portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.).There are also records for when a user completes an offer.
This transactional data also has a record for each offer that a user receives as well as a record for when a user actually views the offer. We are also given transactional data showing user purchases made on the app including the timestamp of purchase and the amount of money spent on a purchase. We’ll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.
As an example, a BOGO offer might be valid for only 5 days. Every offer has a validity period before the offer expires. Our task is to combine transaction, demographic and offer data to determine which demographic groups respond best to which offer type. Not all users receive the same offer, and that is the challenge to solve with this data set. Some users might not receive any offer during certain weeks. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Once every few days, Starbucks sends out an offer to users of the mobile app.
Provided sample data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app.