More Than Just Great Food: Factors Influencing Customer Traffic in Restaurants
Emily Moravec
Megan Siems
Christine Van Horn
Table of Contents
Table of Figures 4
Table of Tables 4
1 Management Summary 5
1.1 Problem Situation 5
1.2 Method of Analysis 5
1.3 Findings 5
2 Background and Description 6
2.1 Company Background 6
2.2 Problem Scenario 6
2.3 Decisions Involved 6
2.4 Questions to Be Answered 7
3 Initial Stages 8
3.1 Beginning Steps 8
3.2 Sources of the Data 9
3.3 Simplifying Assumptions 9
4 Multiple Linear Regression Analysis using SPSS 10
4.1 Regression Equation 10
4.2 Dependent Variable 11
4.3 Predictor Variables 11
Marketing Campaigns 11
Pricing 12
Guest Satisfaction 12
Macroeconomic Factors 12
4.4 Assumptions 13
Linearity 13
Independence 15
Homoscedasticity 15
Normality 16
4.5 Analyzing Output 18
Adjusted R2 18
ANOVA (Analysis of Variance) 19
Standardized Beta Coefficients 19
Partial Correlations 19
4.6 Results 19
4.7 Interpretation and Conclusion 21
5 Data Envelopment Analysis 22
5.1 Equations 22
5.2 Initial Decisions 24
Model 24
Orientation 24
Scaling 26
5.3 Predictor Variables 26
5.4 Output Variables 27
5.5 Analyzing Output 27
5.6 Results 29
DEA Summary Report 29
DEA Detail Report 30
Efficient DMU Weights 31
Inefficient DMU Reference Set 31
Inefficient DMU Variable Values 31
Application of Results 32
5.7 Assumptions and Suggestions 32
Predictor Variables 32
Constraints 33
5.8 Conclusion 33
6 Conclusions and Critique 34
6.1 Summary of Results 34
Multiple Linear Regression Analysis 34
Data Envelopment Analysis 34
6.2 Recommendations to Management 34
Multiple Linear Regression Analysis 34
Data Envelopment Analysis 35
6.3 Self-Critique 35
Multiple Linear Regression Analysis 35
Data Envelopment Analysis 35
6.4 Suggestions for further Study 35
Multiple Linear Regression Analysis 35
Data Envelopment Analysis 36
Appendix A 37
Graphical Representation of Restaurant Locations 37
Appendix B 38
Multiple Linear Regression Steps in SPSS 38
Appendix C 39
Steps to Calculate Percent Changes in Waterfall Chart 39
Appendix D 40
Appendix E 41
Appendix F 42
SPSS output for Regression Models 42
Appendix G 46
DEA input* 46
Works Cited 47
Table of Figures
Figure 1 Test for Linearity 14
Figure 2 Test of Homoscedasticity 15
Figure 3 Test of Normality with Q-Q Plot 16
Figure 4 Test of Normality with Histogram 17
Figure 5 Waterfall Chart for the Overall Percent Change in Guest Count 20
Figure 6 DEA Example Results 20
Figure 7 Map of Restaurant Locations 20
Table of Tables
Table 1 DEA Summary Report Output 20
Table 2 DEA Detail Report Output 20
Table 3 Caculations for Waterfall Chart 20
Table 4 Summary of Multiple Linear Regression Results 20
Table 5 SPSS Output- Adjusted R Square Summary of Each Regression Model 20
Table 6 SPSS Output- ANOVA Summary of Each Regression Model 20
Table 7 SPSS Output- Summary of Coefficents for Each Regression Model 20
1 Management Summary
1.1 Problem Situation
Although our client is a world leader in casual dining, the company experienced a decrease in overall customer traffic from one year to the next at one of the specific casual dining brands. As the company invested time and money into marketing campaigns for this restaurant chain throughout the year, they were interested in what caused the overall percent change in customer traffic from the first half of fiscal year 2008 (from now on referred to as 1HF08) to the first half of the fiscal year 2009 (1HF09) to be
-3.74%.
1.2 Method of Analysis
To examine this issue, our team used a multiple linear regression analysis to analyze the effects of marketing campaigns as well as economic factors inflencing customer traffic. By comparing the marketing campaigns and economic factors of 1HF08 and 1HF09, our goal was to find what variables had the most significant effect on either driving or dragging customer traffic. As an additional investigation, through a data envelopment analysis (DEA), our team analyzed the efficiency of each specific restaurant in terms of marketing.
1.3 Findings
From the multiple linear regression analysis, the main drive of customer traffic at the restaurants was found to be the marketing campaign of national eBlasts and the main drag of customer traffic was found to be unemployment level, or basically the declining economy. When the data envelopment analysis was performed, six restaurants were found to be performing at the most efficient level. Output results for the DEA provide data for how much to change the marketing variables so the restaurants not performing at top efficiency can reach this level.
2 Background and Description
2.1 Company Background
As a world leader in casual dining, the company has several leading casual dining brands, more than 1,500 restaurants, located in more than 25 countries, and employs over 100,000 team members. For our study, we are focusing on one of the company’s casual dining brands. This restaurant chain has 43 locations across the United States. The first location of this restaurant chain opened in 1991, and the brand has been successfully running for 18 years.
2.2 Problem Scenario
The purpose of our study was to find the factors influencing a decline in guest count from the first half of fiscal 2008 (1HF08) to the first half of fiscal 2009 (1HF09) at one of the company’s specific restaurant brands, despite the increase in marketing dollars spent. A map of the restaurant locations for this brand can be found in Appendix A.
The overall goal of our analysis was to find the factors which had a significant effect on the drives and drags of customer traffic between 1HF08 and 1HF09. By comparing data from 1HF08 and 1HF09, we analyzed what marketing campaigns and economic variavble had a significant impact on customer traffic for this restaurant chain.
2.3 Decisions Involved
There were many questions to be considered in our study. First, we had to decide which variables to analyze from the data given to us by the company, as well as the economic variables to consider. With the company conducting multiple marketing campaigns throughout the year, we needed to decide which marketing campaigns would provide us with the best data to predict future trends. A detailed explanation of the dependent and independent variables chosen for our analysis can be found under the multiple linear regression analysis discussed in section 4. Once our team finalized the variables to include in our study, the appropriate statistical program to analyze the data had to be decided upon. The statistical program SPSS was decided upon due to the ease of use of the interface and the ability to perform a multiple linear regression analysis.
2.4 Questions to Be Answered
The main question to answer was determining which variables were influencing the overall decrease in customer traffic. The multiple linear regression analysis was able to help us answer this question by discovering the main drive and main drag of customer traffic. Using the DEA Analysis, we were also interested in analyzing which locations were performing the most efficiently during the time period of interest. Furthermore, this analysis allowed us to figure out what the non-efficient restaurants need to do in order to increase efficiency. The findings from our study will show our client’s marketing department which marketing campaigns were successful, which locations benefited the most, and where to spend most of the marketing dollars in the future.
3 Initial Stages
3.1 Beginning Steps
Our team first met with our client in late January 2009 to discuss the plan for the project as well as a timeline for the semester. The first two steps included deciding whether we planned to analyze the broad or specific factors influencing customer traffic and whether to use SPSS or SAS statistical programs to analyze our data set. We decided on analyzing the broad factors of the drives and drags of customer traffic as well as SPSS as our predictive analytics software. SPPS was chosen to run the linear regression over the SAS program because of its easy to use interface. After obtaining the needed company data we began organizing the data by restaurant name and city, as well as fiscal year, fiscal month, and fiscal week. We then added variables provided to us by our client, including the following: guest count, net sales, per person average, gift certificate sales, loyalty composite scores, online campaigns, local and national eBlast campaigns, and radio campaigns. A detailed explanation of these variable can be found in the multiple linear regression analysis in section 4. All the data was compiled into a master excel spreadsheet and the economic factors and weather data was added as we found the data online. The economic factors included were the Dow Jones Industrial Average, unemployment level by state, and the Consumer Confidence Index.
Once all the variables were added to the master spreadsheet, we began working with SPSS. Indicator variables needed to be added to the data, and the format of the data had to be altered slightly in order to effectively run in SPSS. The descriptive statistics were then checked in order to make sure there were no missing data points. Assumptions for the regressions were then checked in SPSS so as to be able to proceed with the regression analysis.
Based on the data that had been gathered for the SPSS model, we found it interesting to investigate which restaurants made the most efficient use of all the input variables. When initially meeting with our client, this analysis was not specifically requested of us. However, we decided that the Data Envelopment Analysis should be performed and would add value to our client. Because no one on our team had experience in running this analysis, much background information about the program was researched before it was used.
3.2 Sources of the Data
The company provided us with the data about the different marketing campaigns including when the campaigns were ran and at which locations. The economic variables were not provided by the clients, and were researched on the internet: the unemployment rates were found on the Bureau of Labor Statistics website, the weather data on the WxUSA website, the Consumer Confidence Index at pollingreport.com, and the Dow Jones Industrial Average was found on Google Finance. Furthermore, our client provided us with data from a similar study that was conducted last year by an outside consulting firm. This data was in the form of a PowerPoint presentation that was presented to our client last year as their final deliverable. We used this data to form a realistic hypothesis and as a starting point for our analysis.
3.3 Simplifying Assumptions
Our team decided to simplify the three economic variables into one variable for analysis because we found our initial testings were all correlated with a decline in the economy. Out of these three variables, only the unemployment level was chosen in our analysis as it was found at a local level, rather than the national level which provides more detail in explaining differences in the regression model. Along with the economic variables we also simplified the online data. The online data was first broken down by each individual campaign as a separate variable. Our group is only looking at the impact of marketing campaigns as a whole, rather than on the impact of each campaign separately, so we were able to combine all the online variables into one variable in order to simplify the analysis.
4 Multiple Linear Regression Analysis using SPSS
Our client’s primary interest was finding the drivers and drags of customer traffic at the company’s restaurants. More specifically our client wanted to compare two time periods of data (1HF08 to 1HF09) to find what variables caused the overall percent change in guest count to be -3.74%.
4.1 Regression Equation
For this analysis, our client felt that a multiple linear regression would be the best method to utilize in our analysis to predict customer traffic based on a set of various predictor variables. The outcome of a multiple linear regression is a linear equation of the following form:
Y = a + b1*X1 + b2*X2 + ... + bp*Xp + Error
In this equation, ‘Y’ represents the dependent variable, ‘X’ represents each predictor variable, ‘p’ represents the number of predictor variables, ‘a’ represents the y-intercept of the dependent variable, and the ‘b’ coefficients represent the value for each predictor that must be increased or decreased to increase the dependent variable by one. The beta coefficients (‘b’) can be either positive or negative. A basic equation for our analysis will take the following form:
Guest Count = a + b1(Marketing) + b2(Pricing) + b3(Guest Satisfaction) + b4(Macroeconomic) + Error
Thus, by having values for ‘a’ and ‘b’ in the regression equation, the guest count can be predicted on the set of predictor variables. The b coefficients in the regression equation can then be standardized to find the relative importance of each predictor variable in explaining guest count. This information can be used to figure out the main drivers and drags of customer traffic in the different time periods. In short, the key benefits from a multiple linear regression analysis include “(1) the prediction of values on a criterion variable based on a knowledge of values on predictor variables,” and “(2) the assessment of the relative degree to which each predictor variable accounts for the variance in the criterion variable” (Kachigan).
4.2 Dependent Variable
As our client was interested in the change in customer traffic, the variable of guest count is used as the dependent variable. Our client was able to provide the data for guest count for the different time periods of interest.
4.3 Predictor Variables
Based on the previous study on customer traffic by a consulting company, our client asked us to use certain predictor variables in the linear regression analysis. The predictor variables we were asked to investigate are marketing campaigns, pricing, guest satisfaction, and macroeconomic factors. As these are very broad, we narrowed each variable down to more specific predictors.
Marketing Campaigns
Although there are many different types of marketing campaigns throughout each time period of interest, our client asked us to look at the few specific campaigns he felt were the most effective. These marketing campaigns are as follows: online campaigns, radio campaigns, and national and local eBlasts. The online campaigns consist of advertising ads on the web, either on the company website or on other websites. This type of marketing campaign was measured in the number of clicks, or impressions, on the ads. Radio campaigns consist of radio advertisements and were measured in TRP, target rating points. The national and local eBlasts, or “email blasts,” consist of advertising emails sent directly to potential customers. These eBlasts were measured in the number of emails that were actually opened. It is important to note that for the regressions, indicator variables were created for each of the marketing campaigns. These indicator variables are binary, with a 1 representing that the campaign ran in a given week, and a 0 indicating that the campaign did not run in a given week. These indicator variables were used in the regression analysis as they were indicative of the effectiveness of when a campaign was run versus when it was not run.