11 Keepa Workflows
for Claude & ChatGPT

For each of these competitor ASINs [LIST_ASINS], pull the last 180 days of Buy Box, new-offer, and used-offer price history via Keepa. Summarize the volatility for each, flag any sustained price increases or decreases of 10%+, and identify which products changed Buy Box winner more than 5 times.

Pull the 3-year sales-rank history for these ASINs [LIST_ASINS] via Keepa. Identify any seasonal patterns (peaks/troughs by month). For each, tell me when the BSR typically rises and falls, and the average rank delta during peak weeks.

For each of my brand's ASINs [LIST_ASINS], use Keepa to list the current sellers on the listing. Flag any seller IDs that weren't there 30 days ago, and any sellers from China (shipsFromChina flag). Summarize each new seller's feedback volume and product count.

Use Keepa's Product Finder (keepa_find_products) to find products in the [CATEGORY_ID] category with: current Buy Box price between $15 and $60, current sales rank under 50,000, fewer than 10 active sellers, and at least 100 reviews. Return the top 25 candidates sorted by estimated monthly units sold, and note the Amazon presence on each listing.

Look at our Amazon Ads search-term report from [DATE_RANGE]. Find any sponsored-products campaigns where ACoS jumped more than 50% day-over-day. For the top ASINs in those campaigns, pull Keepa Buy Box winner history around the same time window. Tell me which spikes correlate with us losing the Buy Box.

For each of our top 20 Sponsored Products keywords by spend, run Keepa's search_products and check which competitor ASINs rank organically. Pull their current Buy Box prices via Keepa and compare to our own Buy Box price (via SP-API). Flag any keyword where we're bidding aggressively but our price is more than 15% above the market — those are bid-gap candidates.

For each keyword we currently bid on in Sponsored Products, use Keepa's search_products to identify the top 20 ranking ASINs. Cross-reference with our own catalog (via SP-API listings). Surface keywords where multiple competitor ASINs rank but we have zero impressions — those are organic gaps we should expand into.

Pull my current FBA inventory via SP-API and order velocity for the last 90 days. Cross-reference with Keepa BSR trajectory for each ASIN over the same window. Rank my SKUs by restock priority, weighting heavily for: high BSR (sales) growth, low days-of-supply, and rising competitor activity on the listing (Keepa seller count delta).

For my top 10 listings (highest revenue last 90 days, via SP-API orders), fetch the current title/bullets via SP-API. Then use Keepa best_sellers in the same category to identify the category leaders. Pull their titles via Keepa product_lookup. Summarize the keyword patterns and value-prop framings the leaders use that my listings don't.

Build a competitive intelligence summary for [BRAND] covering last quarter. Include: - Our quarterly Amazon Ads spend, ACoS, TACoS (via Ads reports) - Our quarterly revenue from this brand (via SP-API orders) - For the top 5 competing ASINs in our primary category: their current price, current Buy Box owner, and BSR trajectory from Keepa - Buy Box winner changes for our top ASINs over the quarter (Keepa Buy Box history — count winner-flips and longest sustained competitor wins) - Current organic ranking on our 10 priority keywords (Keepa search_products — single-point snapshot, not historical trend) Format as a 1-page narrative + a section with supporting numbers. Recommend 2 priority actions for next quarter.

Build me a holiday readiness checklist for [BRAND]: - Pull last 3 years of BSR history (Keepa) for each of my ASINs. Identify the typical peak week (Black Friday vs Cyber Monday vs early December). - Compare to my current FBA inventory levels (SP-API). Flag SKUs below 90 days of historical peak-week velocity. - For each at-risk SKU, pull Keepa Buy Box and seller-count history from last year's Q4 — surface any patterns (e.g. hijackers appearing around Thanksgiving) we should pre-empt. - Look at my current Amazon Ads campaign budgets (Ads reports). Recommend whether to pause campaigns on stock-out-risk SKUs and shift spend to better-stocked alternatives. Output as a prioritized action list with deadlines.

Token costs you should know before running prompts: - keepa_category_lookup: 1 token per call (up to 10 ids) - keepa_product_lookup: 1+ per ASIN; +2 if buybox=true; +1 if you force a fresh refresh - keepa_buybox_history: 3 tokens per ASIN (product + buybox decoration) - keepa_search_products: 10 tokens per call (up to 20 results) - keepa_seller_storefront: 1+ tokens; expensive if include_storefront=true on big sellers - keepa_best_sellers: 50 tokens per call - keepa_deals: 5+ tokens depending on filter complexity - keepa_lightning_deals: 1 per ASIN; 500 for the full list (we refuse without include_all=true) - keepa_find_products: varies; complex filters can cost 50+ tokens Plan large batches in advance. The €19 Keepa tier gives 1 token/minute (60/hr); €49 gives 20/minute. Tools cache for ~1 hour by default — don't bypass with update=0 unless freshness is critical.

Match your Keepa tier to your expected MAP usage: - €19/mo Power User (1 tok/min, ~60 tok/hr): occasional manual queries. Fine for trying prompts 1-4. Not enough for daily QBR workflows. - €49/mo (20 tok/min, ~1200 tok/hr): active analyst usage. Comfortable for prompts 5-9 run on demand. ~20 ASIN buybox lookups per minute. - €129/mo (60 tok/min, ~3600 tok/hr): daily agency workflows including QBR packs (prompt 10) and pre-holiday audits (prompt 11) across multiple brands. - €459/mo+ (250+ tok/min): heavy automation — scheduled scans, large catalog monitoring, multi-brand portfolios. Recommended once you're consistently quota-limited. You can stack Keepa subscriptions on one account to get more capacity. MAP surfaces tokens-left and quota-low warnings on every call so you'll see hits coming.