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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
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.
| name | pdp-gap-finder |
| description | 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. |
Every pre-sales question your team answers is a question your product page failed to answer first — and every one of those is a shopper who hesitated, bounced, or bought the wrong thing and returned it. Your support queue is the cheapest PDP research you'll ever get. This recipe finds the products customers ask about most, tells you exactly what's missing from each page, and ranks the fixes by impact. Read-only.
| Variable | Example |
|---|---|
{{WINDOW}} | "the last 90 days" |
{{SCOPE}} | "the apparel category" or a specific product (optional — omit for all) |
{{TOP_N}} | "the top 10 products" |
Using the Gorgias MCP, find where my product pages are failing shoppers, based on {{SCOPE}} over {{WINDOW}}.
- Look at pre-sales conversations and tickets that reference specific products — both before purchase (questions, hesitations) and after (confusion, "this wasn't what I expected", returns).
- Group by product and rank by how much support volume each one generates. Focus on {{TOP_N}}.
- For each product, identify the specific thing shoppers keep asking or getting wrong — sizing, materials, compatibility, dimensions, care, what's in the box.
- Output a table: Product | What shoppers ask or get wrong | What to add to the PDP | Priority. Populate it only from products actually referenced in real conversations — no generic advice.
- Call out the single highest-leverage page to fix first, and why.
Nothing — read-only. Hand the table to whoever owns the storefront.
returns-reduction: products with the worst PDP gaps are usually the worst return offenders too — fix the page, cut the returns.intent-gap-guidance candidates: if shoppers keep asking it, your AI Agent should answer it too.