Economics Burger King Case Study
The Atlanta Burger King chains are located In a highly completive fast-food market.
The company has been Investing money Into advertising every week and would like to evaluate how this has affected the sales of their “Combination 1” hamburger meals. By implementing the data received from the Burger King Manager, Price Waterman Coopers was able to analyze the data to give the manager an insight into the areas of the business that can be improved, such as sales. The information In this analysis is limited to only the data collected from the owner and does not examine the entire menu of the restaurant
Introduction Price Waterman Coopers has examined two Burger King Restaurants located in suburban Atlanta market, to evaluate the demand for the basic hamburger meal package, or “Combination 1” meals on their menus. This area is a highly competitive market with five other mostly well-known, fast food chains centrally located to these two restaurants. This analysis evaluates the Impact of the newspaper advertising expenses Incurred to their sales of these combination meals on a weekly basis for a period of one year, back in 1998.
Performance Measurement Each week the Burger King owner advertised a special price offer at these two exclusive restaurants In the dally newspaper.
For the calendar year of 1998, PWS studied household Income and the population In the suburb, but found that It did not warrant inclusion in the demand analysis. A scatter graph, a regression analysis, a t-test, and equations for elasticity were the data sources used from the data collected by the Burger King Manager.
The purpose of these data sources is to identify the impact of advertising expenses on the demand and price for these restaurants. Data Analysis and Interpretation The regression analysis aids in determining the values of the parameters for a unction to find the best fit of a set of data observations (www. BPCS.
Hrs. Gob). The t- test shows the statistical hypothesis about the mean of a population (www. BPCS. Hrs. Gob).
This is calculated to find the R-value. By comparing the P-value to the rejection region of=. 5 it can be seen that the P-Value is less than -. 05, so then reject ten null Ho Ana accept ten alternative Ha. In tens case, ten P-value Is highlighted above and is less than -05, so then reject the null and accept the alternative. This means that the model is useful and there is a relationship between weekly sales price and advertising of rutabagas.
The R Square, highlighted in green on the regression analysis, is 0. 26 which means that 26% of sales are explained by weekly sales in price and advertising.
This is not good because it is a low percentage, which means that there may be other factors that are affecting the demand. The scatter graph shows the linear equation for Combination 1 meals. The t-test shows the statistical hypothesis about the mean of a population (www.
BPCS. Hrs. Gob). Estimation of Demand Some of the determinants of demand that might be improved in estimating demand are the consumer’s income or access to credit, prices of related goods, nonuser’s tastes and preferences, and number of consumers in the market.
If consumer’s income increases, then demand increases and decreases with a decrease of consumer’s income. Prices of related goods would change the estimation if the prices of a related good decreases, then demand for hamburgers would decrease and if the price of related good increases, then it would increase the demand for our hamburgers.
Consumer’s tastes and preferences can also change demand. If a consumer decides that want to eat healthier, then they would decrease the demand of hamburgers. This would also decrease the number of consumers in the market.