Guest satisfaction and restaurant performance.(analysis of restaurant management)

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 entree 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.

Keywords: customer satisfaction; restaurant performance; service-profit chain; guest intention to return

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Companies and organizations in virtually every industry employ customer-satisfaction measures for the straightforward reason that satisfied customers are essential for a successful business. Despite what seems like agreement on the importance of customer satisfaction, however, there is little consensus on the details of what constitutes satisfaction or even how to quantify the difference customer satisfaction makes. Also in debate are how customer satisfaction should be measured, with what frequency, and at what level of aggregation, as well as how such measures are or should be linked with a firm's performance. What is more, some empirical evidence suggests that the relationships between customer satisfaction, customer loyalty (repeat business), and a firm's performance are tenuous at best.

The study described in this article attempts to address the key issue in customer satisfaction, namely, the relationships between customer satisfaction, customers' repeat-purchase intentions, and restaurant performance. Much research, both theoretical and empirical, has examined how customer satisfaction may be related to organizational goals and business performance. In this study, we employ a large data set from a national restaurant chain to construct models that describe the factors that influence customers' likelihood of repeat purchase. We then link this purchase likelihood, along with other variables, to restaurant sales.

Linking Customer Satisfaction with Performance

The relationships we study are part of a framework referred to as the service-profit chain (this concept was developed by Heskett et al. 2004). In this framework there are certain attributes of the dining experience that affect customer satisfaction. Next, higher customer satisfaction should lead to increased probability of repeat purchase, which in turn should result in greater restaurant sales. In this section, we review earlier work that measured the customer satisfaction and performance links in the restaurant sector.

The empirical literature on this topic with regard to restaurants dates from the past twenty years. A few studies were conducted in the late 1980s and the 1990s focusing mostly on attributes of the dining experience that determine customer satisfaction (see, e.g., Knutson 1988; Davis and Vollmann 1990; Dube, Renaghan, and Miller 1994; and Kivela, Inbakaran, and Reece 2000). More recently, however, researchers started addressing the links between customer satisfaction and performance, emphasizing the way satisfaction affects customers' repeat purchases (examples of recent contributions include Sulek and Hensley 2004; Soderlund and Ohman 2005; and Cheng 2005). Next, we review the main findings on the drivers of customer satisfaction, the links between such drivers and repeat-purchase intentions, and the influence of customer satisfaction on restaurant performance.

Drivers of Customer Satisfaction

Many researchers have explored the underlying factors that result in customer satisfaction. Knutson (1988) discussed principles that managers should follow to meet or exceed customer expectations, such as employee greeting, restaurant atmosphere, speed of service, and convenience. Fitzsimmons and Maurer (1991) constructed a managerial tool to measure the attributes driving customer satisfaction. Other studies have identified numerous factors that influence customer satisfaction with a dining experience, including waiting time, quality of service, responsiveness of front-line employees, menu variety, food prices, food quality, food-quality consistency, ambience of the facilities, and convenience (Davis and Vollmann 1990; Dube Renaghan, and Miller 1994; Kivela, Inbakaran, and Reece 2000; Sulek and Hensley 2004; Iglesias and Yague 2004; and Andaleeb and Conway 2006).

Customer Satisfaction and Repeat-Purchase Intentions

Determining satisfaction is not sufficient, however, because one needs also to establish the link between satisfaction and repeat purchases, which are an important source of restaurants' profits. Thus, studies have addressed the links between customer satisfaction with various restaurant attributes and repeat-purchase intentions (for instance, see Sulek and Hensley 2004; Soderlund and Ohman 2005; and Cheng 2005). While these studies often find strong links, the importance of a particular attribute varies according to the type of restaurant and the type of customer (for a detailed analysis, see Cheng 2005). For instance, food quality is the critical attribute influencing repeat-purchase intentions in full-service restaurants, while waiting time is the most important attribute in quick-service restaurants (research focusing on full-service restaurants includes Sulek and Hensley [2004] and Clark and Wood [1998]; research on fast-food restaurants is from Davis and Vollmann [1990]). When Kivela, Inbakaran, and Reece (2000) conducted an extensive survey of diners of various restaurants, they found that first and last impressions have the greatest impact on repeat-purchase intentions, followed by excellence in service and food quality. This literature concludes that different classes of restaurant businesses should implement different managerial strategies to compete and succeed (Cheng 2005). Most studies that show strong links between customer satisfaction and repeat-purchase intentions typically employ cross-sectional data. Nevertheless, marketing researchers argue that one should take into account the dynamic properties of such links (see, for example, Rust and Zahorik 1993; Bernhardt, Donthu, and Kennett 2000).

Repeat-Purchase Intentions and Sales Performance

The general conclusion of these studies is that higher levels of customer satisfaction lead to an increase in customers' repeat purchases and improved financial performance (Mittal and Kamakura 2001). However, evidence regarding the link between customer satisfaction and a restaurant's performance remains ambiguous. Anderson, Fornell, and Rust (1997), for instance, found no correlation between customer satisfaction and productivity in service firms as a group or among restaurants in particular. In contrast, Bernhardt, Donthu, and Kennett (2000) employed data from a national chain of quick-service restaurants and found a positive association between changes in customer satisfaction and changes in sales performance. They argued that researchers and managers should take into account the dynamic properties of this link because there is a time horizon for the influence of customer satisfaction on restaurant performance. Soderlund and Ohman (2005) found another dimension in addition to time. They concluded that the correlations between (1) repeat-purchase intentions and customer satisfaction and (2) repeat-purchase intentions and actual repeat purchases are sensitive to the particular measure of repeat-purchase intentions employed. Overall, the restaurant literature calls for further empirical research on the finks between customer satisfaction and firm performance (Soderlund and Ohman 2005).

In the study described in this article, we address at the same time all three elements of the link between customer satisfaction and performance, namely, customer satisfaction, repeat-purchase intentions, and firm performance. Our model considers the dynamic nature of the aforementioned relationships and identifies the lag structure among the three constructs. Finally, our study fills a gap in the empirical literature that focuses on the restaurant sector by linking customer satisfaction to restaurant performance.

Study Goals and Data Sources

We set out to determine the principal drivers of customer satisfaction in a restaurant chain and, subsequently, to determine how customer-satisfaction data can be most effectively used to improve the chain's performance. In particular, our goals were the following: (1) to identify the customer-experience attributes that cause customers to come back to a restaurant; (2) to prioritize those customer experience attributes in terms of their effect on customers' likelihood to come back; and (3) to identify the relationships between likelihood to come back, and guest count or restaurant sales, and quantify the effect of changes in "comeback" scores on restaurant performance.

We acquired a large data set from a national restaurant company that has more than three hundred outlets in locations covering roughly one-half of the United States. This company's three restaurant divisions record total sales of approximately $1,000,000 per day. This rich data set contained several distinct parts. First, we had data from more than eighty thousand guest surveys regarding guests' detailed and overall restaurant experience spanning the period September 2005 to April 2006. Second, the data set also contained detailed information on various indices of daily individual restaurant performance, such as guest counts, sales, and margin. Third, we collected data on a series of restaurant characteristics to refine our analysis for the three restaurant concepts, including number of restaurant seats, lot square footage, and building square footage. Fourth, we measured the available marketing activity during the time that our guest-satisfaction survey was conducted. Included here were weekly data on TV and radio advertising by market, direct marketing activity, number of free-standing inserts (FSIs), and outdoor marketing activity. Although we attempted to gather monthly data on unemployment rates, Consumer Price Index, and hourly wage rates, these data are not available at a level that coincides with the restaurants' locations, except forthe unemployment rate. Unemployment data are available by zip code from the U.S. Department of Labor, Bureau of Labor Statistics.

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