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Cluster why customers actually return products — sizing, quality, expectation gaps — by product, from ticket and return language, so you can fix the root cause instead of just processing the refund. Returns are a margin killer; this finds where they start. Read-only.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Build a targeted win-back segment from support signal — customers who hit a problem, left low CSAT, or went quiet after a bad experience — as a list you can hand to your email/SMS tool. The most qualified re-engagement audience you have is in your tickets. Read-only.
Turn the last 7 days of Gorgias tickets into an executive-ready weekly CX digest — analyzed across five business functions (Product, Marketing, Ecommerce/Revenue, Product Marketing, Leadership) with a TLDR, signal-status tagging, a PDP-gap table, and owner-tagged actions. Read-only; optionally publishes to Notion.
Build a back-in-stock demand list from customers who asked about out-of-stock or discontinued products — ranked by demand, with a per-product notify list — to drive back-in-stock campaigns and inform the next buy. Read-only.
Turn recurring customer language, objections, and praise from tickets into marketing angles, FAQ content, and on-site or ad copy — written in your customers' own words. The best copy you'll write is already in your inbox. Read-only.
Find the products generating the most pre-sales questions and post-purchase confusion, and get a concrete list of what to add to each product page. Turns support tickets into PDP improvements that lift conversion and cut returns. Read-only.
Analyze how your Shopping Assistant is performing as a revenue channel — most recommended products, best-converting recommendation pairs, revenue and time-to-purchase per conversation, and the pre-sales topics and handover reasons behind it. Read-only. Analytics are in beta — directional.
| name | returns-reduction |
| description | Cluster why customers actually return products — sizing, quality, expectation gaps — by product, from ticket and return language, so you can fix the root cause instead of just processing the refund. Returns are a margin killer; this finds where they start. Read-only. |
Returns are one of the biggest silent margin leaks in DTC, and the reason for each one is sitting in plain language in your tickets — "ran small," "looked different online," "stopped working after a week." This recipe clusters the why behind returns by product, so you can fix the source (a misleading PDP, a sizing gap, a quality issue) instead of just eating the refund again next month. Read-only.
| Variable | Example |
|---|---|
{{WINDOW}} | "the last 90 days" |
{{SCOPE}} | "all products", a category, or one SKU |
Using the Gorgias MCP, analyze why customers are returning products in {{SCOPE}} over {{WINDOW}}.
- Pull tickets and conversations about returns, exchanges, and "this wasn't what I expected" — use tags where they exist, and customer language where they don't (label those (heuristic)).
- Cluster the reasons customers give: sizing/fit, quality/defect, didn't match the description or photos, shipping/damage, changed their mind, wrong item.
- Break it down by product — which products drive the most returns, and the dominant reason for each.
- For the worst offenders, name the specific expectation gap and the root-cause fix: PDP copy, a sizing guide, better photos, a QA flag for the buying team, or a packaging change.
- Rank by volume × fixability — where will one change remove the most returns.
Nothing — read-only.
pdp-gap-finder to fix the page.