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.

Note

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 columms
Merged_Customers	The following columns are common inputs: amperity_id birthdate city email gender given_name phone postal state surname
Unified_Itemized_Transactions	The following columns are common inputs: is_return item_quantity item_revenue order_datetime order_id product_id
Unified_Transactions	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:

• Churn propensity

• Customer lifetime value

• Product affinity