BUS631: Brand Management

From the output of the topic models, we introduce temporal topic variability (TTV), which can help accurately "predict" brand preference shifts that occur in the future. TTV is a summary of changes in relationships between specific social media topics and a given brand, and we predict that these can affect future shifts in brand preferences. Thus, we empirically test the likelihood that shifts in topic–brand relationships will have an impact on future changes in brand preference. Note that the topic meanings are kept constant and the changes in relationship between specific topics and brands over time is what constitutes this metric. In other words, we address the following questions: if a brand such as Google observes high variability expressed in social media topics in period t (as measured by changes in topic probabilities associated with a brand from the previous period to the current period), will this result in a greater likelihood of brand preference shifts in period t + 1? Will these shifts be present even after we control for category differences?

To answer these questions, and to identify brand preference shifts, we integrated the social media data with brand outcomes data from Young & Rubicam's Brand Asset Valuator (BAV) survey for a future period (here, "future" refers to the period t + 1 vs. t). Using these brand outcomes as dependent variables, we regressed these against a current-period change in social media topic probabilities (we define the current period subsequently). We controlled for category random effects in our model.

We considered three brand outcomes: brand preferences, net promoter score, and percentage of lapsed users. Brand preferences are measured across the same set of brands in the BAV data, using a survey approach. Specifically, the BAV survey asks respondents to review a brand preference question and to select one of the four response categories regarding each brand ("the one I prefer," "one of several I’d consider," "would only consider when no alternative," or "would never consider"). We viewed those who selected the first two responses as those who prefer the brand, and the percentage of those who indicated preference formed the dependent variable. The net promoter score was based on the percentage of respondents who said that they would recommend the brand to a friend, and lapsed users were the percentage who indicated that they had lapsed in brand usage. If TTV can predict future shifts in these important brand outcomes, this would mean that our proposed approach functions as an early warning system to measure changes in a brand's positioning in the marketplace.

We gathered additional data from Twitter, using the same Twitter handles but going back to the 2011–2015 time frame. We used the same 100-topic solution previously generated and searched for topic terms that we had identified using the analysis described previously. We then examined the probabilities of topics for each brand and each year and calculated the average difference in probabilities for each year relative to the previous year (e.g., 2012 [vs. 2011], 2013 [vs. 2012], 2014 [vs. 2013], 2015 [vs. 2014]) for each of the 100 topics for each brand. We then took the sum of the absolute value of differences in topic probabilities. Our metric of brand–topic associations for brand x across topics k and time t is

$\text{TTV of brand x }=\sum_{\mathbf{k}=1}^K\left\{\sum_{\mathbf{t}}^{\mathrm{T}}\left[\operatorname{abs}\left(\text { topic } \mathbf{k} \text { prob }_{(\mathbf{t})}\right)-\left(\text { topic k pro } \mathrm{b}_{\mathrm{t-1}}\right)\right]\right\}$

(3)

Turning to the BAV data, we calculated changes in future brand preference as

$=\frac{\left[a b s\left(\text { brand preference }_{t+1}\right)-\left(\text { brand preferenc } \mathrm{e}_t\right)\right]}{2}$

(4)

Note that in this specification, TTV is regressed against a future change in brand preference (and change in net promoter scores, and change in percentage of lapsed users). We estimated the model using an instrumental variable regression using generalized method-of-moments specification (we use STATA's procedure to invoke an instrumental variable regression with generalized method-of-moments estimation and cluster-robust standard errors;). This specification included lagged change in brand preference as an endogenous regressor and used lagged changes in brand equity dimensions of BAV brand stature and BAV brand strength as instruments for change in brand preference (see Table WA2 in the Web Appendix for results). We used cluster robust standard errors to account for category effects. In this model, we found that TTV was a significant predictor of all three models (future change in brand preference: β = 45.09, p < .01; future change in net promoter score: β = 21.20, p < .01; future change in percentage of lapsed users: β = 18.84, p < .01). In an alternative specification, also estimated the model using maximum likelihood and used a fixed-effects specification to control for category effects. We found that the TTV indicator was a marginally significant predictor of future period brand preference volatility (β = 12.73, p = .06); it was not a significant predictor or future change in net promoter score (β = 5.79, n.s.) but was a significant predictor of future change in percentage of lapsed users (β = 13.54, p < .05).

Overall, the results of this analysis using TTV provides further evidence that identifying topics from social media and assessing how their associations with brands shift over time (while keeping the topic meanings the same) can help predict how a brand is likely to be perceived in the future. The results also confirm that the suggested approach to extracting brand image information from social media can be used as a predictive tool to forecast future shifts in key brand metrics such as future brand preferences and percentages of lapsed users. This type of predictive validation is a key step in establishing construct validity, in that our metric effectively correlates with other, known approaches to measurement of brand image that are costly and time-consuming. It provides insights into the nature of brand image by identifying topics of conversation that relate to a brand. In addition, the TTV concept provides a novel approach to predicting future shifts in brand image. In the next section, we outline various applications of the differential language analysis of social media topics as they relate to various brands and describe the implications for data-driven brand management decision making.