Returns & CX
Cut returns at the root, not the refund
Returns-rate root cause, fraud signals, refund-policy audit and churn diagnostics.
Returns are a margin line disguised as a customer-service problem, and they usually have a fixable root cause. CDO Agent analyses returns-rate root cause by SKU and reason, benchmarks it against category norms, surfaces returns-fraud signals and audits the refund policy - so you cut the returns that should not happen instead of just processing the refunds faster.
The problem
What slows this down
The returns rate is a single headline number, so nobody knows which SKUs or reasons actually drive it.
Returns are treated as a CX cost to process, not a margin leak with a root cause to fix.
A small share of customers and SKUs drive most returns and some are fraud, but the signals are never pulled together.
The refund policy was written once and never tested against what it actually costs in margin.
What you get
What the agent does here
Returns root cause
Returns rate decomposed by SKU, category and reason and benchmarked against norms - apparel runs very differently from electronics - so you fix the sizing, photography or description issue driving the worst lines.
Fraud and abuse signals
The customer and SKU patterns that signal returns abuse or fraud, pulled together so the small share driving most of the cost is visible and actionable.
Policy and churn
A refund-policy audit against what it actually costs in margin, plus churn diagnostics, so CX decisions weigh retention against the returns and refund bill.
Covers
What this domain spans
- Returns-rate root cause by SKU and reason
- Category benchmarking (fashion vs electronics)
- Returns-fraud and abuse signals
- Refund-policy cost audit
- Churn and retention diagnostics
- Returnless-refund economics
Metrics
What the agent watches
- Returns rate
- By SKU, category and reason - not just the headline number.
- Return reason mix
- Sizing, damage, not-as-described - where the fix is.
- Returns cost / order
- Processing + refund + lost margin per returned order.
- Repeat-return customers
- The tail that signals abuse or a product problem.
Day one
Questions you can ask
Returns & CX
What is driving the returns spike in apparel, and what would fix it?
Returns & CX
Which SKUs have a returns rate well above the category norm, and why?
Returns & CX
Which customers show returns-abuse patterns worth flagging?
Returns & CX
What does our refund policy actually cost us in margin per month?
Every answer comes back in your own numbers, with a clear next step.
FAQ
Common questions
- Can it find the cause of returns, not just the rate?
- Yes. It decomposes the returns rate by SKU, category and reason and benchmarks it against category norms, so you see that, say, one apparel line returns far above the fashion norm on sizing - a fixable cause, not just a number.
- Does it detect returns fraud?
- It surfaces the customer and SKU patterns associated with returns abuse and fraud so the small share driving most of the cost is visible. You decide what to do; the agent gives you the signals and the exposure.
- How does it benchmark returns?
- Against category norms from its knowledge base - apparel, electronics and other categories return at very different rates, so a 30% rate that is alarming for electronics may be normal for fashion. The agent frames your number against the right benchmark.
Who this is for
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250 free credits, no card. Connect a source, ask a real question, and see the answer in your data.