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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.
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
| name | winback-list-builder |
| description | 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. |
Your support history knows exactly which customers wobbled: who hit a problem, who left a low score, who hasn't ordered since a rough experience. That's the most qualified win-back audience you'll ever build — and it's sitting in Gorgias, not your ESP. This recipe turns it into a clean segment you can drop into Klaviyo, Postscript, or wherever your flows live. Read-only.
| Variable | Example |
|---|---|
{{SEGMENT_CRITERIA}} | "left CSAT ≤ 3 in the last 90 days on a now-resolved ticket" |
{{WINDOW}} | "the last 90 days" |
{{FIELDS}} | "email, first name, order count, last issue" |
Using the Gorgias MCP, build a win-back segment from my support data over {{WINDOW}}.
- Find customers matching: {{SEGMENT_CRITERIA}}.
- Return them as an export-ready list with these fields: {{FIELDS}}.
- Group them by why they're on the list (had a defect, slow shipping, low CSAT, abandoned after a question) — the reason should drive the message.
- For each group, suggest a win-back angle and offer that actually addresses what went wrong.
- Exclude anyone with an open ticket — don't re-market to someone you're still resolving.
Nothing in Gorgias — read-only. It produces a list of your own customers' contact details for you to load into your own email/SMS tool. Handle it like any customer export.
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.
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.
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.
