Proposed University-Domino’s Partnership
Managerial Economics and Globalization – ECO 550
Professor Pennington - Strayer University

The university and Domino’s Pizza are considering a joint venture for evening students. The assessment of the potential joint venture we will determine if there is enough demand as revealed from student surveys, to legitimize the proposal to sell pizzas on each campus each evening in a mutually beneficial fashion. For the university it would allow for students and faculty to dine and work together for the first hour of each class, and for Domino’s the potential gains include larger profits and expanded business opportunity. To collect information, every evening of the week students and faculty were asked to complete a survey to help determine if there is enough demand and how cost points may affect that demand. The demand and forecast for pizza will determine if it is justified to move forward with the proposed joint venture.
When completing a demand analysis it is important to test and review the proper demographic and independent variables. In the proposed University-Domino’s joint venture the surveys collected many data points. The demographic variables included students age, number of people in the household, and approximate household income. The independent variables included number of fast food restaurants passed, distance of commute, whether the student is coming to class straight from work, number of times fast food is eaten weekly, amount spent when eating fast food, and if a drink was purchased. The demand function, also known as the curve will specify the relationship between the various price points of pizza and quantity demanded.
It is also important justify the reasons certain variables are chosen. The first demographic variable is student’s age. This is an important variable because it will allow us to review the likely demand of pizza by age, thus eventually looking at the student body’s age and projecting demand from that. The number of people in the household is important, because perhaps married students, or those with children will have appreciably different fast food buying habits. Household income is also important because it can indicate whether wealthier people would tend to purchase Domino’s or perhaps have more refined taste. The independent variables such as the number of fast food restaurants passed, distance of commute and whether the student is coming to class straight from work all speak directly to the opportunity for the student to obtain substitute goods (specifically the time or opportunity to purchase other food) and how that would impact probability of purchasing pizza and at what price point. The last three independent variables, the number of times fast food is eaten weekly, the amount spent when eating fast food, and if a drink was purchased all speak to actual purchase habits of students and the impact of these variables then can again be indicative of pizza purchase and price point opportunities to determine potential profits.
The coefficient of determination (or R squared, R2) and is most often expressed as a number between 0 and 1.0, with 1.0 indicating the regression line fits the data well, and 0 indicating it does not fit data well. The coefficient of determination for price of pizza in this case is 0.72 (as indicated in excel: sheet ‘SimpleMOLS’, cell A116) and indicates that price point to quantity/demand is indeed a valid connection and the regression line would represent the data well. The regression statistics (as indicated in excel: sheet ‘LinRegI’, cells M 9 & 10) shows value of the regression as 0.9540, and an adjusted figure of 0.9463, again showing that the set and regression fit very well. The fact that the regression line can viewed as accurate or valid allows us to project with accuracy potential demand, and how the changes within each different variables could affect that demand, thus potential profit. Since the data set is good and regression is within acceptable parameters we can use these projections with a good amount of certainty.
We also have to review the statistical significance of the regression and the individual variables to quantify how helpful these variables may be when determining if the proposed partnership is worthwhile. To do this we compare the cells from 114 over 115 (in excel: sheet ‘SimpleMOLS’, cell A116), knowing that the larger the number, the more significance we can give the data for that variable. The most pronounced variable is age with 0.485/0.89 so we can clearly advise that age is a huge factor in determining demand for pizza. Another significant variable is, of course, price with -.019/0.09 which proves basic logic that the lower the price the higher the demand. These statistics will definitely impact the decision to peruse this joint venture. What is needed is the full student body demographics and information on the students actions (specifically the actions directly corresponding to the survey results).
With this data set, we must assume the four month forecast of demand for pizza will be very similar to the demand for pizza today. With no time delay trends to model, meaning we only have data from one point in time, it would be unlikely to produce a meaningful four month forecast from simply this data. I would recommend collecting data from one or two other sources. First I would recommend soliciting enrolment information from the university to determine if there are any appreciable changes to the demographics or number of students as the semester moves forward. Does enrollment drop substantially, leading to lost opportunity of pizza sales, or are late enrolling all young, thus more likely to buy more pizza? Key data from the university would allow us to make a more substantial forecast. The second set of information I would look for is trends in patron enthusiasm of a pizza restaurants or raw data on pizza sales over time (like first four months) in similar locations, or even other Domino’s restaurants. Ideally, if Domino’s had a similar set up with another university or organization we could determine if pizza purchasing trends increase due to solidifying and expanding the customer base over time, or decreases due to a decrease in enthusiasm about the profit. Without these other potential data points we can only suggest a similar sales forecast to what the original survey provided.
Discussing if this proposal is feasible is also a difficult task due to a lack of information. Before making a full analysis and recommendation on whether Domino’s should enter this marketplace we would need to determine the cost to Domino’s for every slice of pizza. If we would have this information we could then match the cost to create the pizza versus the demand at certain price points and compare that with the required profits needed to make the venture desirable. Until more provided I would not recommend one way or another.