How an E-commerce Company Used AI to Spot Churn Risk Before Renewals

The Challenge

At an e-commerce company, customer success teams, sales teams, account managers and revenue operations teams looked after customers whose contracts were coming up for renewal. The run-up to a renewal date mattered, because it was the time to address any concerns before the customer decided whether to continue.

Signals that a customer might not renew, such as dissatisfaction or a drop in usage, were easy to miss. The relevant information sat in different places, from usage and support requests to open problem records, recent meeting notes and payment behaviour, and bringing it together for every customer approaching renewal was difficult. Without a combined view, risks could surface too late to act on, and follow-up depended on which accounts happened to receive attention.

The Solution

With cognipeer Studio, our team configured an AI agent that identified customers approaching renewal and assessed the risk around each one. For every account, it analysed usage frequency, support requests, open problem records, the most recent meeting notes and payment behaviour together, rather than looking at each signal on its own. Reading these signals side by side made it easier to see where a customer relationship needed attention.

For accounts that showed risk, the agent prepared a summary of what it had found, along with suggestions for a conversation with the customer and possible intervention steps. It then gave the relevant account manager a prioritised list, so it was clear which accounts needed attention first. Account managers reviewed the summaries and suggestions and decided how and when to contact each customer.

Implementation

1. Identified upcoming renewals. The agent was configured to find customers whose renewal dates were approaching.
2. Combined the risk signals. It analysed usage frequency, support requests, open problem records, recent meeting notes and payment behaviour for each account.
3. Produced account summaries. For accounts at risk, the agent summarised its findings and suggested conversations and intervention steps.
4. Delivered prioritised lists. Each account manager received a prioritised list of the at-risk accounts they were responsible for, with the response left to them.

Business Impact

Renewal risk was handled earlier and more deliberately. Account managers could see which customers needed attention and why, based on several signals read together, and they went into conversations with a summary and suggested next steps already prepared. Follow-up on accounts approaching renewal became more systematic, and renewal rates increased.

• Earlier management of churn risk for customers approaching renewal.
• Higher renewal rates, supported by earlier attention to at-risk accounts.
• More systematic follow-up in customer relationships.
• Decisions kept with account managers, who chose how to act on each summary and suggestion.

Technology Behind the Solution