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Abstract:
The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations sent out by existing members, outbound WOM may be precisely tracked. WOM, along with traditional marketing, can then be linked to the number of new members subsequently joining the site (signups). Due to the endogeneity among WOM, new signups, and traditional marketing activity, the authors employ a Vector Autoregression (VAR) modeling approach. Estimates from the VAR model show that word-of-mouth referrals have substantially longer carryover effects than traditional marketing actions. The long-run elasticity of signups with respect to WOM is estimated to be 0.53 (substantially larger than the average advertising elasticities reported in the literature) and the WOM elasticity is about 20 times higher than the elasticity for marketing events, and 30 times that of media appearances. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper bound estimate for the financial incentives the firm might offer to stimulate word-of-mouth.

Word-of-Mouth Marketing, Internet, Social Networks, Vector Autoregression

Abstract:
The success of Internet social networking sites depends on the number and activity levels of their user members. While users typically have numerous connections to other site members (i.e., “friends”), only a fraction of those “friends” may actually influence a member’s site usage. Since the influence of potentially hundreds of friends needs to be evaluated for each user, inferring precisely who is influential - and therefore of managerial interest for advertising targeting and retention efforts - is difficult. We develop an approach to determine which users have significant effects on the activities of others using the longitudinal records of members’ login activity. We propose a non-standard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each user. The approach identifies the specific users who most influence others’ activity and does so considerably better than simpler alternatives. For the social networking site data, we find that, on average, about one-fifth of a user's friends actually influence his/her activity level on the site.

Internet, Social Networking, Bayesian Methods

Abstract:
Extant research has shown that consumer online product ratings can significantly influence product sales. However, these ratings have also been shown to be subject to a number of social influences. In other words, posted product ratings not only reflect the customers’ experience with the product but also reflect the influence of others’ ratings. The objective of this paper is to model posted product ratings in an effort to measure the impact of the social dynamics that may occur in a ratings environment on both subsequent rating behavior as well as product sales.

Our modeling efforts are two fold. First, we model the arrival of product ratings and separate the effect of social influences from the underlying or baseline ratings behavior. Second, we model product sales as a function of posted product ratings. However, rather than simply modeling the effects of observed ratings, we decompose ratings into a baseline rating and the contribution of social influence. From this model, we can measure the overall sales impact resulting from observed social dynamics.

We show that ratings behavior is significantly affected by previously posted ratings. We further show that the effect on sales resulting from this social dynamic is significant but relatively small compared to the effect that ratings have when they represent an unbiased and independent evaluation of the product. With the increased popularity of online discussion and ratings forums, many marketers have been investing in efforts to moderate these conversations or to contribute comments of their own, effectively biasing the sentiments expressed in the online forum. Our results show that while these efforts can affect sales, their effects are limited and short-lived.

User-Generated Content, Online Consumer Product Ratings, Social Dynamics