From reactive returns to a proactive,
data-led prevention engine.
Overview
MOAR helped a leading fashion retailer re-engineer its returns and post-purchase operations, building a data-led, scalable returns engine to reduce volume, accelerate refunds, and improve customer experience.
About the Customer
The customer is a leading fashion retailer managing high-volume returns and post-purchase operations across multiple categories and channels.
Problem Statement
Fragmented returns workflows, inconsistent return reasons, and slow refund cycles were driving elevated return rates, rising cost-to-serve, and declining customer trust. A lack of root-cause visibility meant the retailer was reacting to returns after the fact, rather than preventing them upstream.
Impact Achieved
In under 12 weeks, MOAR helped improve customer trust and repeat purchase intent, lowered cost-to-serve across post-purchase operations and gave the retailer clear visibility into top return drivers across categories, shifting returns from a reactive cost centre into a data-driven, prevention-focused operation.
Reduction in return rates (category-dependent)
Faster refund turnaround time
How MOAR Advisory Enabled Impact
MOAR, the GCC consulting services provider, re-engineered returns and post-purchase operations across people, process, and technology, combining returns operations discipline, root-cause analytics, and workflow automation to build a proactive returns prevention and recovery model.
The transformation began with people. We built a dedicated returns and post-purchase ops pod, a team with a single mandate: own returns end-to-end. Clear category-level accountability replaced ambiguous ownership, so every spike in return volume had a name attached to it, not just a number. As the team matured, it was progressively up-skilled on prevention-led approaches.
That mindset needed structure to scale. On the process front, we standardized return reason codes and workflows across categories, replacing inconsistent tagging with a common language for why products were coming back. Clear SLAs for refunds and escalations brought discipline to turnaround times, but the real shift came from root-cause analysis that involved digging into returns drivers across fit, content, pricing, and fulfilment, then feeding those insights back into catalog, PDP, and vendor actions.
Technology tied it all together, and we pulled returns data out of separate silos, OMS, customer service, and warehouse systems, and brought it into one central view. No more piecing together fragments from different teams just to understand what happened to a single return.
With that data in one place, we built returns analytics dashboards, which made it easy to spot exactly which categories and products were driving the most returns and losing the most value.
The last piece of the puzzle was automation. With workflows running on their own and alerts flagging anything that needed a closer look, routine refunds and exceptions no longer needed manual handling. That freed the team up to focus on bigger patterns, not paperwork.