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

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

http://geoinfluence.net

  • Preparing for submission at Journal of Marketing Research
  • 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