Reference no: EM132588243
Marketing Analytics
1. Thus far in the course we have discussed two types of analytics: descriptive and predictive. Describe each one. How are they different? How do they work together? Why are they both necessary? What is the biggest difference between the two?
2. The file USmacrodata.xlsx contains U.S. quarterly GNP, Inflation rates, and Unemployment rates. Use this file to perform the following exercises:"
a. Develop a regression to predict quarterly GNP growth from the last four quarters of growth. Check for non-normality of residuals, heteroscedasticity, autocorrelation, and multicollinearity.
b. Develop a regression to predict quarterly inflation rate from the last four quarters of inflation. Check for non-normality of residuals, heteroscedasticity, autocorrelation, and multicollinearity.
c. Develop a regression to predict quarterly unemployment rate from the unemployment rates of the last four quarters. Check for non-normality of residuals, heteroscedasticity, autocorrelation, and multicollinearity.
3. The file Oreos.xlsx gives daily sales of Oreos at a supermarket and whether Oreos were placed 7" from the floor, 6" from the floor, or 5" from the floor. How does shelf position influence Oreo sales?
4. Logistic regression falls under the umbrella of discrete choice theory. Do some research and in your own words explain what this is. How is it useful in marketing? What does it do that regular regression cannot?
5. Assume the annual retention rate for a cell phone subscriber is 70 percent and the customer generates $300 per year in profit. Assuming an annual discount rate of 8 percent, compute the value of a customer.
Attachment:- Marketing Analytics.rar