On the motivating impact of price and online recommendations at the point of online purchase

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 ElsevierDo online recommendations have the same motivating impact as price at the point of online purchase? The results (n = 268) of an conjoint study show that: (1) when the price is low or high relatively to market price, it has the strongest impact (positive and negative) on the likelihood of an online purchase of an mp3 player, (2) when the price is average to market price, online recommendation and price are equal in their impact at the point of online purchase, and, (3) the relative impact from price increases when online shopping frequencies increases. The implications these results give are that online retailers should be aware that online recommendations are not as influential as a good offer when consumers purchase electronics online. However, other customer recommendations have a stronger impact on novice online shoppers than towards those consumers that shop more frequently online

Securities trading of concepts (STOC)

Identifying winning new product concepts can be a challenging process that requires insight into private consumer preferences. To measure consumer preferences for new product concepts, the authors apply a “securities trading of concepts,” or STOC, approach, in which new product concepts are traded as financial securities. The authors apply this method because market prices are known to efficiently collect and aggregate private information regarding the economic value of goods, services, and firms, particularly when trading financial securities. This research compares the STOC approach against stated-choice, conjoint, constant-sum, and longitudinal revealed-preference data. The authors also place STOC in the context of previous research on prediction markets and experimental economics. Across multiple product categories, the authors test whether STOC (1) is more cost efficient than other methods, (2) passes validity tests, (3) measures expectations of others, and (4) reveals individual preferences, not just those of the crowd. The results show that traders exhibit a self-preference bias when trading. Ultimately, STOC offers two key advantages over traditional market research methods: cost efficiency and scalability. For new product development teams deciding how to invest resources, this scalability may be especially important in the Web 2.0 world.United States. Office of Naval Research (Contract Number N00014-93-1-3085)National Science Foundation (U.S.). Information Technology Research (Contract Number IIS-0085836)National Science Foundation (U.S.). Knowledge and Distributed Intelligence Initiative (Contract Number DMS-9872936)National Science Foundation (U.S.) (Contract Number IIS-9800032)United States. Office of Naval Research (United States. Defense Advanced Research Projects Agency) (Contract Number N00014-00-1-0907

Experimental Markets for Product Concepts

Market prices are well known to efficiently collect and aggregate diverse information regarding the value of commodities and assets. The role of markets has been particularly suitable to pricing financial securities. This article provides an alternative application of the pricing mechanism to marketing research - using pseudo-securities markets to measure preferences over new product concepts. Surveys, focus groups, concept tests and conjoint studies are methods traditionally used to measure individual and aggregate preferences. Unfortunately, these methods can be biased, costly and time-consuming to conduct. The present research is motivated by the desire to efficiently measure preferences and more accurately predict new product success, based on the efficiency and incentive-compatibility of security trading markets. The article describes a novel market research method, pro-vides insight into why the method should work, and compares the results of several trading experiments against other methodologies such as concept testing and conjoint analysis

Hedonic Consumer Decision Making And Implications For The Marketing Of Media Goods

This cumulative dissertation investigates aspects of consumer decision making in hedonic contexts and its implications for the marketing of media goods through a series of three empirical studies. All three studies take place within a common theoretical framework of decision making models, applying parts of the framework in novel ways to solve real-world marketing research problems (study 1 and 2), and examining theoretical relationships between variables within of the framework (study 3). One notable way in which the studies differ is their theoretical treatment of the hedonic component of decision making, i.e. the role and conceptualization of emotions.Die vorliegende kumulative Dissertation untersucht anhand von drei empirischen Studien Entscheidungsverhalten im Kontext des Hedonischen Konsums und dessen Implikationen für das Marketing von Mediengütern. Hedonischer Konsumer ist definiert als die Facette des Konsumentenverhaltens, die sich auf „multisensorische, fantastische und emotionale Aspekte der Produktnutzung“ bezieht. Alle drei Studien sind weitgehend in den theoretischen Bezugsrahmen des „Information Processing View“ eingebettet, der Konsumenten als begrenzt rationale Nutzenmaximierer beschreibt. Die Kapitel 1 und 2 dieser Dissertation wenden Teile der Information Processing View-Theorie in neuartiger Weise auf aktuelle Probleme der Marketing-Forschung und der Filmindustrie an, während Kapitel 3 den Information Processing View systematisch um emotionale Aspekte des Entscheidungsverhaltens ergänzt und die theoretischen Beziehungen der Modellvariablen untereinander erforscht

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Improving productivity in the entertainment industry is a very challenging task as it heavily depends on generating attractive content for the consumers. The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can design customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry. We use the natural language processing methodology combined with econometric analysis to explore whether and to what extent emotions shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analyzing 6,174 movie scripts, we generate the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are then plugged into an econometric model to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers’ and critics’ reviews. We find that emotional arcs in movies can be partitioned into 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This U-shaped emotional arc results in financially successful movies irrespective of genre and production budget. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed

"Pre-launch prediction of market performance for short lifecycle products using online community data"

Prediction of sales for short life-cycle products can be problematic. Generic predictive models based on past launches may provide only crude historic data which are unsuited for distinctive, innovative products. This paper investigates the role of online communities in providing pre-launch data to predict post-launch sales. We argue that levels of awareness, word-of-mouth, expectations, and adoption intention prevailing within an online community for an upcoming product have an independent direct effect on the product's future sales. Additionally, we test the complementarity effect of these community variables by introducing a higher order construct called Pre-release Community Buzz, to demonstrate the incremental explanatory power of using pre-launch community variables to predict post-launch sales. Data for community variables were collected from a movie-based online community, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). We found strong support for both direct and complementarity effects of community variables in predicting a movie's opening week sales. We also found that community variables mediate the effects of generic predictor variables such as MPAA ratings, star cast, production budget and competition on opening week sales. Tests for robustness demonstrated the value of community variables. Models which included community variables had higher predictive power than those without. Implications for theory and practice are presented

Demand Forecasting: Evidence-Based Methods

In recent decades, much comparative testing has been conducted to determine which forecasting methods are more effective under given conditions. This evidence-based approach leads to conclusions that differ substantially from current practice. This paper summarizes the primary findings on what to do – and what not to do. When quantitative data are scarce, impose structure by using expert surveys, intentions surveys, judgmental bootstrapping, prediction markets, structured analogies, and simulated interaction. When quantitative data are abundant, use extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Among causal methods, use econometrics when prior knowledge is strong, data are reliable, and few variables are important. When there are many important variables and extensive knowledge, use index models. Use structured methods to incorporate prior knowledge from experiments and experts’ domain knowledge as inputs to causal forecasts. Combine forecasts from different forecasters and methods. Avoid methods that are complex, that have not been validated, and that ignore domain knowledge; these include intuition, unstructured meetings, game theory, focus groups, neural networks, stepwise regression, and data mining

Marketing Science Conference and the Stanford GSB Marketing Seminar for their useful comments. All errors are my own. Correspondence: The University of Chicago

Abstract Digitization of content is changing how consumers and firms use purchase and rental markets. Low transaction costs make accessing content easier for consumers. Digital technology enables firms to create non-durable "rental" versions of their content and restrict content to the purchasing consumer, effectively shutting down resale markets. To empirically analyze the interaction of purchase and rental markets, I design a preference measurement tool to recover consumers' inter-temporal preferences through currentperiod choices alone. I then use these preferences to solve for a dynamic equilibrium between consumers and the firm. In the context of the online home-video market, I find that when the firm is able to commit to holding prices fixed forever, providing content through the purchase market alone is sufficient. However, when the firm is unable to commit, it should serve both purchase and rental markets. Canonical theory models would predict exclusive rentals, but the purchase option enables indirect price discrimination in practice. I also find that when consumers place a premium on accessing new content, they are less likely to inter-temporally substitute, thereby increasing the firm's pricing power. Consistent with theory, commitment to future prices increases profits considerably. This finding supports the rigid pricing structure of such retailers as Apple, despite studios' push toward more pricing flexibility. Keywords: purchase and rental markets, durable good pricing, online content, experiment design, conjoint analysis * This paper is based on my dissertation. I would like to thank my advisor Wesley Hartmann for his invaluable guidance. I would also like to thank my dissertation committee members Harikesh Nair, Sridhar Narayanan and V. Srinivasan for their valuable feedback. Thanks to Latika Chaudhury, Pradeep Chintagunta, J.P.Dubé, Avi Goldfarb, Günter Hitsch, Oleg Urminsky; to seminar participants at Chicago, Cornell, Dartmouth, Northwestern, Ohio State, Rochester and UBC; to participants at th