How to Identify Customers at Risk of Churn

1. Problem Statement / Business Use Case

Customer churn is one of the most important metrics for subscription-based businesses, e-commerce platforms, and service providers. Losing customers not only reduces recurring revenue but also increases the cost of acquiring new customers. By identifying customers at risk of churn, businesses can proactively engage them with retention campaigns, targeted promotions, or personalized offers to keep them active.

This guide walks you through a language-agnostic approach to flagging at-risk customers using transaction history, profile data, and business-defined thresholds. It is designed for programmers, analysts, and data practitioners who want actionable insights without being tied to a specific programming language.

2. Inputs

To implement this recipe, you need access to several key data elements:

  • Customer transaction history – Dates and amounts of purchases, which allow you to measure engagement recency.
  • Customer profile information – Attributes such as signup date, last purchase date, and optionally, total lifetime value or segment.
  • Churn threshold – A business-defined number of days of inactivity after which a customer is considered at risk.

Tip: Churn thresholds may differ by product line or customer segment. For example, frequent buyers might be flagged after 30 days of inactivity, whereas occasional buyers might have a 90-day threshold.

3. Outputs

The goal of this recipe is to produce a prioritized list of customers who are likely to churn, enabling targeted retention efforts. Outputs can include:

  • At-risk customers – Those who have not engaged within the churn threshold.
  • Active customers – Those currently engaging within an acceptable timeframe.
  • Optional risk scores – Numeric values representing the likelihood of churn, which can help prioritize retention campaigns.

4. Step-by-Step Logic / Pseudocode

The core logic can be implemented in any programming language or analytics tool. Pseudocode is provided here for clarity:

For each customer:
    Calculate days_since_last_purchase
    If days_since_last_purchase > churn_threshold:
        Flag customer as 'at risk'
    Else:
        Flag customer as 'active'

Optional enhancement:
    Sort at-risk customers by total lifetime value or recent purchase frequency
    Prioritize retention actions based on these rankings

If you are familiar with SQL queries, this is an example of how to calculate churn risk

SELECT 
    customer_id,
    last_purchase_date,
    total_value,
    CASE 
        WHEN DATEDIFF(CURRENT_DATE, last_purchase_date) > @churn_threshold THEN 'at risk'
        ELSE 'active'
    END AS status
FROM 
    customers
ORDER BY 
    CASE 
        WHEN DATEDIFF(CURRENT_DATE, last_purchase_date) > @churn_threshold THEN total_value
        ELSE NULL
    END DESC;

This approach ensures that even without complex predictive models, you can generate actionable insights. Businesses can then act quickly to prevent churn, increasing retention and revenue.

5. Considerations / Edge Cases

When applying this recipe in real-world scenarios, consider the following:

  • Customers with no purchase history – Decide whether to flag, ignore, or track them separately.
  • Missing or inconsistent dates – Clean and validate your data to avoid misclassification.
  • Segment-specific thresholds – Customers may have different engagement patterns; adjust thresholds accordingly.
  • Seasonality effects – Be aware of seasonal products or campaigns that may influence engagement metrics.

By addressing these considerations, you ensure that your churn detection process is accurate, actionable, and aligned with business reality.

6. Optional Enhancements / Variations

Once the basic logic is implemented, you can enhance your churn detection with additional insights:

  • Rolling engagement trends – Track declining purchase frequency or website activity to flag early risk.
  • Behavioral signals – Include customer support interactions, subscription cancellations, or login activity to improve accuracy.
  • Automated retention campaigns – Connect flagged customers to marketing or CRM systems for immediate outreach.

These enhancements transform a descriptive analytics approach into predictive and prescriptive insights, providing more strategic value for your business.

Topic Map

Vocabulary (8-12 words & definitions)

  • Churn – The process of customers stopping their engagement or leaving the business.
  • Retention – Efforts aimed at keeping customers active and satisfied.
  • Threshold – The maximum allowable period of inactivity before a customer is flagged.
  • Transaction – A recorded purchase or engagement by a customer.
  • Flag – A marker indicating a customer meets a specific condition.
  • Profile – Collection of customer attributes, including behavior and demographics.
  • Score – A numeric measure of the likelihood of churn.
  • Engagement – Any interaction indicating ongoing interest in the product or service.

Concepts (2-3)

  • Customer segmentation – Grouping customers by behavior, value, or risk profile.
  • Risk assessment – Evaluating which customers are likely to disengage.
  • Retention prioritization – Identifying high-value customers to focus retention efforts on.

Procedures (1-3)

  • Calculate days since last purchase for each customer.
  • Compare against the churn threshold to classify as at-risk or active.
  • Prioritize retention actions based on value or engagement metrics.

Themes / Categories

  • Customer lifecycle management
  • Business intelligence and analytics
  • Predictive insights for proactive decision-making