Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp

74 Pages Posted: 31 Jan 2018 Last revised: 28 Oct 2019

See all articles by Mengxia Zhang

Mengxia Zhang

University of Southern California

Lan Luo

University of Southern California

Date Written: March 1, 2018


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 analyze 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 these restaurants’ characteristics (e.g., cuisine type; price level), competitive landscape, and their entry and exit (if applicable) time based on each restaurant’s Yelp/Facebook page, own website, or the Google search engine. Using a predictive XGBoost model, we find that photos are more predictive of restaurant survival than are reviews. Interestingly, the information content (e.g., number of photos with food items served) and helpful votes received by these photos relate more to restaurant survival than do photographic attributes (e.g., composition or brightness). Additionally, photos carry more predictive power for independent, mid-aged, and medium-priced restaurants. Assuming that restaurant owners do not possess any knowledge about future photos and reviews for both themselves and their competitors, photos can predict restaurant survival for up to three years, while reviews are only informative for one year. We further employ causal forests to facilitate interpretation of our predictive results. Our analysis suggests that, among others, the total volume of user-generated content (including photos and reviews) and helpful votes of photos are both positively related 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

Suggested Citation

Zhang, Mengxia and Luo, Lan, Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp (March 1, 2018). Available at SSRN: or

Mengxia Zhang

University of Southern California ( email )

2250 Alcazar Street
Los Angeles, CA 90089
United States

Lan Luo (Contact Author)

University of Southern California ( email )

Hoffman Hall 319
Los Angeles, CA 90089-1427
United States

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