Twitter-patter: how social media drives foot traffic to retail stores

This paper answers how changes in social media activity influence customers to visit nationally known, brick-and-mortar retail stores. We consider seven measures of social media activity within a Social Impact Theory framework and test under what context does online chatter about a brand lead to higher foot traffic to those brand stores. We use hierarchical linear regression to account for the random effects of brand and store heterogeneity, which is superior to ordinary linear regression. Despite wide variation, when brand mentions increase by one standard deviation—either in likes or disagreement—then next-day foot traffic to stores of that brand will increase by 0.04 standard deviations (3–4%). This modest but meaningful effect, however, fully dissipates within 1 week. The weak cross-brand effects show that social media has distinct and larger influence on brands individually rather than universally. Our approach is novel due to (1) the large scale of data, (2) the breadth of analysis, (3) the multi-level specification, and (4) in estimating global elasticities between changes in electronic word-of-mouth (WoM) communication about brands and changes in store visits of those brands.