What’s inside?

• A representation of sales and promotion information on five products from three brands within four categories (mouthwash, pretzels, frozen pizza, and boxed cereal) over 156 weeks.
• Unit sales, households, visits, and spend data by product, store, and week
• Base Price and Shelf Price, to determine a product’s discount, if any
• Promotional support details (e.g. sale tag, in-store display), if applicable

What’s it for?

This dataset is designed to facilitate time series analyses, including:

• Price sensitivity analysis
• Promotional effectiveness analysi
• Comparing/contrasting results across products, categories or store geographies

What’s inside?

• A representation of household level transactions over a period of two years from four categories: Pasta, Pasta Sauce, Syrup, and Pancake Mix

What’s it for?

• Classroom projects and case studies
• Understanding the process required to mine data
• Learning how to merge data tables and aggregate data

How should I use it?

Professors have had success asking students questions such as:

• What is the household penetration of Product X? That is, out of all customers purchasing Pasta Sauce, what percent purchase Product X or Brand Z?
• Did any customers first purchase an item or category using a coupon? If so, how many of these customers made additional purchases of the item or category?
• In two complementary categories (e.g. Pasta and Pasta Sauce), what products, if any, are commonly purchased together?

Special considerations

Don’t forget, you’re dealing with Big Data! Large file sizes may take 5+ minutes to download, and importing the millions of rows of data contained within will require specialised software such as R, Microsoft Excel with PowerPivot, Microsoft Access, SAS, SPSS, SQL, etc.

What’s inside?

• A representation of household level transactions over two years from a group of 2,500 households who are frequent shoppers at a retailer
• All of a household’s purchases within the store, not just those from a limited number of categories
• Customer attributes and direct marketing contact history for select households

What’s it for?

• More advanced classroom settings
• Academic research on the effects of direct marketing to customers

How should I use it?

Professors have had success asking students questions such as:

• How many customers are spending more/less over time?
• Which customer attributes appear to affect spend of the customer?
• Is there evidence to suggest that direct marketing improves overall customer engagement?

Special considerations

Don’t forget, you’re dealing with Big Data! Large file sizes may take 5+ minutes to download, and importing the millions of rows of data contained within will require specialised software such as R, Microsoft Excel with PowerPivot, Microsoft Access, SAS, SPSS, SQL, etc.