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:

  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

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:

  • amperity_id

  • birthdate

  • city

  • email

  • gender

  • given_name

  • phone

  • postal

  • state

  • surname

Unified_Itemized_Transactions

The following columns are common inputs:

  • amperity_id

  • is_return

  • item_discount_amount

  • item_quantity

  • item_revenue

  • order_datetime

  • order_id

  • product_category

  • product_description

  • product_id

  • product_subcategory

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:

  • amperity_id

  • order_datetime

  • order_id

  • order_cancelled_quantity

  • order_cancelled_revenue

  • order_discount_amount

  • order_quantity

  • order_returned_quantity

  • order_returned_revenue

  • order_revenue

  • purchase_brand

  • purchase_channel

  • store_id

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:

  1. Predicting which customers are likely to spend

  2. Identifying each customer’s churn lifecycle status

Which customers are likely to spend?

The churn propensity model outputs a series of fields that predict each customer purchase behavior in the coming year, including fields for:

  1. Average order revenue

  2. Likelihood to spend

  3. Order frequency

  4. Total spend

Fields that predict a customer’s future purchase behavior are available from the Predicted_CLV_Attributes table:

Field 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 fields to build audiences that align to various stages within your churn prevention campaigns. You can access these fields directly from the segment editor:

Which customers are more likely to spend?

Which customers are likely to churn?

The churn propensity model outputs a series of fields that categorize your customers by:

  1. Predicted lifecycle status, such as active, cooling down, at risk, and lost

  2. Predicted lifetime value, such as top 1%, top 10%, and top 50%

Fields that predict a customer’s likelihood to churn, along with their predicted lifetime value, are available from the Predicted_CLV_Attributes table:

Field 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:

  1. Active (likelihood to purchase is greater than 60%)

  2. Cooling down (likelihood to purchase is between than 50% and 60%)

  3. At risk (likelihood to purchase is between than 35% and 50%)

  4. Highly at risk (likelihood to purchase is between than 20% and 35%)

  5. Lost (likelihood to purchase is less than 20%)

For one-time buyers, groupings include the following tiers:

  1. Active (purchased within 60 days)

  2. Cooling down (purchased 60-120 days ago)

  3. At risk (purchased 120-180 days ago)

  4. Highly at risk (purchased 180-240 days ago)

  5. Lost (purchased 240+ days ago)

predicted_customer_lifetime_value_tier

A percentile grouping of customers by pCLV. Groupings include:

  1. Platinum: top 1% of customers

  2. Gold: top 1%-5% of customers

  3. Silver: top 5%-10% of customers

  4. Bronze: top 10%-25% of customers

  5. Medium: top 25%-50% of customers

  6. Low: bottom 50% of customers

Use any combination of these fields 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 fields directly from the segment editor:

Which customers are more likely to churn?

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 each customer’s probability of purchase, number of orders, and 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:

  1. How much will customers spend in the next year?

  2. Which customers are your most valuable customers?

How much will customers spend?

The predicted_clv_next_365d field in the Predicted_CLV_Attributes table contains the total predicted customer spend over the next 365 days. You can access this field directly from the segment editor:

Which customers will spend the most in the coming year?

After you select this field 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:

  1. Platinum: top 1% of customers

  2. Gold: top 1%-5% of customers

  3. Silver: top 5%-10% of customers

  4. Bronze: top 10%-25% of customers

  5. Medium: top 25%-50% of customers

  6. Low: bottom 50% of customers

Use the predicted_customer_value_tier field in the Predicted_CLV_Attributes table to add customers to audiences by value tier. You can access this field directly from the segment editor:

Who are your most valuable customers?

After you select this field 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:

  1. Recommended audience sizes

  2. Ranking customers by product scores

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 field 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 field with the product_attribute field to build customer rankings for a specific product category, class, or brand. You can access this field directly from the segment editor:

Ranking in the Product_Affinity table.