Question

PART I Starbucks launched its prepaid (debit) Starbucks Card in November 2001. The card, which holds between $5 and $500 can be used at any Starbucks location. Suppose Starbucks management wants to study the reasons why some people purchase debit cards with higher prepaid amounts than do other people. Suppose a study of 25 randomly selected prepaid purchasers is taken. Respondents are asked the amount of the prepaid card, the customer’s age, the number of days per month the customer makes a purchase at Starbucks, the number of cups of coffee that customer drinks per day, and the customer’s income. The data is given in the Excel spreadsheet.

Amount of Prepaid Card ($)	Age	Days per Month at Starbucks	Cups of Coffee per Day	Income ($1,000)
5	25	4	1	20
25	30	12	5	35
10	27	10	4	30
5	42	8	5	30
15	29	11	8	25
50	25	12	5	80
10	50	8	3	30
15	45	6	5	35
15	32	16	7	25
15	23	10	1	20
20	40	18	5	40
35	35	12	3	40
40	28	10	3	50
15	33	12	2	30
200	40	15	5	80
15	37	3	1	30
40	51	10	8	35
5	20	8	4	25
30	26	15	5	35
100	38	19	10	45
30	27	12	3	35
25	29	14	6	35
25	34	10	4	45
50	30	6	3	55
15	22	8	5	30

1. Using these data, develop a multiple regression model to study how well the amount of the prepaid card can be predicted by the other variables and which variables seem to be more promising in doing the prediction.

2. Rerun your model based on the significant independent variables only and explain the results.

3. What sales implications might be evident from this analysis?

PART II Suppose marketing wants to be able to profile frequent visitors to a Starbucks store. Using the same data set already provided, develop a multiple regression model to predict days per month at Starbucks by Age, Income and Number of cups of coffee per day.

4. How strong is the model?

5. Which independent variables seem to have more promise in predicting how many days per month a customer visits Starbucks?

6. What marketing implications might be evident from this analysis?

Answer 1