What this prompt does
This prompt asks Claude or ChatGPT to design a returns and exchange flow for an online store. You set [store_name] and [store_type], then describe the [return_window], the [return_reasons], the [return_rate], the [refund_methods], plus retention levers like [discount_percentage] and [store_credit_bonus], and the [framework]. The model designs the full customer journey — initiating a return, selecting a reason, choosing a return method, the exchange flow, tracking and status, refund processing, an admin dashboard, and the framework implementation.
It works because a smooth returns experience is a retention feature: it's what makes a customer trust buying again. By naming [return_reasons] and [return_rate], the model designs reason pickers and analytics that match your actual return patterns. The [discount_percentage] and [store_credit_bonus] variables power the "keep it for a partial refund" and "store credit bonus" levers that quietly reduce return volume and refund cost.
When to use it
- You're building self-service returns and want to cut support load from "where is my refund?" tickets.
- You need a multi-step return flow with eligibility checks and clear ineligibility explanations.
- You want an exchange flow with real-time variant stock and price-difference handling.
- You're designing prepaid label generation, QR drop-off, and carrier pickup scheduling.
- You need a refund pipeline with status tracking and proactive notifications.
- You want retention levers (partial-refund keep offer, store credit bonus) built into the flow.
Example output
The model returns a journey-based design document, step by step from initiating a return to the admin dashboard. Each step lists concrete UI — a return-vs-exchange toggle, a structured reason picker with sub-questions, a return-method selector with refund-timeline estimates, and a visual status pipeline from request to refund issued. The closing [framework] section adds a multi-step form with progress, label generation via carrier API, barcode rendering, and mobile optimization. You get a detailed UX spec, not finished checkout code.
Pro tips
- Set
[return_reasons]with rough percentages so the reason picker and analytics reflect where your returns actually come from. - Tune
[discount_percentage]and[store_credit_bonus]to numbers you'd really offer — they directly shape the retention prompts. - Use
[return_window]to encode tiered rules (for example longer windows for premium members) in the eligibility check. - Keep
[refund_methods]accurate so the refund step only shows options you actually support. - Emphasize the "where is my refund?" self-service status — it's the single biggest support-ticket reducer in the flow.
- Ask the model to expand the admin dashboard if you want fraud detection and policy A/B testing detailed.
