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 returning to make a purchase.
A score closer to 0 indicates a low probability of churn, suggesting the customer is likely to remain active.
A score closer to 1 indicates a high probability of churn, suggesting the customer is at risk of leaving.
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.
This score is calculated from various factors such as the customer’s historical purchase behavior and engagement levels.
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
By leveraging churn propensity modeling, you can take a proactive approach to customer retention, reduce churn rates, and improve overall customer satisfaction.
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 |
A customer’s predicted average order revenue over the next 365 days. |
Predicted CLV Next 365d |
The total predicted spend for a customer over the next 365 days. |
Predicted Order Frequency Next 365d |
A customer’s predicted number of orders over the next 365 days. |
Predicted Probability of Transaction Next 365d |
The probability that 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.
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:
By grouping repeat customers into these tiers, you can:
For one-time buyers, groupings include the following tiers:
By segmenting one-time buyers into different tiers, you can:
|
Predicted Customer Lifetime Value (pCLV) Tier |
A percentile grouping of customers by pCLV. Groupings include:
By grouping customers by pCLV percentiles, you can:
|
By combining various customer attributes, you can create highly targeted churn prevention and one-time buyer campaigns that focus on your most valuable customers at the optimal time.