results of the generalized cross-regional diffusion model indicate that there is untapped market potential in the GCRM market, with varying adoption patterns across regions. The authors also propose a conceptual framework to understand the factors that affect cross-regional learning in GCRM adoption. In addition, the study provides insights into implementation and calibration of a GCRM framework and directions for further research.