Post on 30-Dec-2015
description
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
Gu
est
Cou
nt
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
Gu
est
Cou
nt
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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Questions?