Guest Satisfaction and Restaurant Performance

Abstract

This study demonstrates a methodology to quantify the links between customer satisfaction, repeat-purchase intentions, and restaurant performance. Using data from a national restaurant chain, the authors constructed a series of mathematical models that predict how the level of customer satisfaction with certain attributes of guests' dining experience affects the likelihood that they will come back. In turn, the model shows how guests' “comeback” scores and other variables affect restaurant performance (i.e., sales and entrée counts). Robust and statistically significant, the models showed that restaurants that pay attention to food quality, appropriate cost, and attentive service have the greatest chance to increase guests' intent to return. In turn, that intent to return is a chief driver of increased sales.