Crusty Pizza Restaurant CASE THREE
Crusty Pizza Restaurant: Forecasting using Regressions
Group One: Jenna Baseler and Zachary Kain
MBA 610-T304
Introduction
The purpose of this case is to determine which key variables drive Crusty Pizza Restaurant’s monthly profit and then forecast what the monthly profit would be for potential stores. Based off of this information we will be able to make a recommendation to Crusty Dough Pizza Restaurant on which stores they should open and which they avoid. The group was provided 60 restaurants’ data that included monthly profit, student population, advertising expenditures, parking spots, population within 20 miles, pizza varieties, and competitors within 15 miles. For the potential stores we were given all of this information sans the monthly profit. While identifying the key variables in the data set, we are able to discover the possible variables that predict the monthly profit of each of the proposed restaurants.
Monthly Advertising Expenditures
In previous research we found that monthly advertising expenditure was a large driver of profit, so we did further regression analysis to determine if there was a strong correlation (see Appendix A for detailed Regression Analysis Data). In crunching the numbers we found the coefficient of determination between advertising and monthly profit was 0.6564 with a very high level of confidence. We were able to find with the coefficient for the independent variable to monthly sales expenditures that with every dollar they spend on advertising they increase our monthly profit by $14. This has quite a large impact on profit considering that the average restaurant spends $1590.98 in advertising. So if a store boosted their advertising budget by $500 they would potentially see a $7,000 increase in their monthly profit. The scatter plot below shows the 60 stores we researched and their advertising expense as compared to their monthly profit. As you can see there is a positive trend in the amount invested and
Group One: Jenna Baseler and Zachary Kain
MBA 610-T304
Introduction
The purpose of this case is to determine which key variables drive Crusty Pizza Restaurant’s monthly profit and then forecast what the monthly profit would be for potential stores. Based off of this information we will be able to make a recommendation to Crusty Dough Pizza Restaurant on which stores they should open and which they avoid. The group was provided 60 restaurants’ data that included monthly profit, student population, advertising expenditures, parking spots, population within 20 miles, pizza varieties, and competitors within 15 miles. For the potential stores we were given all of this information sans the monthly profit. While identifying the key variables in the data set, we are able to discover the possible variables that predict the monthly profit of each of the proposed restaurants.
Monthly Advertising Expenditures
In previous research we found that monthly advertising expenditure was a large driver of profit, so we did further regression analysis to determine if there was a strong correlation (see Appendix A for detailed Regression Analysis Data). In crunching the numbers we found the coefficient of determination between advertising and monthly profit was 0.6564 with a very high level of confidence. We were able to find with the coefficient for the independent variable to monthly sales expenditures that with every dollar they spend on advertising they increase our monthly profit by $14. This has quite a large impact on profit considering that the average restaurant spends $1590.98 in advertising. So if a store boosted their advertising budget by $500 they would potentially see a $7,000 increase in their monthly profit. The scatter plot below shows the 60 stores we researched and their advertising expense as compared to their monthly profit. As you can see there is a positive trend in the amount invested and