Amazon EV Accessory Research
Keyword to profit estimate — the go/no-go report before you order inventory
A full Amazon product-research workflow for EV accessories: expand keywords, shortlist ASINs, read BSR demand, map prices, mine reviews for complaint themes, profile competitors, source from China at 3 MOQ tiers, build unit economics, and score the niche — ending in a go/no-go report.
Install it in 3 steps
No sign-up. No CLI tools. Pick your AI, copy one line, paste it over.
Prefer doing it yourself? Buy the skill ($4.99), download the ZIP from your checkout confirmation, unzip it into the directory shown above, restart your AI session — same result. Already bought it? Your AI assistant can download it for you.
What’s inside
One SKILL.md (the playbook) + one starter template. A preview:
Amazon EV Accessory Research
When to use
- You're considering selling an EV accessory on Amazon (floor mats, console organizers, screen protectors, charging adapters, frunk storage) and need to know if the niche is worth entering
- You have a keyword idea but don't know how competitive it is, who the top sellers are, or what they're doing wrong
- You need a go/no-go report: demand, competition, China supply cost, profit margin, and the opportunity gap — before spending a dollar on inventory
- You want to find the weak spot in existing listings (bad reviews, missing features) so your version can position against it
- You're choosing between 2–3 product ideas and want them scored on the same framework
Workflow
- Keywords (input: product idea → output: keyword list)
Expand the seed keyword into 10–20 search terms: Amazon search-bar suggestions, related searches, long-tail variants. For each, note the search-volume tier (high / medium / low) if data is available, and mark buyer intent vs browsing — "model y floor mats 7 seater" is buyer intent, "tesla accessories" is browsing. The report only scores buyer-intent keywords. - ASIN (input: keyword list → output: ASIN shortlist)
For the top buyer-intent keywords, collect the top 10–20 ASINs. Record brand, seller type (FBA / FBM), and listing age. Drop sponsored-only listings — ads are not organic demand. Keep the ASINs that appear across multiple keywords; those are the real incumbents. - BSR (input: ASIN shortlist → output: demand read)
Record each ASIN's Best Sellers Rank in its category. Translate BSR into an estimated monthly sales band — and state the estimation method and its uncertainty. Flag demand concentration: winner-take-all (top 3 take most sales) or spread out (room for a new entrant)? - Price (input: ASIN shortlist → output: price landscape)
Record every price point and map the bands: budget / mid / premium. Note price clustering — a tight cluster means brutal price competition; a gap between bands is an opening. Record the price of the #1 seller separately; that's the price you must beat or justify. - Reviews (input: ASIN shortlist → output: review intelligence)
For each ASIN: rating, review count, review velocity (reviews per month). Read the 1-star and 3-star reviews and extract complaint themes: fit, quality, installation, packaging, smell. Complaints are product requirements for your version — write them as "must fix" items. - Competitors (input: review intelligence → output: competitor map)
Pick the 3–5 real competitors. For each: positioning, price, rating, review count, listing strengths (A+ content, video, image quality) and weaknesses (the complaint themes). Identify the attackable incumbent: high sales + low rating + fixable complaints. If every incumbent is 4.7★ with 10,000 reviews and no complaints, say so — that's a no-go signal. - China supply chain (input: product spec → output: sourcing options)
Define the product spec from the complaint themes (what your version must fix differently). Find 3–5 suppliers on 1688/Alibaba: MOQ, unit price at 500 / 1,000 / 3,000 units, lead time, customization options (logo, packaging, color). Note quality tiers and which tier matches the mid/premium band you're targeting. - Profit estimate (input: price + supply cost → output: unit economics)
Build the P&L: landed cost (unit + freight + duty + buffer), Amazon fees (referral + FBA), PPC budget, returns reserve. Compute contribution margin per unit at the target price and break-even units per month. Show the numbers at 3 price points (budget / mid / premium). If margin is under 25% after all costs at a realistic price, flag it. - Market opportunity (input: everything → output: verdict)
Score the niche on 4 dimensions, 1–5 each: demand (BSR/sales band), competition intensity (review counts, rating floor), differentiation room (complaint themes you can fix), margin headroom (can you clear 30% after all costs?). Give a go / no-go / go-with-conditions verdict, and name the single biggest risk in one sentence. - Report (input: everything → output: written report)
Write the report with template.md. Verdict on page one, then the evidence. Every number gets its source and date; estimates are labeled as estimates with the method named. The report must be usable by someone who wasn't in the room — no unexplained jargon, no missing steps.
Template preview
Amazon EV Accessory Research Report
Product idea: [e.g. Model Y console organizer] Research date: [YYYY-MM-DD] Analyst: [AI assistant + researcher name]
1. Verdict
[GO / NO-GO / GO WITH CONDITIONS]
One sentence why: […]
Single biggest risk: […]
2. Keyword map
Top buyer-intent keywords: […]
3. ASIN shortlist + demand read
Demand concentration: [winner-take-all / spread out] Notes: […]
4. Price landscape
Price bands: budget [$…–$…] / mid [$…–$…] / premium [$…–$…] #1 seller price: [$…] Clustering: [tight cluster = price war / gap between bands = opening] Notes: […]
5. Review intelligence
Must-fix items for our version:
- […]
- […]
- […]
