Customer Service
Returns processing automation
Automate returns authorisation decisions based on policy rules, customer history, and product category. Instant authorisation for straightforward cases, human review for exceptions.
40–60%
reduction in returns processing time
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Returns policy must be unambiguous and codified before automation. If your policy has subjective elements ('reasonable wear'), you need to define thresholds for the AI.
Fraud detection must be integrated — automated returns approval without checking for serial returners, wardrobing, or receipt fraud creates an exploitable loophole.
The customer experience improvement (instant refund for simple cases) is often more valuable than the operational efficiency. Measure both.
Experiment starter: Analyse your last 1,000 returns. Categorise them as 'straightforward' (clear policy match, standard product, within window) versus 'complex'. If 60%+ are straightforward, the automation case is strong. Pilot with the straightforward cases only.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in Customer Service
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