Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products

This study examines how consumers' interest in a new experience product develops as a result of advertising and word-of-mouth activities during the pre-launch period. The empirical settings are the U.S. motion picture and video game industries. The focal variables include weekly ad spend, blog volume, online search volume during pre-launch periods, opening-week sales, and product characteristics. We treat pre-launch search volume of keywords as a measure of pre-launch consumer interest in the related product. To identify probable persistent effects among the pre-launch time-series variables, we apply a vector autoregressive modeling approach. We find that blog postings have permanent, trend-setting effects on pre-launch consumer interest in a new product, while advertising has only temporary effects. In the U.S. motion picture industry, the four-week cumulative elasticity of pre-launch consumer interest is 0.187 to advertising and 0.635 to blog postings. In the U.S. video game industry, the elasticities are 0.093 and 1.306, respectively. We also find long-run co-evolution between blog and search volume, which suggests that consumers' interest in the upcoming product cannot grow without bounds for a given level of blog volume

A Statistical Measure of a Population's Propensity to Engage in Post-Purchase Online Word-of-Mouth

The emergence of online communities has enabled firms to monitor consumer-generated online word-of-mouth (WOM) in real-time by mining publicly available information from the Internet. A prerequisite for harnessing this new ability is the development of appropriate WOM metrics and the identification of relationships between such metrics and consumer behavior. Along these lines this paper introduces a metric of a purchasing population's propensity to rate a product online. Using data from a popular movie website we find that our metric exhibits several relationships that have been previously found to exist between aspects of a product and consumers' propensity to engage in offline WOM about it. Our study, thus, provides positive evidence for the validity of our metric as a proxy of a population's propensity to engage in post-purchase online WOM. Our results also suggest that the antecedents of offline and online WOM exhibit important similarities.Comment: Published at http://dx.doi.org/10.1214/088342306000000169 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands

Can’t Take a Joke? The Asymmetrical Nature of the Politicized Sense of Humor

In an effort to tease out possible expressions of dispositional differences in people of different political ideologies, this study uses media preference and consumption data from the 2008 National Annenberg Election Survey (NAES08-Online) to examine characteristics of audiences for a range of television shows and genres. The individual shows include two political satires, The Daily Show with Jon Stewart, and The Colbert Report; a late-night comedy/variety show, The Tonight Show with Jay Leno; a hospital-based ensemble situation comedy, Scrubs; two animated comedies, The Simpsons, and The Family Guy; and two action-oriented dramas, 24, and CSI: Miami. The genres include comedies, dramas, sports and documentaries. The results of a series of one-way ANOVAs and regression analyses supported the hypotheses that conservatives do not enjoy humor as much as liberals, and that they enjoy political humor even less than non-political humor

User Reviews and Their Relationship to the Online Sale of Used DVDs

Except for books, research on factors impacting the sale of low-priced used products is uncommon. This study examines the relationship between Netflix user reviews of DVDs and their sale on Amazon as used products. An Amazon account was created by the authors, and a broad mix of 121 used DVDs were sold between 10/15/2012 and 11/20/2015. Using a low-price strategy, all sold within 57 days of posting on the site with 17.4% selling on the first day. DVDs with lower user ratings (valence) (p \u3c .001) and lower rating volume (p \u3c .05) took longer to sell. Valence and volume were not correlated (p \u3e .05). In an OLS regression that included valence, volume, price, and release date, valence showed the highest beta (-.507, p \u3c .001) for days to sale, followed by price (.237, p \u3c .01), and release date (.168, p \u3c .05). For Blu-rays which were well-liked blockbusters, volume predicted sales and valence didn’t. Valence appears to be an important word of mouth variable affecting sale of used products when the range of quality is broad

A typology categorization of millennials in their technology behavior

Hay un interés creciente por los millennials; y sin embargo, hasta la fecha hay escasas segmentaciones de los millennials en cuanto a su comportamiento en relación a la tecnología. En este contexto, este estudio trata las siguientes cuestiones:”¿Son los millennials monolíticos o hay diferentes segmentos en esta generación en cuanto a su comportamiento tecnológico?”. Y si este fuera el caso: “¿Existen diferencias importantes en cuanto a la forma en que los millennials usan la tecnología?”. Nuestro objetivo consiste en examinar los potenciales perfiles de los millennials en relación a su comportamiento y uso de la tecnología. Los datos obtenidos de una muestra de 707 millennials se analizaron mediante un análisis de componentes principales y análisis clúster. A continuación, los segmentos se caracterizaron mediante un análisis MANOVA. Nuestros resultados revelan la existencia de cinco segmentos o tipologías de millennials en cuanto a su comportamiento tecnológico: los “devotos de la tecnología”, los “espectadores”, los “prudentes”, los “adversos” y los “productivos”. Este estudio contribuye de forma detallada al conocimiento sobre cómo las diferentes categorías de millennials usan la tecnología.There is an increasing interest for millennials; however, to date millennials’ segmentations regarding their technology behavior are scarce. In this context, this study addresses the following questions: “Are millennials monolithic, or are there segments within this generation group regarding the technology behavior?”. And if so: “Are there important variances in the way that millennial segments use technology?”. Our purpose is to examine the potential profiles of millennials regarding their technology use and behavior. Data from a sample of 707 millennials was gathered and analyzed through principal component analysis and cluster analysis. Then, millennials’ segments were profiled using a MANOVA analysis. Our findings revealed five different segments or typologies of millennials regarding their technology behavior: technology devotees, technology spectators, circumspects, technology adverse users and productivity enhancers. This study contributes with a detailed perspective of how different millennial segments use technology

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

Using Consumer-Generated Social Media Posts to Improve Forecasts of Television Premiere Viewership: Extending Diffusion of Innovation Theory

Billions of US dollars in transactions occur each year between media companies and advertisers purchasing commercials on television shows to reach target demographics. This study investigates how consumer enthusiasm can be quantified (via social media posts) as an input to improve forecast models of television series premiere viewership beyond inputs that are typically used in the entertainment industry. Results support that Twitter activity (volume of tweets and retweets) is a driver of consumer viewership of unscripted programs (i.e., reality or competition shows). As such, incorporating electronic word of mouth (eWOM) into forecasting models improves accuracy for predictions of unscripted shows. Furthermore, trend analysis suggests it is possible to calculate a forecast as early as 14 days prior to the premiere date. This research also extends the Diffusion of Innovation theory and diffusion modeling by applying them in the television entertainment environment. Evidence was found supporting Rogers’s (2003) heterophilous communication, also referred to by Granovetter (1973) as “weak ties.” Further, despite a diffusion pattern that differs from other categories, entertainment consumption demonstrates evidence of a mass media (external) channel and an interpersonal eWOM (internal) channel

The Lord Of The Ratings: Is A Movie\u27s Fate is Influenced by Reviews?

Third-party reviews play an important role in many contexts in which tangible attributes are insufficient to enable consumers to evaluate products or services. In this paper, we examine the impact of professional and amateur reviews on the box office performance of movies. Using a simple diffusion model, we establish an econometrics framework to control for the interaction between the unobservable quality of movies and a word-of-mouth diffusion process and thereby estimate the residual impact of online amateur reviews on demand. The results indicate the significant influence of the valence measure (star ratings) of online reviews, but their volume measure (propensity to write reviews) is not significant once we control for quality. Furthermore, the analysis suggests that the variance measure (disagreement) of reviews does not play a significant role in the early weeks after a movie opening. The estimated influence of the valence measure implies that a one-point increase in the valence can be associated with a 4–10% increase in box office revenues