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 + b1*X1 + b2*X2 + ... + bp*Xp + Error Variables

◦ Dependent Guest Count

◦ Independent Marketing Campaigns Pricing Guest Satisfaction Macroeconomic Factors

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Guest Count Difference by Week(1HF09 - 1HF08)

Fiscal Week

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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 r2 values are not strong Remaining predictor variables were not

significant in predicting customer traffic

Summary of MLR Models

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

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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

DEA Results

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

Conclusions

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

Recommendations

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