Customer purchase preferences

This algorithm enriches the customer profile with data about their purchasing habits and preferences. For example, whether they spend more during weekends or work days, or which is their favourite store.

Description

You must provide data about the customer’s transactions, including data that can be categorized. For example, the purchase date, payment method, sales assistant and store ID for each transaction.

The output includes information about your customer’s preferences, such as the store they most frequently shop at, and their preferred payment method.

Customer purchase preferences input data and output

Input data Output
Transactional data

Customer_id.

Date.

Order_id.

Order_line_id.

Product_id.

Product category.

Amount.

Discount.

Sales assistant.

Store_id.

Payment method.

Top categories

Preferred store.

Preferred payment method.

Preferred sales assistant.

Preferred half-year.

Preferred month.

Preferred weekday.

Preferred hour.

 

Output examples

The output is provided as new fields that are associated with each customer profile.

Example of the output

Customer ID 11223344
Top categories Shoes, Suits, Accessories
Preferred store Malpensa MXP Corner
Preferred payment method Credit card
Preferred sales assistant Joe Smith
Preferred half-year
1
Preferred month July
Preferred weekday Weekends
Preferred time Evening

 

Tags:

  • Clustering.
  • Transactional.

 

 

Previous page: Customer time-based behavior statistics | Next page: RFM clustering