A data-driven approach to measure restaurant performance by combining online reviews with historical sales data

Fernando Batista,Elizabeth Fernandes,Paulo Cortez,Ricardo Ribeiro,Sérgio Moro

doi:10.1016/j.ijhm.2020.102830

Abstract

• Decision support for restaurant managers using online reviews and sales data. • Impact on sales forecast is assessed through a dashboard. • Sales forecast model based on TripAdvisor data and the Bass Emotion model. • Restaurant experts highlighted the time saved in the decision-making process. Restaurant management requires customer responsiveness to deal with increasingly higher expectations and market competitiveness. This study proposes an approach to simplify the decision-making process of restaurant managers by combining both live social media customer feedback and historical sales data in a sales forecast model (based on TripAdvisor data and the Bass model). Our approach was validated with internal and external (i.e., online reviews) data gathered from six restaurants. The collected data was processed using data analytics for developing a dashboard that provides value for restauranteurs by taking advantage of online reviews and sales forecast. Such dashboard was evaluated by restaurant management experts, which provided positive feedback, highlighting in particular the time saved in the decision-making process.

Highlights

Revenue management enables to optimize businesses toward revenue maximization by understanding consumer behavior and implementing a product and price strategy (Kimes, 1999)
While restaurateurs already have customer information from social networks and business performance key performance indicator (KPI)’s into the same system, the combination of a forecast model with social media feedback has no precedent in the foodservice literature
Contributions and implications In terms of theoretical implications, an innovative aspect of our research is that this study proposes a sales forecast model that takes advantage of both revenue and social media feedback information to foresee customers’ behavior within the managed restaurants

Summary

Introduction

Revenue management enables to optimize businesses toward revenue maximization by understanding consumer behavior and implementing a product and price strategy (Kimes, 1999). The number of customers that arrive at a given hospitality unit is a key input to forecast revenue (Weatherford & Kimes, 2003). Restaurant researchers and practitioners devised models based on the number of customers per time unit (Heo, 2017). The Bass model (BM) has been widely adopted to forecast demand by predicting the number of new customers in the forthcoming period (Mahajan et al, 1991). Restaurant customers, whether they are innovators or imitators (according to the BM), may lead to successive increases in the number of new customers, which can be modeled through the BM (Sultan et al, 1990). Within our knowledge, there are no research studies that adopted the BM to restaurant management

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