Predictive models

A predictive model is a feature of AmpIQ that predict customer behavior, such as predicted customer lifetime value (predicted CLV), churn propensity, product affinity, and lifecycle events.


Predictive models require at least four years of historical data (five years or more is recommended). You should have at least 100,000 customer transactions on an annual basis, with at least a 10% retention rate.

The following tables must be configured in your customer 360 database prior to running predictive models:

  1. Customer_360

  2. Merged_Customers

  3. Transaction_Attributes

  4. Transaction_Attributes_Extended

  5. Unified_Itemized_Transactions (updated to include product catalogs)

  6. Unified_Transactions

Common inputs to models

Columns from the Merged_Customers, Unified_Transactions, and Unified_Itemized_Transactions (including product catalogs) are used as inputs to predictive modeling. For multi-brand tenants, the amperity_id column from the Customer_360 table is also used as an input to predictive modeling.

The following columns are common inputs to predictive models:

Source table

Input columns


The following columns are common inputs:

  • amperity_id

  • birthdate

  • city

  • email

  • gender

  • given_name

  • phone

  • postal

  • state

  • surname


The following columns are common inputs:

  • is_return

  • item_quantity

  • item_revenue

  • order_datetime

  • order_id

  • product_id


The following columns are common inputs:

  • amperity_id

  • order_cancelled_quantity

  • order_cancelled_revenue

  • order_datetime

  • order_discount_amount

  • order_id

  • order_quantity

  • order_returned_quantity

  • order_returned_revenue

  • order_revenue

  • purchase_brand

  • purchase_channel

  • store_id

Available models

The following predictive models can be enabled for your tenant: