Turning a keyword export into an SEO map and backend search terms
Search visibility on a marketplace comes from putting the right terms in the right fields — title, bullets, and the hidden backend search-term box. You've got a raw keyword export from a research tool, but organizing hundreds of terms into a strategy by hand is slow. AI is good at clustering and placing them, as long as it works only from your real export and never invents search volumes or terms.
You are an Amazon SEO strategist. Build a keyword placement plan from the export below. Use ONLY the keywords in this data. Keyword export (terms with search volume or relevance, pasted from my research tool): {{keyword_export}} Product facts (so you can judge relevance): {{product_facts}} Backend search-term field limit: {{byte_limit}} Produce: 1. A clustered keyword map: primary terms, secondary terms, and long-tail variants, grouped by theme. 2. Placement — which cluster belongs in the title, which in bullets, and which in the backend field. 3. A backend search-term string within the byte limit: lower-case, space-separated, no commas, no repeated words, no words already in the title. 4. A short list of terms in my export that are irrelevant or risky and should be dropped. Rules: - Use only keywords present in my export. Do not invent search volumes, add new keywords, or import terms from your own knowledge. - Never include competitor brand names, trademarked terms, or other brands' product names — these violate marketplace policy and trigger suppression or IP complaints. - Flag any term that doesn't actually match this product; irrelevant keywords are a relevance and policy risk, not free traffic.
Fill in your details and the prompt updates live — then copy.
Primary: insulated water bottle; 32 oz water bottle Secondary: metal water bottle; stainless steel water bottle Long-tail: cup holder water bottle; car cup holder bottle Placement — Title: insulated water bottle, 32 oz, stainless steel. Backend: metal bottle cup holder friendly cold drinks reusable. Drop these: "YetiBrand alternative" — competitor trademark, do not use in title or backend (policy risk). "straw bottle" — this product has no straw, irrelevant.
The full workflow
- Export real search terms from your keyword tool or brand analytics before you start — don't ask the AI to guess volumes
- Run the prompt, then sanity-check that every suggested term actually appears in your export
- Remove any competitor brand or trademarked term the model kept, even if it has volume
- Paste the backend string into the listing and confirm it fits the real byte limit
- Re-run after a few weeks with fresh search-term reports to catch terms that are actually converting
Watch out for
Never put competitor brand names, trademarked terms, or other sellers' product names in your title or backend field — Amazon prohibits it and it triggers listing suppression and intellectual-property complaints. Make the model flag and drop them rather than chasing their search volume.
AI will invent plausible keywords and search volumes that were never in your data. Insist it use only your real export, and drop any term that doesn't match the actual product — an irrelevant keyword hurts relevance and can breach marketplace listing rules.
Where this comes from
Every use case on this site is grounded in real reports from working e-commerce sellers — not invented by us.