Churn risk predictive segmentation to reduce churn

Use the power of churn prediction to retain your ecommerce customers

September 13, 2024

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Hey there

I hope this message finds you well.

In the constantly changing business environment, we often prioritize the acquisition of new customers, dedicating significant resources to drive growth.

But, in today's economy, the true secret to maximizing returns lies in nurturing the relationships we already have.

I understand that customer acquisition costs can be overwhelming, and that's why I advocate for a strategic shift toward reducing churn.

Churn rate, representing the percentage of existing customers who don't return within a specific timeframe, reveals crucial insights about your business's vulnerabilities.

In this newsletter, I will share our learnings on how you can use churn risk prediction to reduce churn and retain more of your customers.

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Defining Churn Risk Segments

The definition of churn risk segments typically includes several key criteria:

  • Placed an order at least once over the course of their history with your business: This criterion ensures that these are not entirely dormant customers but have engaged with your brand at some point.

  • No orders placed in the last 180 days: This marks a specific time frame within which customers have shown a lapse in their purchasing behavior, indicating a potential disengagement.

  • Received at least two emails within the last 90 days: Even though they've received communication, their interaction with these emails has been limited.

  • Zero email opens in the last 90 days: Despite having received emails, these customers have not shown engagement by opening any of them.

  • Not suppressed: These customers have not been intentionally suppressed from your email marketing campaigns.

👉 It's important to note that while this definition serves as a generic guideline, the ideal time frames and criteria can vary depending on your industry and product. For instance, if you sell durable goods like mattresses, the period of inactivity before identifying a churn risk segment might be longer due to the nature of the product.

Common Customer Churn Factors and Solutions:

  1. Product dissatisfaction: Address design flaws to improve customer satisfaction.

  2. Shipping issues: Fix delays, damaged goods, and packaging problems for better customer experiences.

  3. High prices: Stay competitive, offer discounts, and emphasize product value.

  4. Accurate descriptions: Ensure product descriptions match reality to prevent customer disillusionment.

  5. Post-purchase support: Provide guidance for complex products to encourage repeat purchases.

  6. Simplify returns: Streamline return/exchange policies for long-term customer retention.

Klaviyo’s Churn Risk Model

Klaviyo's churn risk model follows specific criteria to ensure accurate predictions:

  1. Your business must have a minimum of 500 customers with order history. This size allows for meaningful comparisons with overall customer behavior.

  2. The business should have a history of at least 180 days of orders, including recent orders within the last 30 days.

  3. Some customers within your business should have placed 3 or more orders.

Meeting these data requirements enables you to access a visualization like the one below (go to a specific profile, under Metrics and Insights tab you will see this):

  • Customer CLV is represented by a colored bar: blue for Historic CLV (past spending) and green for Predicted CLV (future spending).  

  • The graph also displays the expected date of the next order, average time between orders, average order value, and current churn risk prediction.

  • The model considers factors like the number of orders, time between orders, and the most recent purchase to assess churn risk.

  • Churn risk gradually increases as the time since the last purchase lengthens.

  • The model adapts based on a customer's actual purchase frequency, resulting in a unique churn prediction for each business.

  • It refines predictions by comparing individual behavior to similar patterns in your customer population, particularly those linked to repeat purchases.

👉 Please note that Klaviyo’s Churn Prediction Score is not available to create segments or flow triggers as of now. You can read their reasoning here.

If you want to further narrow down the segment that we created above, you can use the predicted CLV property to filter out your most valuable customers first (as shown below):

Ways to Prevent Customer Churn:

Once you've identified these valuable churn risk segments, it's time to take action. Here are some effective strategies to win back these customers:

  1. Personalized Promotions: Send promotions that resonate with the preferences and past behavior of these customers. Remind them of the value your brand offers.

  2. Coupon Reminders: Gently nudge them with reminders of any unused coupons they may have. This can serve as a compelling incentive to re-engage.

  3. Additional Discounts: Offer exclusive discounts or special offers to incentivize them to make a purchase.

  4. Re-Engagement Campaigns: Create targeted re-engagement email campaigns designed to reignite their interest in your products or services.

  5. Persistence and Patience: Don't give up easily. Churn risk segments represent customers who already know your brand. With persistence, you can regain their interest and loyalty.

Final thoughts

Churn analysis is merely the beginning; the true power lies in taking action. If you spot customers with a churn prediction value of 75% or higher, it's time to shore up your customer experience.

Don't let your valuable customers slip away. Predict churn and take proactive steps to reduce attrition for your brand. In the grand scheme of business, remember: Predicting churn is the key to preventing it.