Subscription Retention | eCommerce Automation

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eCommerce

Subscription eCommerce Retention & Recovery System

Reduce churn and recover failed payments for subscription box and recurring revenue eCommerce brands.

eCommerce automation case study featuring workflow automation and integration.

-28%

Churn Reduction

+40%

Payment Recovery

-45%

Support Load

Overview

A subscription box company was losing subscribers to payment failures and passive churn. The automated retention system reduced churn by 28% and recovered 40% more failed payments.

Business Context

The subscription business model lives and dies on retention metrics, and this brand was bleeding subscribers through preventable churn that was undermining their growth investments. Payment failures alone were causing them to lose over 15% of their subscriber base annually — customers who never intended to cancel but whose expired cards or insufficient funds triggered automatic subscription termination without any recovery attempt. Meanwhile, active cancellation requests were processed immediately without any attempt at retention, leaving significant revenue on the table that competitors were successfully capturing through targeted save offers. The finance team had calculated that improving retention by just 10% would have the same revenue impact as doubling their marketing budget, making retention automation their highest-ROI investment opportunity.

How We Built It

We built a comprehensive retention and recovery system integrated with their Recharge subscription platform that touches every point in the subscriber lifecycle where churn risk exists. The pre-dunning layer monitors upcoming renewal dates and payment method health using card expiration data and previous failure patterns, proactively reaching out to subscribers with expiring cards or payment issues before the charge fails through personalized email and SMS reminders with easy payment update links. When payments do fail, a multi-channel recovery sequence activates across email, SMS, and in-app notifications, with messaging tailored based on subscriber tenure, lifetime value, past engagement patterns, and failure reason codes from the payment processor. Recovery messages escalate in urgency over a 14-day window with different value propositions and clear deadlines. The cancel flow intercepts cancellation requests with an AI-powered conversation that identifies the reason for cancellation and presents relevant save offers — discounts ranging from 10-30% based on subscriber value, subscription pauses of varying lengths, product swaps within the catalog, or frequency changes depending on the stated concern. OpenAI powers the save offer recommendation engine, analyzing subscriber history, stated churn reason, and historical save success rates to select the offer most likely to retain each individual customer. Subscribers who do churn enter automated win-back sequences triggered at optimal intervals (30, 60, and 90 days) based on their profile and reason for leaving, with messaging that addresses their original concern and highlights product improvements. A churn analytics dashboard surfaces patterns in cancellation reasons with drill-down by subscriber segment, acquisition source, and product preferences, enabling the team to address root causes through product improvements rather than just reactive saves. Subscription health scoring uses engagement signals like email opens, portal logins, and order modifications to identify at-risk subscribers before they show explicit intent to cancel, enabling proactive outreach to subscribers whose engagement patterns suggest declining satisfaction.

Challenges

High passive churn from payment failures

No proactive retention outreach

Cancel requests handled manually

No visibility into churn reasons

Inconsistent win-back campaigns

What We Delivered

Pre-dunning alerts before payment failures

Multi-channel recovery sequences (email, SMS, in-app)

Cancel flow with AI-powered save offers

Churn reason analytics dashboard

Win-back automation for churned subscribers

Subscription health scoring

Tech Stack

Recharge/Bold Subscriptions API, Make.com, HubSpot, OpenAI, Twilio

Results

-28%

Churn Reduction

+40%

Payment Recovery

-45%

Support Load

Strategic Impact

The 28% reduction in churn represents a fundamental improvement to the business model's unit economics, increasing customer lifetime value by an estimated $45 per subscriber and reducing the acquisition burden needed to maintain growth, effectively providing free marketing budget. The 40% improvement in failed payment recovery translates directly to retained revenue — these are subscribers who wanted to continue but would have been lost to preventable technical issues, representing recovered revenue that flows directly to the bottom line. The 45% reduction in support load came from automated handling of routine subscription modifications — pauses, skips, address changes, payment updates — that previously required human intervention, freeing the support team for higher-value customer interactions that build brand loyalty. The AI-powered save offers have created a systematic approach to retention that captures value consistently, rather than depending on individual support agent skill in handling cancellation calls, ensuring every subscriber receives an optimized retention attempt. Churn reason analytics have driven product and fulfillment improvements that address root causes, creating a virtuous cycle of better retention over time — for example, identifying that shipping delays were a top cancellation reason led to carrier changes that reduced complaints. The proactive approach to at-risk subscribers has shifted retention from reactive to predictive, identifying and addressing satisfaction issues before they become cancellation decisions, often through personalized re-engagement content or early access to new products. The subscription health scoring model has become increasingly accurate over time as it learns from outcomes, and the team now uses it to guide not just retention outreach but also product recommendations and engagement campaigns. The infrastructure has positioned the brand to experiment with new subscription models — annual plans with discounts, tiered offerings with different box sizes, add-on products with their own subscription cadences — with confidence that the retention systems can handle increased complexity and surface insights on what's working.