Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.

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In this research, the authors jointly model the sentiment expressed in social media posts and the venue format to which it was posted as two interrelated processes in an effort to provide a measure of underlying brand sentiment. Using social media data from firms in two distinct industries, they allow the content of the post and the underlying sentiment toward the brand to affect both processes. The results show that the inferences marketing researchers obtain from monitoring social media are dependent on where they “listen” and that common approaches that either focus on a single social media venue or ignore differences across venues in aggregated data can lead to misleading brand sentiment metrics. The authors validate the approach by comparing their model-based measure of brand sentiment with performance measures obtained from external data sets (stock prices for both brands and an offline brand-tracking study for one brand). They find that their measure of sentiment serves as a leading indicator of the changes observed in these external data sources and outperforms other social media metrics currently used.

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.

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How users consume media has shifted dramatically as viewers migrate from traditional broadcast channels toward online channels. Rather than following the schedule dictated by television networks and consuming one episode of a series each week, many viewers engage in binge watching which involves consuming several episodes of the same series in a condensed period of time. In this research, the authors decompose a user's viewing behavior into (1) the decision to continue the session after each episode viewed, (2) whether the next episode viewed is from the same or different series and(3) the time elapsed between sessions. Applying this modeling framework to data provided by Hulu.com, a popular online provider of broadcast and cable television shows, the authors examine the drivers of binge watching behavior, distinguishing between user-level traits and previously viewed content.The authors simultaneously investigate users' response to advertisements. Many online video providers support their services with advertising revenue, making understanding how users respond to advertisements and how advertising affects subsequent viewing of paramount importance to both advertisers and online video providers. The results reveal that advertising responsiveness changes over the course of online viewing sessions, decreasing later in the viewing session. The authors discuss the implications of their results for advertisers and online video platforms.

In this research, we investigate how television advertising drives online word-of-mouth (WOM). We first explore if television advertising (1) affects online WOM about the brand advertised and (2) associates with changes in online WOM about the program in which the advertisement airs. We further examine if the media context in which the advertisement appears the television programimpacts the relationship between television advertising and online WOM. By investigating the integration of consumer social media participation with television programming, known as social TV, we aim to improve the field's understanding of the consumer experience with television, advertising, and social media. Using data containing television advertising instances and the volume of minute-by-minute social media mentions, our analyses reveal that television advertising impacts online WOM for both the brand advertised and the program in which the advertisement airs. We additionally find that the programs that receive the most online WOM aren't necessarily the best programs for advertisers in terms of online engagement. These results suggest the need for social TV activity to be viewed in terms of viewer engagement with both programs and advertisements. Moreover, the results indicate that the program in which the advertisement airs affects the extent of online WOM for both the brand and program following television advertising. Overall, this research sheds light on how marketers, television networks, and program creators can (1) increase online WOM for their respective brands and programs through media planning and advertisement design strategies and (2) incorporate online WOM into the media planning and buying process.

When defection is unobserved, latent attrition models provide useful insights about customer behavior and accurate forecasts of customer value. Yet extant models ignore direct marketing efforts. Response models incorporate the effects of direct marketing, but because they ignore latent attrition, they may lead firms to waste resources on inactive customers. We propose a parsimonious model that allows direct marketing to impact three relevant behaviors in latent attrition models—the frequency with which customers conduct transactions, the size of the transactions, and the duration for which customers remain active. Our model also accounts for how the organization targets its direct marketing across individuals and over time. Using donation data from a nonprofit organization, we find that direct marketing increases donation incidence for active donors. However, our analysis also shows that direct marketing has the potential to shorten the length of a donor's relationship. We find that our proposed model offers superior predictive performance compared with models that ignore the impact of direct marketing activity or latent attrition. We demonstrate the managerial applicability of our modeling approach by estimating the impact of direct marketing on donation behavior and identifying those donors most likely to conduct transactions in the future.