Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance

We study the relative importance of online word of mouth and advertising on firm performance over time since product introduction. The current research separates the volume of consumer-generated online word of mouth OWOM from its valence, which has three dimensions---attribute, emotion, and recommendation oriented. Firm-initiated advertising content is also classified as attribute or emotion advertising. We also shed light on the role played by advertising content on generating the different types of OWOM conversations. We use a dynamic hierarchical linear model DHLM for our analysis. The proposed model is compared with a dynamic linear model, vector autoregressive/system of equations model, and a generalized Bass model. Our estimation accounts for potential endogeneity in the key measures. Among the different OWOM measures, only the valence of recommendation OWOM is found to have a direct impact on sales; i.e., not all OWOM is the same. This impact increases over time. In contrast, the impact of attribute advertising and emotion advertising decreases over time. Also, consistent with prior research, we observe that rational messages i.e., attribute-oriented advertising wears out a bit faster than emotion-oriented advertising. Moreover, the volume of OWOM does not have a significant impact on sales. This suggests that, in our data, “what people say” is more important than “how much people say.” Next, we find that recommendation OWOM valence is driven primarily by the valence of attribute OWOM when the product is new and driven by the valence of emotion OWOM when the product is more mature. Our brand-level results help us classify brands as consumer driven or firm driven, depending on the relative importance of the OWOM and advertising measures, respectively.

[1]  B. Shiv,et al.  Heart and Mind in Conflict: The Interplay of Affect and Cognition in Consumer Decision Making , 1999 .

[2]  Dipak C. Jain,et al.  Why the Bass Model Fits without Decision Variables , 1994 .

[3]  Wendy W. Moe,et al.  Measuring the Value of Social Dynamics in Online Product Ratings Forums , 2010 .

[4]  V. Mahajan,et al.  Diffusion of New Products: Empirical Generalizations and Managerial Uses , 1995 .

[5]  Harold E. Burtt,et al.  Psychology of advertising , 1938 .

[6]  K. Pauwels,et al.  Effects of Word-of-Mouth versus Traditional Marketing: Findings from an Internet Social Networking Site , 2009 .

[7]  D. Clarke.  Econometric Measurement of the Duration of Advertising Effect on Sales , 1976 .

[8]  Christian De Cock,et al.  Organisational Change and Discourse: Hegemony, Resistance and Reconstitution , 2005 .

[9]  J. Nunnally.  Psychometric Theory (2nd ed), New York: McGraw-Hill. , 1978 .

[10]  Michael A. West,et al.  Bayesian Forecasting and Dynamic Models (2nd edn) , 1997, J. Oper. Res. Soc..

[11]  Robert P. Leone,et al.  Temporal Aggregation, the Data Interval Bias, and Empirical Estimation of Bimonthly Relations from Annual Data , 1983 .

[12]  Ambar G. Rao,et al.  Assessing When Increased Media Weight of Real-World Advertisements Helps Sales , 2002 .

[13]  John Y. Campbell,et al.  Are Output Fluctuations Transitory? , 1986 .

[14]  Gerard J. Tellis,et al.  How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities , 2011 .

[15]  Roger J. Calantone,et al.  The Stability of Benefit Segments , 1978 .

[16]  R. Kohn,et al.  On Gibbs sampling for state space models , 1994 .

[17]  Xueming Luo,et al.  Consumer Negative Voice and Firm-Idiosyncratic Stock Returns , 2007 .

[18]  T. Ambler,et al.  How Advertising Works: What Do We Really Know? , 1999 .

[19]  S. Frühwirth-Schnatter.  Data Augmentation and Dynamic Linear Models , 1994 .

[20]  Robert P. Leone.  Generalizing What Is Known About Temporal Aggregation and Advertising Carryover , 1995 .

[21]  M. West,et al.  Bayesian forecasting and dynamic models , 1989 .

[22]  Dominique M. Hanssens,et al.  The Impact of Positive vs . Negative Online Buzz on Retail Prices , 2008 .

[23]  Dani Gamerman,et al.  Dynamic hierarchical models: an extension to matrix-variate observations , 2000 .

[24]  K. Krippendorff.  Krippendorff, Klaus, Content Analysis: An Introduction to its Methodology . Beverly Hills, CA: Sage, 1980. , 1980 .

[25]  Carl F. Mela,et al.  The Long-Term Effect of Marketing Strategy on Brand Sales , 2010 .

[26]  Xueming Luo,et al.  Quantifying the Long-Term Impact of Negative Word of Mouth on Cash Flows and Stock Prices , 2009, Mark. Sci..

[27]  Dominique M. Hanssens,et al.  The Impact of Marketing-Induced versus Word-of-Mouth Customer Acquisition on Customer Equity Growth , 2008 .

[28]  Gregory S. Carpenter,et al.  Meaningful Brands from Meaningless Differentiation: The Dependence on Irrelevant Attributes , 1994 .

[29]  Bob M. Fennis,et al.  The Psychology of Advertising , 2010 .

[30]  Gary L. Kreps,et al.  Investigating Communication: An Introduction to Research Methods , 1999 .

[31]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[32]  D. Horsky,et al.  Advertising and the Diffusion of New Products , 1983 .

[33]  Pradeep Chintagunta,et al.  The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets , 2010, Mark. Sci..

[34]  Joel B. Cohen,et al.  The Nature and Role of Affect in Consumer Behavior , 2008 .

[35]  Michel Tuan Pham,et al.  When Do People Rely on Affective and Cognitive Feelings in Judgment? A Review , 2011, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[36]  Carl F. Mela,et al.  Building Brands , 2008, Mark. Sci..

[37]  A. Montgomery.  Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data , 1997 .

[38]  Natasha Zhang Foutz,et al.  Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products , 2012 .

[39]  Andrew Whinston,et al.  The Dynamics of Online Word-of-Mouth and Product Sales: An Empirical Investigation of the Movie Industry , 2008 .

[40]  Sumit K. Majumdar,et al.  Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship , 2007 .

[41]  David Godes,et al.  Using Online Conversations to Study Word-of-Mouth Communication , 2004 .

[42]  Carl F. Mela,et al.  The Dynamic Effect of Innovation on Market Structure , 2004 .

[43]  Prasad A. Naik,et al.  Planning Media Schedules in the Presence of Dynamic Advertising Quality , 1998 .

[44]  Peter E. Rossi,et al.  Estimating Price Elasticities with Theory-Based Priors , 1999 .

[45]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[46]  Pradeep K. Chintagunta,et al.  Modeling and Forecasting the Sales of Technology Products , 2004 .

[47]  Adrian F. M. Smith,et al.  Sampling-Based Approaches to Calculating Marginal Densities , 1990 .

[48]  Dominique M. Hanssens,et al.  The Impact of Marketing-Induced versus Word-of-Mouth Customer Acquisition on Customer Equity Growth , 2008 .

[49]  Wendy W. Moe,et al.  Measuring the Value of Social Dynamics in Online Product Ratings Forums , 2010 .

[50]  Puneet Manchanda,et al.  Marketing Activity, Blogging and Sales , 2012 .