Assessing Brand Perceptions with Social Media. David A. Schweidel (Emory University) Wendy W. Moe (University of Maryland)

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1 Assessing Brand Perceptions with Social Media David A. Schweidel (Emory University) Wendy W. Moe (University of Maryland)

2 Agenda Can we trust social media data? Social media and brand health Assessing the competitive landscape with social media

3 Why We Question Social Media Data Pre-Purchase Evaluation Purchase Decision and Product Experience Post-Purchase Evaluation Opinion Formation Do I Post? What Do I Post? SELECTION EFFECT Posted Product Ratings ADJUSTMENT EFFECT Opinion Expression

4 How Online Opinion Forums Evolve Variance Average Activists Post frequently Attracted by lack of consensus More negative Variance and volume make them more negative Low Involvements Post infrequently Deterred by lack of consensus More positive Variance and volume make them more positive Source: WW Moe and DA Schweidel (2012), Online Product Opinions: Incidence, Evaluation and Evolution, Marketing Science

5 Can We Use Social Media to Track Brand Health? 100% Proportion of Positive Comments 80% 60% 40% 20% Blog Forum Microblog Aggregate Correlation with offline brand tracking survey Venue Correlation 0% Observation Month Blogs.197 Forums Distribution of Comments 100% 80% 60% 40% 20% 0% Observation Month Blog Forum Microblog Other Microblogs -394 Average.008 Source: DA Schweidel and WW Moe (2014), Listening in on Social Media: A Joint Model of Sentiment and Venue Format Choice, Journal of Marketing Research

6 Linking Social Media to Offline Metrics Potential for GBI as a lead indicator GBI Month of Overlap Period (t) Average Survey Response Correlation with survey (t) GBI =.376 Avg sentiment =.008 Blogs =.197 Forums = Microblogs =.394 GBI in month t-1 Survey in month t Correlation with survey (t-1) GBI =.881 Avg sentiment =.169 Blogs =.529 Forums =.213 Microblogs =.722

7 Linking Social Media to Offline Metrics Coeff StdErr p-val Constant S&P* GBI(t) S&P Index GBI(t-1) GBI Lagged GBI Adj R-sq.475 * Closing price in month

8 Assessing the Competitive Landscape Using Social Media Data Social Media Metrics Volume of mentions of Brand i Comentions of Brands i and j Sentiment of mentions Incorporate a latent space model where brands are plotted on a latent map. Metric Volume of mentions of Brand i Comentions of Brand i and Brand j Sentiment toward Brand i Map Location Brand i s distance from origin Distance between Brand i and Brand j Location on Brand i on dimension t Potential applications Benchmarking metrics against competitors Dynamics over time

9 Data Top 20 Quick Serve Restaurants (QSR) from The QSR 50 6 months of Twitter mentions from Crimson Hexagon (July to December 2013) Data collected at weekly level Number of mentions of each brand Number of comentions Sentiment associated with (solo) brand mentions (number of positive, neutral or negative comments) Assume that the total number of potential posts in each week is 1.5 million ( number of weekly posts mentioning any of the QSR 50)

10 Social Media-Inferred Latent Space Taco Bell Subway Panera Chick Fil A KFC Wendy's BK Jack Arby's in the Box Sonic Little DQ CarlsHardees Caesar Dominos Papa John's Dunkin Pizza Hut McD Chipotle SBUX * Based on July-December 2013

11 Social Media-Inferred Latent Space KFC Wendy's BK Jack in the Box Arby's DQ Sonic Little Caesar CarlsHardees Dominos Papa John's Panera Dunkin Pizza Hut Chick Fil A

12 Holdout Analysis: Benchmarking Sentiment Brand Actual mentions Actual comentions Predicted Sentiment Actual Sentiment McDonald's 110,049 3, Subway 49,366 2, Starbucks 140,616 4, Wendy's 17, Burger King 12, Taco Bell 78,214 2, Dunkin Donuts 12, Pizza Hut 12, Chick-fil-A 25,475 1, KFC 11, Panera Bread 16, Sonic Drive-In Dominos 7, Carl's Jr./Hardee's 3, Chipotle Mexican Grill 57,010 2, Jack in the Box 5, Arby's 4, Little Caesars 2, Dairy Queen 4, Papa Johns 5, McDonald s is outperforming benchmark sentiment given volume of mention and co-mention activity. Burger King reported a problem with a meat supplier. Company president made headlines with comments regarding gay marriage. Panera launches new community cafes.

13 Financial Analysis ln R bt CONSTANT b b D 1 b b where R is revenue, D is the differentiation metric 2 D 2 bt Term Estimate Std Error p-value Term Estimate Std Error p-value D t D t D t D t BIANNUAL Adj R QUARTERLY Adj R

14 Conclusions Individuals social media contributions are subject to known biases Accounting for factors that contribute to these biases, social media can serve as a proxy for brand health Looking at the competitive landscape via social media suggests that brands that are differentiated on social media have stronger financial performance

Marketing Spend 2015 Report Highlights Conclusions Table of Contents Page Marketing Spend Overview by Chain

Marketing Spend 2015 Report Highlights Conclusions Table of Contents Page Marketing Spend Overview by Chain Industry Data Report Spend 2015 Report Highlights budget overviews by segment and chain. Changes in national and local/co-op spending allocations. Creative ad agency roster changes. Conclusions Aggregate