Predicted CLV Attributes table¶
The Predicted CLV Attributes table contains information, for each individual Amperity ID, about predicted future spend and the probability of churn.
Note
The size of the Predicted CLV Attributes table in your tenant is based on the number of Amperity IDs.
Add table¶
Add the Predicted CLV Attributes table to the customer 360 database.
as a SQL table¶
A SQL table is added to a database using Spark SQL and a SELECT
statement that defines the table’s schema.
The Days Since Last Order and Historical Order Frequency Lifetime columns in the Predicted CLV Attributes table are not recommended for use with segmentation or for downstream workflows. Use SQL to build a predicted attributes table for use in the customer 360 database that omits these columns.
To add the Predicted CLV Attributes table as a SQL table
From the Customer 360 page, under All Databases, select the menu for the customer 360 database, and then click Edit.
From the Database Editor, click Add Table.
Set Build Mode to “SQL”, and then use a SELECT statement similar to the following to define the schema:
SELECT amperity_id AS `amperity_id` ,predicted_average_order_revenue_next_365d AS `average_revenue_365` ,predicted_clv_next_365d AS `clv_365` ,predicted_customer_lifecycle_status AS `purchase_likelihood` ,predicted_customer_lifetime_value_tier AS `value_tier` ,predicted_order_frequency_next_365d AS `predicted_orders_365` ,predicted_probability_of_transaction_next_365d AS `purchase_probability` FROM Predicted_CLV_Attributes
Hide the table from the Visual Segment Editor by verifying that Show in VSE? is unselected.
Click Activate to update the customer 360 database with your changes.
as a passthrough table¶
A passthrough table adds a domain table to your database. Some domain tables are replaced by stitched domain tables, which include the Amperity ID, but otherwise are identical to the source domain table.
To add the Predicted CLV Attributes table as a passthrough table
From the Customer 360 page, under All Databases, select the menu for a database, and then click Edit.
Click Add Table.
Set Build Mode to “Passthrough”, and then select the Predicted CLV Attributes table from the drop-down menu.
Hide the table from the Visual Segment Editor by verifying that Show in VSE? is unselected.
Click Activate to update the customer 360 database with your changes.
Column reference¶
The Predicted CLV Attributes table contains the following columns:
Column name |
Data type |
Description |
---|---|---|
Amperity ID |
String |
The unique identifier that is assigned to clusters of customer records that all represent the same individual. The Amperity ID does not replace primary and foreign keys, but exists alongside them within unified profiles. Note The Amperity ID is a universally unique identifier (UUID) that is represented by 36 characters spread across five groups separated by hyphens: 8-4-4-4-12. For example: 123e4567-e89b-12d3-a456-426614174000
|
Predicted Average Order Revenue Next 365D |
Decimal |
The predicted average order revenue over the next 365 days. |
Predicted CLV Next 365D |
Decimal |
The total predicted customer spend over the next 365 days. |
Predicted Customer Lifecycle Status |
String |
A probabilistic grouping of a customer’s likelihood to purchase again. For repeat customers, groupings include the following tiers:
For one-time buyers, groupings include the following tiers:
|
Predicted Customer Lifetime Value Tier |
String |
A percentile grouping of customers by predicted CLV. Groupings include:
Note This attribute returns only the customers who belong to the selected value tier. For example, to return all of your top 10% customers, you must choose platinum, gold, and silver. Silver by itself will return 5% of your customers, specifically those are in your 5-10%. |
Predicted Order Frequency Next 365D |
Decimal |
The predicted number of orders over the next 365 days. |
Predicted Probability Of Transaction Next 365D |
Decimal |
The probability a customer will purchase again in the next 365 days. |