What signals should a customer health score include for ecommerce?

The most predictive signals are: recency of last purchase, purchase frequency trend (is it increasing or decreasing?), return rate, CSAT score on recent support interactions, email open and click rate for post-purchase communications, and subscription status if applicable. Weight each signal by its empirical correlation with churn in your specific customer data.

How do you act on a declining customer health score?

Prioritize intervention by severity: critical health triggers immediate personal outreach or a win-back offer; at-risk health triggers an automated re-engagement sequence; healthy customers need no special action. Timing intervention to the customer's natural repurchase window significantly improves response rates.

How is customer health score different from RFM analysis?

RFM (Recency, Frequency, Monetary value) is a subset of health scoring focused on purchase behavior. A full health score adds non-purchase signals — support satisfaction, email engagement, return behavior — that predict churn in customers who are still purchasing but disengaging. The additional signals catch at-risk customers earlier than RFM alone.

Glossary

Customer Health Score

A customer health score is a composite metric that aggregates behavioral signals — purchase frequency, recency, average order value, support history, email engagement, and product return rate — into a single indicator of how likely a customer is to continue purchasing or to churn.

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What it means

Key insight

A customer health score turns the lagging indicator of churn into a leading indicator — so you can intervene before a customer is already gone.

In subscription software businesses, customer health scoring is a mature discipline used to predict renewals and cancellations before they happen. In ecommerce, the same principle applies: customers who are engaged, purchasing regularly, and having positive support experiences are healthy; customers who haven't purchased in 90 days, who had an unresolved support issue, or whose average order value is declining are at-risk. A health score aggregates these signals — weighted by their predictive power for churn — into a single number or status (healthy, at-risk, critical) that can be acted on proactively. Support history is a significant input: customers who had support issues that were resolved to their satisfaction contribute positively to health; customers with unresolved or badly-handled issues contribute negatively. Monitoring health scores allows brands to prioritize win-back and retention outreach toward the customers most worth saving — rather than sending the same generic re-engagement email to everyone.

Why it matters

Customer health scoring transforms retention from a reactive to a proactive function. Without it, brands discover churn after it happens — when the customer has already stopped purchasing. With it, at-risk customers can be identified weeks before the decision point and targeted with the right intervention: a proactive support check-in, a personalized incentive, or a win-back campaign timed to the customer's natural repurchase window. For Shopify merchants, even a simple health score model — combining recency, frequency, and recent support satisfaction — provides actionable differentiation that generic email marketing cannot.

How Bookbag helps

Health Signal Aggregation

Bookbag combines Shopify purchase data with support interaction outcomes — resolution satisfaction, escalation history, CSAT ratings — to provide a richer health signal than purchase data alone.

At-Risk Customer Flagging

Bookbag identifies customers whose combination of purchase recency and support history places them at elevated churn risk, surfacing them for proactive outreach before the churn decision is made.

Post-Support Health Impact

After every support interaction, Bookbag updates the customer's health signal based on the interaction outcome — a resolved issue improves health; an escalated or unsatisfied interaction triggers an at-risk flag.

Frequently Asked Questions

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