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More Than Just Great Food: Factors Influencing Customer Traffic in Restaurants

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

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More Than Just Great Food: Factors Influencing Customer Traffic in Restaurants

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

  2. 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

  3. Restaurant Locations New Restaurants

  4. 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

  5. Linearity Check Points should be symmetrically distributed around a diagonal line

  6. MLR Results • Main Drive of Customer Traffic • National eBlasts (marketing) • Main Drag of Customer Traffic • Unemployment level (economy) • Concerned r2values are not strong • Remaining predictor variables were not significant in predicting customer traffic

  7. Summary of MLR Models

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

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

  10. DEA Simple Example Guest Count eBlasts Sent

  11. 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

  12. DEA Best Predictors of Marketing Efficiency • Input: • Loyalty Composite Score • Number of eBlastssent • radio TRPs • local unemployment level • Output: • Guest Count • Net Sales

  13. DEA Results Most Efficient Least Efficient

  14. Conclusions • 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

  15. Recommendations • 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

  16. Questions? Hungry yet?

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