Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp
98 Pages Posted: 31 Jan 2018 Last revised: 30 Nov 2021
Date Written: October 28, 2021
Despite the substantial economic impact of the restaurant industry, large-scale empirical research on restaurant survival has been sparse. We investigate whether consumer-posted photos can serve as a leading indicator of restaurant survival above and beyond reviews, firm characteristics, competitive landscape, and macro conditions. We employ machine learning techniques to extract features from 755,758 photos and 1,121,069 reviews posted on Yelp between 2004 and 2015 for 17,719 U.S. restaurants. We also collect data on restaurant characteristics (e.g., cuisine type; price level), competitive landscape, as well as entry and exit (if applicable) time from each restaurant’s Yelp/Facebook page, own website, or Google search engine. Using a predictive XGBoost algorithm, we find that consumer-posted photos are strong predictors of restaurant survival. Interestingly, the informativeness of photos (e.g., the proportion of food photos) relates more to restaurant survival than do photographic attributes (e.g., composition; brightness). Additionally, photos carry more predictive power for independent, young or mid-aged, and medium-priced restaurants. Assuming that restaurant owners possess no knowledge about future photos and reviews, photos can predict restaurant survival for up to three years, while reviews are only predictive for one year. We further employ causal forests to facilitate the interpretation of our predictive results. Among photo content variables, the proportion of food photos has the largest positive association with restaurant survival, followed by proportions of outside and interior photos. Among others, the proportion of photos with helpful votes also positively relates to restaurant survival.
Keywords: marketing analytics, restaurant survival, consumer posted photo, image recognition, computer vision, consumer shared reviews, text mining, deep learning, convolutional neural network, yelp
JEL Classification: A10, C10, C13, C41, C45, C50, C53, C55, D00, E00, E02, L10, L25, M10, M21, M31
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