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

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

Buzz vs. Sales: Big Social Data Analytics of Style Icon Campaigns and Fashion Designer Collaborations on H&M’s Facebook Page

This paper examines the relationship between social media engagement and financial performance of the global fast fashion company, H&M. We analyze big social data from Facebook on the seven H&M style collections that occurred during 2012 and 2013 to investigate if style icon campaigns have a larger effect on quarterly sales than designer collaborations. We find that style icons such as David Beckham generate more social buzz than designer collaborations. Social Set Analysis of the Facebook data shows that the overlap between the users H&M reach with their different style collections is fairly small. The deviations between forecasted quarterly sales and actual quarterly sales are analyzed. Our results show that that style icon campaigns have a larger impact on sales than designer collaborations and reveal that the quarters with the largest deviations coincide with the quarter in which H&M ran a style icon campaign. We discuss the implications of our findings and outline directions for future research

Is Your Brand Going Out of Fashion? A Quantitative, Causal Study Designed to Harness the Web for Early Indicators of Brand Value

Can Internet search query data be a relevant predictor of financial measures of brand value? Can Internet search query data enrich existing financial measures of brand valuation tools and provide more timely insights to brand managers? Along with the financial based motivation to estimate the value of a brand for accounting purposes, marketers desire to show “accountability” of marketing activity and respond to the customer’s perception of the brand quickly to maintain their competitive advantage and value. The usefulness of the “consumer information processing” framework for brand, consumer and firm forecasting is examined. To develop our hypotheses, we draw from the growing body of work relating web searches to real world outcomes, to determine if a search query for a brand is causal to, and potentially predictive of brand, consumer and firm value. The contribution to current literature is that search queries can predict perception, whereas previous research in this nascent area predicted behavior and events. In this direction, we propose arguments underpinning this research as follows: the theoretical background relative to brand valuation and the theoretical frame based on an in-depth review of how scholars have used search query data as a predictive measure across several disciplines including economics and the health sciences. From a practitioner perspective, unlike traditional valuation methods search query data for brands is more timely, actionable, and inclusive

Using Online Search Data to Forecast New Product Sales

This dissertation focuses on online search as a measure of consumer interest. Internet use is at an all-time high in the United States, and according to the Pew Internet & American Life Project, 91% of Internet users use search engines to find information. Consumers' choices of search terms are not well understood. However, we argue that people will focus their searches on terms that are of interest to them. As such, data on the search terms used can provide valuable measures and indicators of consumer interest in a market. This can be particularly valuable to managers in search of tools to gauge potential product interest in a new product launch. In this research, we develop a model of pre-launch search activity. We find search term usage to follow rather predictable patterns in the pre-launch and post-launch periods. As such, we extend our pre-launch search model to link pre-release search behavior to release-week sales - providing a very valuable forecasting tool. We illustrate this approach in the context of motion pictures. Our modeling framework links search activity to sales and incorporates product characteristics. Our results indicate consistent patterns of search over time and systematic relationships between search volume, sales, and product attributes. We extend our model by studying the role of advertising. This allows us to better understand the relationship between advertising and online search activity and also allows us to compare the forecasting performances of each of the two approaches. We find that search data offers significant forecasting power in opening-weekend box-office revenues. We further find that advertising, combined with search data, offers improved forecasting ability

Beyond 'Global Production Networks': Australian Fashion Week's Trans-Sectoral Synergies

When studies of industrial organisation are informed by commodity chain, actor network, or global production network theories and focus on tracing commodity flows, social networks, or a combination of the two, they can easily overlook the less routine trans-sectoral associations that are crucial to the creation and realisation of value. This paper shifts attention to identifying the sites at which diverse specialisations meet to concentrate and amplify mutually reinforcing circuits of value. These valorisation processes are demonstrated in the case of Australian Fashion Week, an event in which multiple interests converge to synchronize different expressions of fashion ideas, actively construct fashion markets and enhance the value of a diverse range of fashionable commodities. Conceptualising these interconnected industries as components of a trans-sectoral fashion complex has implications for understanding regional development, world cities, production location, and the manner in which production systems “touch down” in different places

Does Chatter Matter? The Impact of User-Generated Content on Music Sales

The Internet has enabled the era of user-generated content, potentially breaking the hegemony of traditional content generators as the primary sources of “legitimate” information. Prime examples of user-generated content are blogs and social networking sites, which allow easy publishing of and access to information. In this study, we examine the usefulness of such content, consisting of data from blogs and social networking sites in predicting sales in the music industry. We track the changes in online chatter for a sample of 108 albums for four weeks before and after their release dates. We use linear and nonlinear regression to identify the relative significance of online variables on their observation date in predicting future album unit sales two weeks ahead Our findings are as follows: (a) the volume of blog posts about an album is positively correlated with future sales, (b) greater increases in an artist’s Myspace friends week over week have a weaker correlation to higher future sales, (c) traditional factors are still relevant – albums released by major labels and albums with a number of reviews from mainstream sources like Rolling Stone also tended to have higher future sales. More generally, the study provides some preliminary answers for marketing managers interested in assessing the relative importance of the burgeoning number of “Web 2.0” information metrics that are becoming available on the Internet, and how looking at interactions among them could provide predictive value beyond viewing them in isolation. The study also provides a framework for thinking about when user-generated content influences decision making

A little bluebird told me : social media conversation effects on business outcomes-evidence from the movie industry

textIn this dissertation, I examine how online conversations as electronic word-of-mouth (eWOM) information via social media networks affect business outcomes. Using data from the movie industry, my goal is to show how conversation quantity and quality, defined here as volumes and valence, on social network sites affect important business outcomes such as sales. Using a dynamic simultaneous equation system, I find that social media conversations can be a precursor to and an outcome of sales. Aggregated data from multiple sources show how social media variables and other key variables—volume, valence, and other information related to movies such as YouTube views, ratings, advertising, production budget, number of screens—contribute to box-office and home video sales through eWOM via social media. Findings highlight that eWOM volume correlates with box-office performance and home video sales: the more positive and strong the conversation, the higher the box office and home video sales. The study extends prior research on WOM and offers insight into how film studios can strategically manage social media to enhance box office and home video revenue.Advertisin