Journal of Interactive Marketing

An increasing number of companies engage in the customization of products to reap the benefits of increased sales and the potential to charge higher prices. Online configurators, which allow customers to assemble “customized” versions of a product, play an important role in customization. In particular, they help companies learn about user preferences and generate reliable forecasts. Therefore, menu-based choice experiments have gained increased attention in recent years. Despite their high relevance, little is known about the properties of the different modeling approaches under varying study conditions. We compare four prominent modeling approaches in an extensive simulation study that systematically varies respondent heterogeneity, choice menu complexity, the available sample size, the number of individual tasks, and the underlying behavioral model. We evaluate the models for reservation price recoverability, model fit, and predictive validity. The findings show that in most cases, one obtains better results for simple and straightforward representations of respondent behavior (e.g., separate multinomial logit models for different functional areas) than for the more sophisticated modeling approaches (e.g., probit-based approaches).

A challenge in digital content marketing is to create meaningful messages on meaningful moments. To do so, brands frequently align social media messages with topical moments, also known as Real-time Marketing (RTM). While RTM aims to make meaningful connections, the creative development is subject to time pressure due to its real-time nature, which could have a negative effect on originality and craftsmanship, two other creativity dimensions besides meaningfulness which drive consumer responses. We address this tension by examining the creative crafting of RTM on Instagram and its consequences. Based on a content analysis of 516 Instagram messages, we indeed found a meaningfulness bias for RTM, such that meaningfulness comes at the expense of originality and craftsmanship. However, the findings from the content analysis, as well as an additional experiment (N = 245), showed that only craftsmanship and originality, and not meaningfulness, positively induced consumer responses. Implications are discussed.

Brands regularly attempt to stimulate consumer engagement by posting messages on social media platforms (e.g., Facebook), but their messages are often ignored. How can managers write social media messages that engage consumers? The present research sheds light on how the language of brand messages influences consumer engagement. Text analyses of brand messages posted on Facebook and Twitter shows that brand messages that include words that express certainty (e.g., always, everything, forever) are associated with higher levels of consumer engagement (e.g., likes, comments, shares/retweets). The results of two controlled experiments demonstrate that messages expressing certainty increase engagement by influencing how consumers perceive brands. Specifically, expressing certainty makes brands seem more powerful, and perceptions of power exert a positive effect on engagement. In addition, the indirect effect of certainty on consumer engagement is stronger (weaker) among consumers with higher (lower) power distance beliefs. These results assist marketing managers by demonstrating how language influences consumer engagement on social media.

The purpose of this study is to understand the role of social media content on users’ engagement behavior. More specifically, we investigate: (i)the direct effects of format and platform on users’ passive and active engagement behavior, and (ii) we assess the moderating effect of content context on the link between each content type (rational, emotional, and transactional content) and users’ engagement. The dataset contained 1,038 social media posts and 1,336,741 and 95,996 fan likes and comments, respectively based on Facebook and Instagram. The results reveal that the effectiveness of social media content on users’ engagement is moderated by content context. The findings contribute to understanding engagement and users’ experience with social media. This study is a pioneering one to empirically assess the construct of social media engagement behavior through the effects of content types and content contexts on a dual social media platform.

Unlike brick-and-mortar stores where consumers can use all of their senses to examine a product, online retailers must properly present products to shoppers via a computer-mediated interface. In this exploratory study, we investigate how the brain makes purchase decisions when it encounters different types of visual presentation strategies. Specifically, we use event-related fMRI to study brain activation preceding purchase decisions under three visual presentation strategies: (1) static picture, (2) image zooming, and (3) rotation video. Twenty-four (24) participants made 60 apparel purchase decisions. Utilizing machine learning, we study whether and how various neural circuits are engaged in purchase decision-making in regard to different types of visual sensory information. The functional neuroimaging results suggest high accuracy (highest: 95%, Rotation condition) in predicting purchase decisions using brain activity in the product evaluation process. Furthermore, cross-category validation reveals the activation patterns under the Rotation condition most successfully predict choices across the other two presentation conditions: this suggests a potential universal brain activity pattern in these apparel purchase decisions. Finally, we found some brain regions (such as the cerebellum) that are less likely to be associated with purchase decisions but are considered influential in predicting online apparel purchase decisions in the context of watching a rotation product video. The cerebellar activation may suggest association with spatial cognition and virtual navigation in addition to motor control. This study contributes to the research field of NeuroIS and online shopper study. The results provide empirical evidence to support the refined S-O-R theoretical model Jacoby (2002) and heuristic information process and inform the practical implications of sensory-engaging presentations in helping consumers to make online purchase decisions.

In predictive modeling, firms often deal with high-dimensional data that span multiple channels, websites, demographics, purchase types, and product categories. Traditional customer response models rely heavily on feature engineering, and their performance depends on the analyst's domain knowledge and expertise to craft relevant predictors. As the complexity of data increases, however, traditional models grow exponentially complicated. In this paper, we demonstrate that long-short term memory (LSTM) neural networks, which rely exclusively on raw data as input, can predict customer behaviors with great accuracy. In our first application, a model outperforms standard benchmarks. In a second, more realistic application, an LSTM model competes against 271 hand-crafted models that use a wide variety of features and modeling approaches. It beats 269 of them, most by a wide margin. LSTM neural networks are excellent candidates for modeling customer behavior using panel data in complex environments (e.g., direct marketing, brand choices, clickstream data, churn prediction).

Brands, both human and corporate, are increasingly communicating with their consumers using emojis. The current work examines if and how these pictographs shape online brand engagement on Twitter (i.e., likes & retweets). To do so, we first examine datasets generated by scraping the tweets of the most popular celebrity brands and most popular corporate brands (Study 1). This study demonstrates that emoji presence increases engagement with tweets, with more emoji leading to more likes and retweets. Two controlled experiments then explore the role of perceived playfulness in explaining this effect of emojis on engagement (Studies 2 and 3). We find that the effect of emojis on brand engagement varies depending on the nature of the interplay between emojis and text, and the subsequent effect of this interplay on perceived playfulness. Theoretical contributions and social media practitioner implications are also addressed.

Communication platforms are undergoing a transition from physical to digital spaces. The Internet has radically changed the business scenario wherein people have become the “media” for collaborating to share information. Practitioners have started using electronic word of mouth (eWOM) for consumer insight through text analytics, sentiment, hashtag analytics, and other machine learning tools. Due to the growing importance of the topic as a research area, it is vital to map the research domain. This paper uses performance analysis tools (quantitative and qualitative) and science mapping tools (conceptual and intellectual structures) for literature review and the identification of future research directions. The content analysis of emergent topics and themes offers directions for future researchers. Recommender systems, data mining (artificial intelligence and machine learning), semantics, and sentiment analysis are emergent topics. We contribute to the eWOM literature by canvassing state-of-the-art research and proposing future research directions.