Geo-Influence: Modeling Location-Specific Effects of Social Influence on Brand Preferences

Hyunhwan “Aiden” Lee, Joseph Johnson, Gerard J. Tellis. “Geo-Influence: Modeling Location-Specific Effects of Social Influence on Brand Preferences”

http://geoinfluence.net

  • Submitted to Marketing Science
  • The 41st Annual ISMS Marketing Science Conference, June 20 – 22, 2019, Rome, Italy
  • The 2019 Haring Symposium, April 19 – 20, 2019, Bloomington, IN.
  • The 1st Interdisciplinary Research Cluster Day, April 1, 2019, Coral Gables, FL.
  • The 3rd Annual Three Minute Thesis Competition, Feb 6, 2019, Coral Gables, FL.
No brand is the market leader uniformly across geographic locations. Brands have strong and weak markets. Therefore, analysts need to track brand preferences by location. However, current methods of tracking geographic brand preferences (e.g., surveys, company sales, scanner panel data, Google Analytics, etc.) have limitations: limited sampling of brands, stores, and categories with time and region aggregation. We address these limitations through a spatial, nonparametric approach to track brand preferences at the micro-temporal and micro-geographic level using Twitter. Our approach inherits the intrinsic strengths of social media: micro-temporal, micro-geographic, faster than news media, higher frequency than sales, low cost, competitive monitoring, sentiment inclusion, and social network inclusion. A key feature of our approach is modeling the temporal geographic influence of social connections based on the followers of each post. We estimate our model for carbonated soft drinks brands. Our validation uses Pepsi sales, an apparel fashion brand, and the 2016 U.S. presidential election (a domain where marketing is critical). We find that geographic influence adds predictive power to micro-temporal and micro-geographic brand measures, leads online sales and predicts elections. Our study implies that the micro-temporal and micro-geographic social media measures provide actionable insights for marketing brands and political candidates.

Keywords: micro-temporal brand preference, micro-geographic brand preference, sentiment analysis, Twitter, geographic influence model, election prediction

Brand Hazard: A Prognostic Complement to Customer-Based Brand Equity

Joseph Johnson, Debanjan Mitra, Sivaramakrishnan Siddarth, Hyunhwan “Aiden” Lee

  • Revision invited, Journal of Marketing

Existing customer-based brand equity metrics are built on customers’ choices and preferences and not on the dynamics inherent in their brand switching and timing, which potentially handicaps the prognostic ability of such metrics.   To remedy this we develop a complementary metric that focuses on the rate of change in customers’ purchase transitions. We use the counting process to model the repeat and switching rates of a brand, parse out baselines for each transition rate, cumulate these baselines over a given duration, and combine them to obtain brand hazard (BH). Using customer panel data, we estimate BH for six leading brands in three product categories over sixteen two-year moving windows.  We find increases in BH foretells future declines in the intercept of logit models of customers’ brand choice (BCI) – a customer based brand equity metric estimated from the same data. More important, we find that changes in BH and BCI are able to predict future changes in a brand’s price premium and revenue premium in the broader market.  Our findings imply that BH complements BCI, and tracking them together provides a comprehensive prognosis of a brand’s health.

Key Words:  Branding, Brand Equity, Brand Management, Hazard Models, Markov Process, Counting process