Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value – This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies

Empirical Evaluation of Automated Sentiment Analysis as a Decision Aid

Research has consistently shown that online word-of-mouth (WOM) plays an important role in shaping customer attitudes and behaviors. Yet, despite their documented utility, explicit user scores, such as star ratings have limitations in certain contexts. Automatic sentiment analysis (SA), an analytics technique that assesses the “tone” of text, has been proposed as a way to deal with these shortcomings. While extant research on SA has focused on issues surrounding the design of algorithms and output accuracy, this research-in-progress examines the behavioral and interface design issues in regards to SA scores as perceived by their intended users. Specifically, in an online context, we experimentally investigate the role of product (product category) and review characteristics (review extremity) in influencing the perceived usefulness of SA scores. Further, we investigate whether variations in how the SA scores are presented to the user, and the nature of the scores themselves further affect user perceptions

Influencing the Influencers: Analyzing Impact of Prior Review Sentiments on Product Reviews

Extant research has widely studied the impact of online product review on sales and most studies have found a significant impact of these reviews as an e-WOM tool. Given the importance of the online reviews, we study a hitherto understudied area of antecedents of sentiments in user reviews. We assess the impact of contagion effect of past review sentiments on reviewers\u27 choice to write a review. We analyze the impact of emotional response of users while writing product reviews triggered by the appraisal response to prior online reviews. A short selection of reviews, which most e-commerce websites show, along with the numerical product rating (if any) could strongly bias the sentiments in a review being written under their influence. Through a mix of experimental methods and text analysis of online reviews, we find that review writers tend to veer towards extreme reviews in absence of any benchmark or prior review

Word of Mouth, the Importance of Reviews and Ratings in Tourism Marketing

The Internet and social media have given place to what is commonly known as the democratization of content and this phenomenon is changing the way that consumers and companies interact. Business strategies are shifting from influencing consumers directly and induce sales to mediating the influence that Internet users have on each other. A consumer review is “a mixture of fact and opinion, impression and sentiment, found and unfound tidbits, experiences, and even rumor” (Blackshaw & Nazarro, 2006). Consumers' comments are seen as honest and transparent, but it is their subjective perception what shapes the behavior of other potential consumers. With the emergence of the Internet, tourists search for information and reviews of destinations, hotels or services. Several studies have highlighted the great influence of online reputation through reviews and ratings and how it affects purchasing decisions by others (Schuckert, Liu, & Law, 2015). These reviews are seen as unbiased and trustworthy, and considered to reduce uncertainty and perceived risks (Gretzel & Yoo, 2008; Park & Nicolau, 2015). Before choosing a destination, tourists are likely to spend a significant amount of time searching for information including reviews of other tourists posted on the Internet. The average traveler browses 38 websites prior to purchasing vacation packages (Schaal, 2013), which may include tourism forums, online reviews in booking sites and other generic social media websites such as Facebook and Twitter.Peer reviewedFinal Accepted Versio

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Secure webs and buying intention: the moderating role of usability

El presente trabajo ha planteado un modelo conceptual a fin de mostrar como los antecedentes de la intención de compra se ven reforzados en contextos de Webs altamente usables. Específicamente, el trabajo analiza en profundidad el rol moderador de la usabilidad en la explicación de la conexión entre seguridad de una Web e intención de compra. Entre ambos extremos (seguridad e intención de compra), se han incluido diversas variables para explicar mejor su conexión. Para ello, ha sido diseñada una Web ficticia de ropa dirigida al segmento joven de clase media. A fin de alterar la usabilidad de la Web se han realizado dos tipos de manipulaciones: la velocidad y la facilidad de uso de la Web. Las dos Webs creadas (alta usabilidad y baja usabilidad) fueron visitadas por un total de 170 encuestados que fueron compensados con un USB valorado en 15 euros. Los resultados muestran que la seguridad percibida en la Web acarrea tres interesantes efectos (especialmente para la Web altamente usable): (i) mejora las actitudes agrado, (ii) reduce el nivel de riesgo percibido; (iii) aumenta la confianza. Los dos últimos efectos, a su vez, acaban aumentando la intención de compra.. Por último, se ha demostrado que la usabilidad, efectivamente, refuerza las relaciones consideradas en el modelo propuesto para explicar la intención de compra.A conceptual model has been proposed to show how buying intention antecedents are reinforced in highly usable contexts. Specifically, this paper deeply analyses the moderator role of system variables (usability) on explaining the relationship between Web security and buying intention. Between both extremes (security and buying intention), several relationships have also been stated to better explain this effect. An “ideal” fictitious Website was designed for a non existent clothing company directed at the segment of middle class consumers. In order to alter Web usability, two blocks of changes were made, one concerning Website speed and the other related to ease of use. Our experiment sample consisted of 170 respondents who participated in exchange for a pen-drive (USB) valued at 15 euros. The results show that improving website security has three interesting effects (especially in high usable contexts): (i) it improves pleasure attitudes, (ii) reduces the level of perceived risk and (iii) increases trust. Secondly, it has been found that to increase buying intention, two actions must be taken: (i) to diminish perceived risk and (ii) to improve users’ pleasure attitudes towards the Website. Finally, usability has been found to have a moderating role in all the relationships considered (reinforcing them)