Returns Reduction Programme for a UK Ecommerce Brand

Business Context

The retailer faced increasing returns pressure from sizing uncertainty, expectation mismatch, and delayed post-purchase communication. Returns processing complexity also affected support operations and fulfilment planning.

Core Challenges

1. Limited visibility into likely return drivers by SKU.
2. Fragmented post-purchase messaging across channels.
3. Slow feedback loop between support and merchandising teams.

Delivery Approach

1. Returns-risk scoring by SKU, customer segment, and order context.
2. Automated post-purchase interventions for high-risk journeys.
3. Insight dashboards linking return drivers to operational actions.

Implementation Timeline

• Weeks 1-2: Data audit and return-driver hypothesis mapping.
• Weeks 3-6: Model build, validation, and intervention design.
• Weeks 7-8: Workflow integration with CRM and support systems.
• Weeks 9-10: Pilot monitoring and optimisation.

Operational Outcomes

The team improved merchandising and support decisions with clearer risk insight. Customer journey quality improved through timely, context-specific messaging designed to reduce avoidable return behaviour.

Next-Phase Roadmap

Planned expansion includes dynamic fit guidance and supplier-level returns variance monitoring.