Detection of potential customers’ empathy behavior towards customers' reviews

The aim of the current study is to identify potential customers' empathy behavior and their behavioral reactions based on appraisal and stimulus–organism–response (SOR) theories to customers' reviews of financial services firms using lexicon-based unsupervised learning techniques. After filtering, we obtained 30263 reviews from the Yelp dataset of financial service companies. We examined the connections between several sorts of emotional dimensions and different types of behavioral reactions of potential consumers using lexicon-based unsupervised machine learning methods. Our findings show that the various types of customer sentiment have a significant impact on potential customers' emotional experiences on social media platforms, prompting them to behave differently. Furthermore, potential consumers' reactions to the customers' reviews varied according to their seven emotional aspects. The study is the first to examine the impact of potential customers' empathetic behavioral reactions on customers' evaluations using lexicon-based unsupervised learning techniques.     

An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews

The amount of digital text-based consumer review data has increased dramatically and there exist many machine learning approaches for automated text-based sentiment analysis. Marketing researchers have employed various methods for analyzing text reviews but lack a comprehensive comparison of their performance to guide method selection in future applications. We focus on the fundamental relationship between a consumer’s overall empirical evaluation, and the text-based explanation of their evaluation. We study the empirical tradeoff between predictive and diagnostic abilities, in applying various methods to estimate this fundamental relationship. We incorporate methods previously employed in the marketing literature, and methods that are so far less common in the marketing literature. For generalizability, we analyze 25,241 products in nine product categories, and 260,489 reviews across five review platforms. We find that neural network-based machine learning methods, in particular pre-trained versions, offer the most accurate predictions, while topic models such as Latent Dirichlet Allocation offer deeper diagnostics. However, neural network models are not suited for diagnostic purposes and topic models are ill equipped for making predictions. Consequently, future selection of methods to process text reviews is likely to be based on analysts’ goals of prediction versus diagnostics.     

Social Media's Impact on the Consumer Mindset: When to Use Which Sentiment Extraction Tool?

User-generated content provides many opportunities for managers and researchers, but insights are hindered by a lack of consensus on how to extract brand-relevant valence and volume. Marketing studies use different sentiment extraction tools (SETs) based on social media volume, top-down language dictionaries and bottom-up machine learning approaches. This paper compares the explanatory and forecasting power of these methods over several years for daily customer mindset metrics obtained from survey data. For 48 brands in diverse industries, vector autoregressive models show that volume metrics explain the most for brand awareness and purchase intent, while bottom-up SETs excel at explaining brand impression, satisfaction and recommendation. Systematic differences yield contingent advice: the most nuanced version of bottom-up SETs (SVM with Neutral) performs best for the search goods for all consumer mind-set metrics but Purchase Intent for which Volume metrics work best. For experienced goods, Volume outperforms SVM with neutral. As processing time and costs increase when moving from volume to top-down to bottom-up sentiment extraction tools, these conditional findings can help managers decide when more detailed analytics are worth the investment.     

Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media

The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.     

Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House

CRM has traditionally referred to a company managing relationships with customers. The rise of social media, which has connected and empowered customers, challenges this fundamental raison d'etre. This paper examines how CRM needs to adapt to the rise of social media. The convergence of social media and CRM creates pitfalls and opportunities, which are explored. We organize this discussion around the new “social CRM house,” and discuss how social media engagement affects the house's core areas (i.e., acquisition, retention, and termination) and supporting business areas (i.e., people, IT, performance evaluation, metrics and overall marketing strategy). Pitfalls discussed include the organization's lack of control over message diffusion, big and unstructured data sets, privacy, data security, the shortage of qualified manpower, measuring the ROI of social media marketing initiatives, strategies for managing employees, integrating customer touch points, and content marketing.     

Two hearts in three-quarter time: How to waltz the social media/viral marketing dance

The concept of viral marketing has been discussed in the literature for over 15 years, since Jeffrey Rayport first introduced the term in 1996. However, the more widespread use of social media has recently pushed this idea to a whole new level. We provide insight into the relationship between social media and viral marketing, and illustrate the six steps executives should take in order to dance the social media/viral marketing waltz. We define viral marketing as electronic word-of-mouth whereby some form of marketing message related to a company, brand, or product is transmitted in an exponentially growing way—often through the use of social media applications. We consider the three conditions that need to be fulfilled to create a viral marketing epidemic (i.e., giving the right message to the right messengers in the right environment) and present four different groups of social media viral marketing campaigns (nightmares, strokes-of-luck, homemade issues, and triumphs). We conclude with five points of caution that managers should heed when trying to launch their own viral marketing campaign.     

Payday Loan Marketing in Social Media Networks

The payday lending industry has grown dramatically over the past two decades, with lenders increasingly marketing their products on the Internet. However, little is known about payday loan (PDL ) marketing through social media networks. Our study is the first to report the PDL promotions on Twitter. Out of 23,276 tweets related to PDLs from June 29, 2015 to October 2, 2015, 71% were commercial tweets, most of which came from a small percentage of users. The overall impact to the general public was large and we observed a range of opinions in our sentiment analysis. Commercial tweets had an average sentiment score of 34.9 while noncommercial had a total average sentiment score of 50.7. Commercial tweets were more concentrated in states with restrictive or hybrid regulations on PDL . Our findings provide evidence for enhancing online PDL regulations and using social media data for real‐time monitoring of PDL lending.     

Online food delivery companies' performance and consumers expectations during Covid-19: An investigation using machine learning approach

Online food delivery (OFD) businesses flourished during COVID-19; however, OFD companies experienced different challenges and customers' expectations. This paper uses social media data to explore OFD companies' performance and customers' expectations during the COVID-19 pandemic. The most important topics in developed and developing countries are identified using machine learning. Results show that customers in India are more concerned about social responsibility, while financial aspects are more important in the US. Overall, customers in India are more satisfied with OFD companies during the COVID-19 pandemic than the US customers. We further find that factors such as OFD companies' brand, market size, country, and COVID-19 waves play a crucial role in moderating customer sentiment. The results of the study offer several managerial insights.     

The role of machine learning analytics and metrics in retailing research

This research presents the use of machine learning analytics and metrics in the retailing context. We first discuss what is machine learning and explain the field’s origins. We then demonstrate the strengths of machine learning methods using an online retailing dataset, noting key areas of divergence from the traditional explanatory approach to data analysis. We then provide a review of the current state of machine learning in top-level retailing and marketing research, integrating ideas for future research and showcasing potential applications for practitioners. We propose that the explanatory and machine learning approaches need not be mutually exclusive. Particularly, we discuss four key areas in the general scientific research process that can benefit from machine learning: data exploration/theory building, variable creation, estimation, and predicting an outcome metric. Due to the customer-facing nature of retailing, we anticipate several challenges researchers and practitioners might face in the adoption and implementation of machine learning, such as ethical prediction and customer privacy issues. Overall, our belief is that machine learning can enhance customer experience and, accordingly, we advance opportunities for future research.     

Making new products go viral and succeed

Why are some new product introductions more viral and successful than others? This research integrates theories of interpersonal communication and consumer learning to explore this question. Analyzing a unique data set of millions of consumer word-of-mouth transmissions (eWOM) on social media regarding 345 new automobile products introduced during 2008–2015, we find that more innovative products generate more eWOM volume but surprisingly less positive sentiment. These effects vary in magnitude across eWOM channels. However, the use of rich-content communication, pre-announcement, and cobranding strengthens (weakens) the positive (negative) effect of product innovativeness on eWOM volume (sentiment). The results further indicate that eWOM sentiment is a stronger predictor of new product success than eWOM volume. Experimental results reveal more insights into how product innovativeness influences eWOM metrics in several product categories and shed light on the role of excitement and perceived risk as mechanisms underlying these effects. The research offers useful implications for firms to design effective viral marketing campaigns to enhance new product success.     

In bot we trust: A new methodology of chatbot performance measures

Chatbots are used frequently in business to facilitate various processes, particularly those related to customer service and personalization. In this article, we propose novel methods of tracking human-chatbot interactions and measuring chatbot performance that take into consideration ethical concerns, particularly trust. Our proposed methodology links neuroscientific methods, text mining, and machine learning. We argue that trust is the focal point of successful human-chatbot interaction and assess how trust as a relevant category is being redefined with the advent of deep learning supported chatbots. We propose a novel method of analyzing the content of messages produced in human-chatbot interactions, using the Condor Tribefinder system we developed for text mining that is based on a machine learning classification engine. Our results will help build better social bots for interaction in business or commercial environments.     

Social media sentimentas an additional performance measure? Examples from iconic theme park destinations

This analysis quantifies social media data collected for two iconic theme park destinations. Disney World and SeaWorld were studied in-depth using social media analytics, and the findings were compared to publicly available performance measures. The scale and length of social media topics discussed differed significantly, and there was mixed evidence of correlations between social media sentiment and other public performance measures. As the role of social media contributions to selecting retailers and service providers develops, understanding the sentiment around well-known organizations and potential impacts of major events can aid decision makers in the retailing of consumer services.     

Mining the text of online consumer reviews to analyze brand image and brand positioning

The growth of the Internet has led to massive availability of online consumer reviews. So far, papers studying online reviews have mainly analysed how non-textual features, such as ratings and volume, influence different types of consumer behavior, such as information adoption decisions or product choices. However, little attention has been paid to examining the textual aspects of online reviews in order to study brand image and brand positioning. The text analysis of online reviews inevitably raises the concept of “text mining”; that is, the process of extracting useful and meaningful information from unstructured text. This research proposes an unified, structured and easy-to-implement procedure for the text analysis of online reviews with the ultimate goal of studying brand image and brand positioning. The text mining analysis is based on a lexicon-based approach, the Linguistic Inquiry and Word Count (Pennebaker et al., 2007), which provides the researcher with insights into emotional and psychological brand associations.     

Implementing social media marketing strategically: an empirical assessment

The purpose of this study is to examine how firms implement social media systematically to drive strategic marketing actions. To this end, the study conceptualises social media implementation as a multidimensional, organisational construct composed of social media strategy, active presence, customer engagement initiatives and social media analytics. Using primary data, the study operationalises the social media implementation construct and tests its effect on firm performance isolated into social media performance and marketing performance. The results indicate that all except the active presence dimension of social media implementation are positively related to social media performance. The results further indicate that social media performance is positively related to marketing performance. The study contributes to the literature by offering a novel conceptualisation and empirical validation of the social media implementation construct.     

Simplicity is not key: Understanding firm-generated social media images and consumer liking

Social media platforms are becoming increasingly important marketing channels, and recently these channels are becoming dominated by content that is not textual, but visual in nature. In this paper, we explore the relationship between the visual complexity of firm-generated imagery (FGI) and consumer liking on social media. We use previously validated image mining methods, to automatically extract interpretable visual complexity measures from images. We construct a set of six interpretable measures that are categorized as either (1) feature complexity measures (i.e., unstructured pixel-level variation; color, luminance, and edges) or (2) design complexity measures (i.e., structured design-level variation; number of objects, irregularity of object arrangement, and asymmetry of object arrangement). These measures and their interpretability are validated using a human subject experiment. Subsequently, we relate these visual complexity measures to the number of likes. The results show an inverted u-shape between feature complexity and consumer liking and a regular u-shape relationship between design complexity and consumer liking. In addition, we demonstrate that using the six individual measures that constitute feature- and design complexity provides a more nuanced view of the relationship between the unique aspects of visual complexity and consumer liking of FGI on social media than observed in previous studies that used a more aggregated measure. Overall, the automated framework presented in this paper opens up a wide range of possibilities for studying the role of visual complexity in online content.     

Social media in marketing research: Theoretical bases,methodological aspects,and thematic focus

The widespread use of social media as a marketing tool during the last decade has been responsible for attracting a significant volume of academic research, which, however, can be described as highly fragmented to yield clear directions and insights. We systematically synthesize and critically evaluate extant knowledge of social media marketing extracted from 418 articles published during the period 2009–2021. In doing so, we use an organizing framework focusing on five key areas of social media marketing research, namely, social media as a promotion and selling outlet, social media as a communication and branding channel, social media as a monitoring and intelligence source, social media as a customer relationship management and value cocreation platform, and social media as a general marketing and strategic tool. Within each of these areas, we provide important theoretical, methodological, and thematic insights, as well as future research directions. We also offer useful managerial implications derived from the articles reviewed.     

If you love something,let it go mobile: Mobile marketing and mobile social media 4x4

Social media applications—including collaborative projects, micro-blogs/blogs, content communities, social networking sites, and virtual worlds—have become part of the standard communication repertoire for many companies. Today, with the creation of increasingly powerful mobile devices, numerous social media applications have gone mobile and new entrants are constantly appearing. The purpose of this article is to take account of this evolution, and provide an introduction to the general topic of mobile marketing and mobile social media. Herein, we define what mobile social media is, what it is not, and how it differs from other types of mobile marketing applications. Further, we discuss how firms can make use of mobile social media for marketing research, communication, sales promotions/discounts, and relationship development/loyalty programs. We present four pieces of advice for mobile social media usage, which we refer to as the ‘Four I's’ of mobile social media. Finally, we conclude by providing some thoughts on the future evolution of this new and exciting type of application.     

Big data on a smaller scale: A social media analytics assignment

It is truly important for students to understand how to monitor online marketing buzz. This assignment, social media analytics, utilizes the content analysis research method to build student's in-depth understanding on how to evaluate and interpret user-generated content (UGC) to create social media campaigns. The authors adapted Resnik and Stern's (1977) coding scheme for UGC. Through experiential learning, students immerse themselves in data and analyze UGC. The assignment scored high in knowledge acquisition as a pedagogical tool. Finally, the authors provide an updated social media analytics coding scheme, guidelines for instructors, student rubric information, and student learning outcomes.