GoodBelly: Using Statistics to Justify the Marketing Expense

by: Hyun-Soo Ahn, Caroline Dickerson

Publication Date: July 10, 2012
Length: 6 pages
Product ID#: 1-429-252

Core Disciplines: Marketing/Sales, Operations Management/Supply Chain

Partner Collection:

Available Documents

Click on any button below to view the available document.

Don't see the document you need? Don't See the Document You Need?
Make sure you are registered and/or logged in to our site to view product documents. Once registered & approved, faculty, staff, & course aggregators will have access to full inspection copies and teaching notes for any of our materials.

$3.95

Need to make copies?

If you need to make copies, you MUST purchase the corresponding number of permissions, and you must own a single copy of the product.

Electronic Downloads are available immediately after purchase. "Quantity" reflects the number of copies you intend to use. Unauthorized distribution of these files is prohibited pursuant to term of use of this website.

Teaching Note

This product has a teaching note available. Available only to Registered Educators. Please login to view it.

Description

GoodBelly is trying to boost its sales at grocery stores like Whole Foods Market. As a small start-up, GoodBelly must optimize the allocation of its limited marketing budget. It currently promotes through in-person demonstrations in stores, but management is concerned that these demonstrations are not effective enough to justify the cost. It is up to GoodBelly’s student intern, Caroline Dickerson, to evaluate its promotional programs using statistical evidence.

Dickerson will need to apply regression analysis to sales data to determine whether or not the company should continue its promotional programs.

Teaching Objectives

After reading and discussing the material, students should:

  • Build and select a regression model.
  • Use dummy variables.
  • Interpret statistical output to generate managerial recommendations.
  • Use regression as a prediction device.
  • Learn the limitations of Microsoft Excel’s statistical package.
  • Evaluate and choose alternative statistical models.