More Than Just Great Food: Factors Influencing Customer Traffic in Restaurants Emily Moravec Megan Siems Christine Van Horn

Client Background  World Leader in Casual Dining  Several Casual Dining Brands  More than 1,500 Restaurants Worldwide  Restaurants located in more than 25countries  First location opened in 1991  Restaurant brand in study has 43 locations across the United States

Restaurant Locations New Restaurants

Multiple Linear Regression  Basic Equation Y = a + b 1 *X 1 + b 2 *X b p *X p + Error  Variables ◦ Dependent  Guest Count ◦ Independent  Marketing Campaigns  Pricing  Guest Satisfaction  Macroeconomic Factors

Linearity Check  Points should be symmetrically distributed around a diagonal line

MLR Results  Main Drive of Customer Traffic ◦ National eBlasts (marketing)  Main Drag of Customer Traffic ◦ Unemployment level (economy)  Concerned r 2 values are not strong  Remaining predictor variables were not significant in predicting customer traffic

Contribution of Significant Variables to Overall Percent Change in Guest Count Overall Percent Change in Guest Count: -3.74% 1HF09 vs. 1HF08

Data Envelopment Analysis  Integrates multiple input and output variables  Calculates a single efficiency index

DEA Simple Example eBlasts Sent Guest Count

DEA Specifics  Four different models: BCC  Two different orientations: Input  Four different scaling options: Geometric Mean  Constraints  Outputs ◦ Status, Level, Efficiency Rating, Multipliers Value, Observed and Ideal Values, and Reference Set

DEA Best Predictors of Marketing Efficiency  Input: ◦ Loyalty Composite Score ◦ Number of eBlasts sent ◦ radio TRPs ◦ local unemployment level  Output: ◦ Guest Count ◦ Net Sales

Most Efficient Least Efficient

 Multiple Linear Regression Analysis ◦ Main Drive: National eBlasts (marketing) ◦ Main Drag: Unemployment level (economy) ◦ Weak r2 values  Data Envelopment Analysis ◦ Best Input Predictors:  Loyalty Composite Score, Number of eBlasts sent, radio TRPs, local unemployment level ◦ Best Output Predictors:  Guest Count and Net Sales

 Multiple Linear Regression Analysis ◦ Unexplained decrease in guest count  Look into other variables such as location, competitors, and changes in price  Data Envelopment Analysis ◦ Client can look at DEA output and adjust marketing strategies accordingly ◦ Variables in DEA were not previously determined to be main predictors of marketing efficiency  Conduct an independent to evaluate main predictors

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