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), along with the following tables already configured in your customer 360 database:
Customer_360
Merged_Customers
Transaction_Attributes
Transaction_Attributes_Extended
Unified_Itemized_Transactions (updated to include product catalogs)
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 |
---|---|
Customer_360 |
The amperity_id columns is used as an input to predictive modeling for multi-brand tenants. |
Merged_Customers |
The following columns are common inputs:
|
Unified_Itemized_Transactions |
The following columns are common inputs:
Important The product_category, product_description, and product_subcategory columns must be joined to the Unified_Itemized_Transactions table. |
Unified_Transactions |
The following columns are common inputs:
|
Model: Churn propensity¶
Churn propensity is a predictive model that determines the likelihood that a customer will be active at any point in time, based on their purchase history. The churn propensity model outputs a score between 0 and 1 that represents a customer’s probability of return.
Tip
p(return) is a probabilistic score that predicts churn likelihood and represents how likely is it for an individual customer to purchase in the next year.
Amperity models churn propensity for each customer’s unique purchase history. Some customers are seasonal shoppers, whereas other customers make monthly (or even weekly) purchases. Churn propensity modeling is based on each customer’s individual p(return) score, which helps you build audiences that:
Identify customers who are likely to churn
Provide better insights about the root causes of customer churn to help you determine what will compel them to stay with right-timed messaging and relevant products
Support a churn prevention campaign that contains a series of escalating win-back offers
Optimize suppression and spend
Use cases¶
The churn propensity model helps you build audiences to support churn prevention campaigns, including:
Which customers are likely to spend?¶
The churn propensity model outputs a series of attributes that predict each customer purchase behavior in the coming year, including attributes for:
Average order revenue
Likelihood to spend
Order frequency
Total spend
Attributes that predict a customer’s future purchase behavior are available from the Predicted CLV Attributes table:
Attribute Name |
Description |
---|---|
Predicted Average Order Revenue Next 365d |
The predicted average order revenue over the next 365 days. |
Predicted CLV Next 365d |
The total predicted customer spend over the next 365 days. |
Predicted Order Frequency Next 365d |
The predicted number of orders over the next 365 days. |
Predicted Probability of Transaction Next 365d |
The probability a customer will purchase again in the next 365 days. |
Use any combination of these attributes to build audiences that align to various stages within your churn prevention campaigns. You can access these attributes directly from the segment editor:

Which customers are likely to churn?¶
The churn propensity model outputs a series of attributes that categorize your customers by:
Predicted lifecycle status, such as active, cooling down, at risk, and lost
Predicted lifetime value, such as top 1%, top 10%, and top 50%
Attributes that predict a customer’s likelihood to churn, along with their predicted lifetime value, are available from the Predicted CLV Attributes table:
Attribute Name |
Description |
---|---|
Predicted Customer Lifecycle Status |
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 |
A percentile grouping of customers by pCLV. Groupings include:
|
Use any combination of these attributes to help focus your churn prevention and one-time buyer campaigns on your most valuable customers at the right stages within the campaign. You can access these attributes directly from the segment editor:

Model: Predicted CLV¶
Predicted customer lifetime value represents the total value of all orders a customer is predicted to make if they return to make another purchase during the next 365 days.
Amperity models predicted CLV by comparing what customers spent in the previous year to their predicted spend in the coming year, and then determines for each customer their:
Predicted pobability of purchase
Predicted number of orders
Predicted average order value
Use predicted CLV modeling to build high-value audiences that identify:
Which customers have the highest predicted value?
Which customers will respond better to special offers and perks?
What are the best personalized experiences for your best customers?
Which customers have individual price preferences?
Use cases¶
The predicted CLV model helps you identify your highest value customers by year or by value tier:
How much will customers spend?¶
The Predicted CLV Next 365d attribute in the Predicted CLV Attributes table contains the total predicted customer spend over the next 365 days. You can access this attribute directly from the segment editor:

After you select this attribute you can specify the type of values you want to use for this audience, such as:
Predicted value is greater than $100
Predicted value is less than $400
Predicted value is between $100 and $400
Which customers are the most valuable?¶
Predicted CLV modeling sorts customers into the following value tiers:
Platinum: top 1% of customers
Gold: top 1%-5% of customers
Silver: top 5%-10% of customers
Bronze: top 10%-25% of customers
Medium: top 25%-50% of customers
Low: bottom 50% of customers
Use the Predicted Customer Lifetime Value Tier attribute in the Predicted CLV Attributes table to add customers to audiences by value tier. You can access this attribute directly from the segment editor:

After you select this attribute you can choose which value tier to use for this audience.
Model: Product affinity¶
Product affinity is a predictive model that identifies which customers are likely to purchase by using a combination of historical purchase data and lookalike audiences. The predicted affinity model outputs a ranked list of customers with three recommended audience sizes.
Amperity models product affinity for any product taxonomy that contains between 20 and 2000 unique values, such as brand, category, subcategory, color, size, season, and style. Product affinity modeling analyzes:
Historical data to identify customers who have purchased a product in the past and are likely to do so again.
Lookalike audiences to identify customers who have not purchased a product, but are likely to buy because they are similar to customers who have purchased.
Use cases¶
The product affinity model enables support for marketing campaigns that benefit from knowing a customer’s preferences across product categories, including:
Recommended audience sizes¶
A recommended audience is a feature of Amperity that answers the following question: “How large of an audience is required to grow revenue over the next 30 days?” Amperity provides three answers to this question, in the form of recommended audience sizes: small (50%), medium (70%), and large (90%), which represent the number of customers that are required to capture 50%, 70%, or 90% of purchases for that audience over the next 30 days.
Recommended audience sizes are calculated using customer transaction data over a 30-day window. A purchase curve is generated, along with corresponding audience sizes that show what size audience would have been required to capture 50%, 70%, and 90% of purchases for a given product over the previous 30 days.
Audience sizes are inclusive of all smaller audience sizes.
A medium audience size (70%) includes all of your customers who are in the small audience size (50%).
A large audience size (90%) includes all of your customers who are in the small and medium audiences.

Recommended audience sizes identify customers who are most likely to purchase. Use recommended audience sizes to:
More effectively engage with customers for product-specific sends, such as clearance sale and new arrival announcements
Define more valuable campaigns to grow revenue for specific product categories
Drive up conversion rates
Drive down opt-outs
Determine categories, products, and trends that resonate with key segments
Attributes for recommended audience sizes are available from the Predicted Affinity table:
Attribute Name |
Description |
---|---|
Audience Size Small |
A small audience is predicted to incude ~50% of future purchasers, while including the fewest non-purchasers. Use a small audience size to help prevent wasted spend and reduce opt-outs. |
Audience Size Medium |
A medium audience is predicted to include ~70% of future purchasers, though it may also include a moderate number of non-purchasers. |
Audience Size Large |
A large audience is predicted to include ~90% of future purchasers, while also including a high number of non-purchasers. |
Combine these attributes with the Product Attribute attribute to build audiences for a specific product category, class, or brand. You can access these attributes directly from the segment editor:

Customer ranking¶
Use customer ranking to define an audience using the top N customers. Use customer ranking as an alternate to recommended audience sizes when an audience is too large (or small) or if a recommended audience size is unavailable for a specific product or category.
Customer ranking identifies the top N customers who are most likely to purchase. Use customer ranking to:
Provide an alternative to a recommended audience size, such as when a recommended audience size is unavailable for a specific product or category
Serve targeted product messages to defined audiences
Identify first-time buyer personas
Drive up conversion rates
Drive down opt-outs
The Ranking attribute in the Predicted Affinity table ranks customer scores by product. A rank that is less than or equal to X will provide the top N customers with an affinity for this product. Combine this attribute with the Product Attribute attribute to build customer rankings for a specific product category, class, or brand. You can access this attribute directly from the segment editor:
