Word-of-mouth (WOM) marketing has recently attracted a great deal of attention among practitioners. For example, several books tout WOM as a viable alternative to traditional marketing communication tools. Word-of-mouth communication strategies are appealing because they combine the prospect of overcoming consumer resistance with significantly lower costs and fast delivery—especially through technology such as the Internet. Unfortunately, empirical evidence is currently scant regarding the relative effectiveness of WOM marketing in increasing firm performance over time. This raises the need to study how firms can measure the effects of WOM communications and how WOM compares with other forms of marketing communication.
The purpose of this study is to improve the understanding of the effects of WOM marketing by taking advantage of new, detailed tracking information made possible by the Internet. Using data from an online social networking site, the authors quantified the effect of WOM referrals, which are recorded electronically, on new member sign-ups to the site (i.e., customer acquisitions). They compare the effect of WOM with that of traditional marketing activity and examine its carryover dynamics both in the short run and in the long run.
Using a vector autoregression (VAR) model, the authors find that WOM referrals have a strong impact on new customer acquisition. The long-term elasticity for WOM referrals is approximately 2.5 times higher than the average advertising elasticity reported in the literature. In addition, the estimated WOM effect on new member sign-ups is substantially larger than that of traditional forms of marketing used by the site. Long-term WOM is approximately 20 times higher than the elasticity for marketing events (.53 versus .026) and 30 times higher than the elasticity for media appearances (.53 versus .017). Part of the reason for the high long-term effect of WOM relative to traditional marketing is that it has a much longer carryover period. The authors find that an increase in WOM continues to affect new member sign-ups for three weeks, while traditional marketing effects last for three to seven days.
An important feature of the VAR-based modeling approach is the ability to handle the endogeneity and indirect effects among WOM, marketing activity, and customer acquisition. The authors find that WOM and new sign-ups are endogenous; that is, WOM leads to more new members and more new members lead to more WOM. The analysis also shows that WOM may enhance the effect of traditional marketing when that activity serves to stimulate WOM. Because the proposed VAR model specification incorporates all these effects, the resultant elasticity estimates should be more valid. Comparisons of the predictive validity of the VAR model results with a series of benchmark models (including regression and diffusion models) provide additional evidence that the use of the VAR model is appropriate for this problem and data.
The article offers managers a tool to improve the metrics they use for assessing the effectiveness of traditional marketing when WOM effects are present. The authors conduct a simulation analysis to illustrate the potential monetary implications from inducing additional WOM by offering financial incentives to existing customers. The results suggest that social networking firms with a primary stream of revenues from online display advertising might consider paying upwards of $.75 for each referral.
Michael Trusov is Assistant Professor of Marketing in the Robert H. Smith School of Business at the University of Maryland. He received his PhD from the Anderson School of Management at the University of California, Los Angeles. He also holds a master’s degree in Computer Science and a master’s degree in Business Administration. He is a winner of Marketing Science Institute’s Alden Clayton Doctoral Dissertation Competition. His research interests include the Internet and e-commerce (social networks on the Internet, clickstream analysis, electronic word-of-mouth marketing, e–customer relationship management, online recommendation systems, paid search, consumer-generated media), discrete choice modeling, eye-tracking, and data mining.
Randolph E. Bucklin is Peter W. Mullin Professor in the Anderson School of Management at the University of California, Los Angeles. His research interests are in the development of models of choice behavior using historical records of customer transactions. He has published extensively on customer behavior in variety of settings, including consumer packaged goods, automotive markets, and the Internet. He is the coeditor of Marketing Letters (2006–2010) and serves on the editorial boards of Journal of Marketing Research, Marketing Science, and International Journal of Research in Marketing. Professor Bucklin received his PhD (Business) and MS (Statistics) from Stanford University and an AB (Economics) from Harvard University.
Koen Pauwels is an associate professor at Ozyegin University and an associate professor in the Tuck School of Business at Dartmouth College. He won the 2007 O’Dell award for the most influential article in Journal of Marketing Research and was a finalist for the 2008 Bass and Little awards at Marketing Science. Koen built his research insights in industries ranging from automobiles and pharmaceuticals to business content sites and fast-moving consumer goods. His current research projects include the predictive power of market dashboard metrics, performance turnaround strategies, and retailer product assortment and price wars. Professor Pauwels received his PhD in Management from the University of California, Los Angeles; he won the EMAC 2001 best-paper award; and he publishes in Harvard Business Review, Journal of Marketing, Journal of Marketing Research, Journal of Retailing, Management Science, and Marketing Science. He also serves on the editorial boards of International Journal of Research in Marketing, Journal of Marketing, Journal of Marketing Research, and Marketing Science.Journal of Marketing, Volume 73, Number 5, September 2009
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