# what Drives immediate and ongoing word of Mouth?

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5 Drivers of word of Mouth 873 Model Development We model WOM at the individual level, examining the number of conversations each agent had during a particular time period of a particular campaign. It is important that our model weights the data from both individuals and campaigns according to how many observations we have for each. We capture these two levels and differing amount of information using a hierarchical model (Gelman and Hill 2007). 6 In addition, we capture time dynamics of WOM by an agent within a campaign by splitting the campaign into two periods and examining how product characteristics have different effects on immediate and ongoing WOM (i.e., across those two periods). We use a Poisson log-normal model, a type of generalized linear mixed-effects models. Formally, each observation, y ijt, represents the number of conversations agent i had during time t of a campaign j for a particular product. Each of those observations has an unobserved Poisson rate of conversations, ijt. This rate parameter, or the expected number of conversations, is a log-linear combination of the global mean, each individual agent s talking propensity i, each product campaign s propensity to be talked about jt, and each observation-specific unobserved error ijt. That is, (1) y ijt Poisson ( ijt ) (2) log ( ijt ) = i + jt + ijt, where the product-campaign s propensity jt has both unobserved components j and observed components X j t, denoted by product campaign covariate vector X j and the coefficient vector t, which is common across agents but time specific: (3) jt = j + X j t The components of t reflect the relationships between the predictors and WOM for each time period. The parameters ijt, i, and j are unobserved random effects and vary across observations, agents, and campaigns, respectively. They are independently normally distributed: (4) ijt N(0, ), i N (0, ), and j N (0, ), where the parameter reflects the degree of unobserved heterogeneity across individual agents and reflects the degree of unobserved heterogeneity across campaigns (not accounted for by the impact of observed covariates X j t ). The parameter reflects the degree of overdispersion in the counts, conditional on the other parameters. This observation error, ijt, can be interpreted as the unobserved interaction effect of a particular person s propensity to talk about a particular product-campaign in a particular time period. As in random effects models, we assume the observed predictors are uncorrelated with the random effects. To investigate our key questions, we examine the coefficients representing the associations of each product characteristic and campaign giveaway with WOM. We study the temporal course of WOM by splitting each campaign into two nonoverlapping, consecutive periods (immediate and ongoing WOM). In particular, we examine the associations 6This approach stems from the classic item response theory model (Gulliksen 1950; Lord 1980). of each product characteristic with WOM over time. Our focal analyses treat the immediate time period as the first three weeks of the campaign and ongoing as the remaining part (typically about seven weeks). We use a discrete approach because it is simpler to interpret and the data are quite sparse for the continuous approach. That said, our results are robust to other ways of dividing the two time periods (e.g., defining the immediate part as the two weeks, four weeks, first half, first third, or first fourth a campaign). Overall WOM refers to the WOM over the whole length of the campaign. In summary, we model the counts of conversations at the level of an agent in a campaign during a given period. Using a multilevel modeling approach, we test how cues, visibility, and interest are linked to immediate, ongoing, and overall WOM. Results and Discussion Product characteristics. What types of products are talked about more? Our results indicate that being cued, publicly visible, and interesting all shape WOM but that these relationships vary over different time horizons. First, we examined overall WOM. Products that are cued more by the environment ( =.08, t = 3.17) or are more publicly visible ( =.06, t = 1.86) receive more overall WOM (Figure 1). The size of the coefficients suggest that compared with a product one standard deviation below the mean in cues, a product one standard deviation above the mean gets an average of 15% more WOM overall. Similarly, a product that is one standard deviation above the mean in public visibility gets an average of 8% more WOM overall. More interesting products, however, did not receive more overall WOM ( =.01, t =.37). The results are similar using other ways to measure interest (e.g., novelty, originality, surprise), casting doubt on the possibility that our findings are driven by the specific measure used. Next, we examined immediate and ongoing WOM. As predicted, both cues and public visibility are associated with figure 1 cued and PuBliclY ViSiBle ProDuctS get More overall wom, But More interesting ProDuctS Do not (cross-campaign analysis) Coefficients of WOM Predictors cues Public Visibility interesting Notes: This is the visualization of the table presenting the results from the whether model of overall WOM. The bars show the estimated coefficient value. The error bars show the 95% confidence interval for the coefficients.

10 878 Journal of Marketing research, october 2011 table 6 expected roi of ProDuct giveaways Purchases per Conversation Product Mailing Cost \$1.00 \$.00 \$1.00 \$3.00 \$2.00 \$1.00 \$.00 \$2.00 \$3.00 \$2.00 \$1.00 \$1.00 Notes: The table shows that expected ROI of a product giveaway is sensitive to mailing cost and the number of purchases that stem from each WOM conversation. long as more than one purchase stems from each initial WOM conversation. In contrast, if the mailing cost is \$2, the giveaway will only net positive ROI if two purchases stem from each conversation. 9 Although precise ROI of each WOM marketing campaign element is beyond the scope of our data, the framework we use here should be considered the first step for researchers and managers to run such a cost benefit analysis. Directions for Further Research We have examined how cues, visibility, and interest affect WOM, but other product characteristics matter as well. People may talk more about products that they like, for example, or those that signal their identity (Berger and Heath 2007; Wojnicki and Godes 2008). 10 It might also be useful to examine how interest, cues, and visibility affect other outcomes (e.g., WOM valence, quality, conversation length, conversation size, sales). For example, more interesting products may generate longer conversations or more positive WOM. Researchers might also examine the differential impact of immediate and ongoing WOM on diffusion. Although the overall amount of WOM a product receives depends heavily on it continuing to be discussed (ongoing WOM), immediate WOM should be particularly important for products that have short life cycles (e.g., movies), for which early sales affect later distribution (e.g., books) or an early installed base is important because of positive network effects (e.g., mobile phone network). A particularly rich area for further investigation is differences in drivers of online and offline (i.e., face-to-face) WOM. Prior work has mostly used online conversations, reviews, or content transmission to study WOM (Berger and Milkman 2011; Godes and Mayzlin 2004; Moe and Trusov 2011), but more than 75% of WOM actually occurs face-toface (Keller and Libai 2009). An important difference is that face-to-face interactions may have a lower threshold for discussion. It is awkward to have dinner with a friend in silence or ride in a cab with a colleague without conversing; therefore, few things will be deemed too boring to talk about. Consistent with this, most WOM reported to BzzAgent is face-to-face, and we find that more interesting products do not get more WOM overall. With online WOM, however, the threshold for discussion is often higher. Most decisions to post a review or share a news article are not driven by the 9These calculations consider the product s unit cost of production to be a sunk cost. 10We found no relationship between WOM and product price, for example, but there was a significant relationship with hedonicity, such that hedonic products received more WOM than more utilitarian ones. need to fill conversational space but by the belief that there is useful or interesting information to be passed along. More practically useful or surprising New York Times articles, for example, are more likely to make the most ed list (Berger and Milkman 2011). Consequently, factors such as interest or practical utility may have a greater impact on online transmission (Berger and Iyengar 2012). Another potential difference is whether WOM is affected by the temporal distance between an experience and talking about it. People often forward online content (e.g., articles, videos) soon after they find it, but face-to-face interactions often involve discussions about more distal experiences. Consequently, how top of mind various experiences are (i.e., accessibility in memory) may play a larger role in faceto-face versus online WOM. Online and offline WOM also differ in whether the decision is about what to share or with whom to share it. In online WOM, the content comes first (e.g., reading a given article), and people then decide whether and with whom to share it. In offline WOM, however, the discussion partner(s) usually comes first (e.g., talking to a certain people or group of people), and people then decide what to share. These different decisions could have a significant impact on WOM. In conclusion, WOM provides a fertile domain to integrate consumer psychology and marketing science. The emergence of social media and online WOM has provided a wealth of data on what consumers say, share, and do. While analyzing this data correctly requires an appropriate toolkit, it provides the opportunity to address a rich set of behaviorally and managerially relevant questions. Percent of Observations appendix a histogram of Data and additional MoDel results (cross-campaign analysis) 50% 40% 30% 20% 10% 0% 4,726 2,664 1, Conversations Notes: The histogram contains all observations from 2000 agents across 335 campaigns. Visualizing this informs the modeling approach. The two main models for the cross-campaign analysis ( whether and when models) involved multiple levels of unobserved heterogeneity. The most unobserved variability is captured by the observation level random effects ( =.77 for whether and.51 for when ), followed by the unobserved heterogeneity across agents ( =.69 for whether and.46 for when ). The remaining unobserved heterogeneity across campaigns not accounted for by the observed campaign-level factors was smallest ( =.27 for whether and.24 for when ). These hierarchical models were not estimated using fully Bayesian inference, rather by quasi-likelihood estimation technique involving a Laplace approximation (Bates 2010).

12 880 Journal of Marketing research, october 2011 Rosen, Emanuel (2009), The Anatomy of Buzz Revisited: Real-Life Lessons in Word-of-Mouth Marketing. New York: Doubleday. Ryu, Gangseog and Lawrence Feick (2007), A Penny for Your Thoughts: Referral Reward Programs and Referral Likelihood, Journal of Marketing, 71 (January), Sernovitz, Andy (2006), Word of Mouth Marketing: How Smart Companies Get People Talking. Chicago: Kaplan Publishing Trusov, Michael, Randolph E. Bucklin, and Koen Pauwels (2009), Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site, Journal of Marketing, 73 (September), Wojnicki, Andrea C. and David Godes (2008), Word-of-Mouth as Self-Enhancement, working paper, Rotman School of Management, University of Toronto. Wu, Fang and Bernardo Huberman (2007), Novelty and Collective Attention, Proceedings of the National Academy of Sciences, 105 (45), Wyer, Robert S. and Thomas K. Srull (1981), Category Accessibility: Some Theoretical and Empirical Issues Concerning the Processing of Social Stimulus Information, Journal of Experimental Social Psychology, 16 (4),

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