Workflow

Amazon FBA Product Profitability Stress Test

Workflow that stress-tests Amazon FBA product ideas by checking competitor density, price range, review barriers, and cross-platform demand before committing inventory budget.

Overview

Launching an FBA product without stress-testing the niche is how sellers lose thousands on dead inventory. This workflow takes a product idea, searches Amazon for competitor density and price range, checks Google for search demand, and searches TikTok for trending signals. It outputs a go/no-go scorecard with hard data instead of gut feelings.

Trigger

On-demand per product idea evaluation.

Schedule

On-demand

Workflow Steps

1

Search Amazon for Competitors

Query Amazon for the product category. Count competitors, extract price range, and note average review counts.

2

Check Google Search Demand

Search Google for the product to estimate search volume and check if organic results show buying intent.

3

Check TikTok Trending Signal

Search TikTok for the product to see if creators are making content about it. Trending on TikTok often precedes Amazon sales spikes.

4

Calculate Stress Test Score

Score the opportunity based on competitor count (fewer is better), price range (higher margin potential), demand signals, and trend momentum.

5

Output Go/No-Go Scorecard

Generate a structured scorecard with all data points and a recommendation.

Python Implementation

Python
import requests, os, json

API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}
TH = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

def search_amazon(product: str) -> dict:
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers=H,
        json={"query": product, "platform": "amazon"},
        timeout=15,
    )
    results = resp.json().get("organic_results", [])
    prices = [r.get("price", 0) for r in results if r.get("price")]
    reviews = [r.get("reviews", 0) for r in results if r.get("reviews")]
    return {
        "competitor_count": len(results),
        "price_min": min(prices) if prices else 0,
        "price_max": max(prices) if prices else 0,
        "avg_reviews": sum(reviews) / len(reviews) if reviews else 0,
    }

def search_google_demand(product: str) -> dict:
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers=H,
        json={"query": f"buy {product}", "country_code": "us"},
        timeout=15,
    )
    data = resp.json()
    results = data.get("organic_results", [])
    buying_intent = sum(1 for r in results if any(kw in r.get("title", "").lower() for kw in ["buy", "best", "review", "price", "deal"]))
    return {"result_count": len(results), "buying_intent_signals": buying_intent, "has_shopping": "shopping_results" in data}

def search_tiktok_trend(product: str) -> dict:
    resp = requests.post(
        "https://api.scavio.dev/api/v1/tiktok/search",
        headers=TH,
        json={"query": product},
        timeout=15,
    )
    results = resp.json().get("results", [])
    return {"tiktok_results": len(results), "trending": len(results) > 5}

def stress_test(product: str) -> dict:
    amazon = search_amazon(product)
    google = search_google_demand(product)
    tiktok = search_tiktok_trend(product)

    score = 0
    if amazon["competitor_count"] < 15: score += 25
    if amazon["avg_reviews"] < 500: score += 20
    if amazon["price_max"] > 25: score += 20
    if google["buying_intent_signals"] > 3: score += 15
    if tiktok["trending"]: score += 20

    return {
        "product": product, "score": score,
        "verdict": "GO" if score >= 60 else "MAYBE" if score >= 40 else "NO-GO",
        "amazon": amazon, "google": google, "tiktok": tiktok,
    }

result = stress_test("portable blender for smoothies")
print(f"Product: {result['product']}")
print(f"Score: {result['score']}/100 -> {result['verdict']}")
print(f"Amazon: {result['amazon']['competitor_count']} competitors, {result['amazon']['price_min']}-{result['amazon']['price_max']} USD")
print(f"TikTok trending: {result['tiktok']['trending']}")

JavaScript Implementation

JavaScript
const H = {'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};
const TH = {'Authorization': 'Bearer '+process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json'};

async function searchAmazon(product) {
  const r = await fetch('https://api.scavio.dev/api/v1/search', {method:'POST', headers:H, body:JSON.stringify({query:product, platform:'amazon'})});
  const results = (await r.json()).organic_results || [];
  const prices = results.map(r=>r.price).filter(Boolean);
  const reviews = results.map(r=>r.reviews).filter(Boolean);
  return {competitorCount:results.length, priceMin:Math.min(...prices)||0, priceMax:Math.max(...prices)||0, avgReviews:reviews.length?reviews.reduce((a,b)=>a+b,0)/reviews.length:0};
}

async function searchGoogleDemand(product) {
  const r = await fetch('https://api.scavio.dev/api/v1/search', {method:'POST', headers:H, body:JSON.stringify({query:'buy '+product, country_code:'us'})});
  const data = await r.json();
  const results = data.organic_results || [];
  const buyingIntent = results.filter(r=>['buy','best','review','price','deal'].some(kw=>(r.title||'').toLowerCase().includes(kw))).length;
  return {resultCount:results.length, buyingIntentSignals:buyingIntent, hasShopping:'shopping_results' in data};
}

async function searchTiktokTrend(product) {
  const r = await fetch('https://api.scavio.dev/api/v1/tiktok/search', {method:'POST', headers:TH, body:JSON.stringify({query:product})});
  const results = (await r.json()).results || [];
  return {tiktokResults:results.length, trending:results.length>5};
}

async function stressTest(product) {
  const [amazon, google, tiktok] = await Promise.all([searchAmazon(product), searchGoogleDemand(product), searchTiktokTrend(product)]);
  let score = 0;
  if (amazon.competitorCount<15) score+=25;
  if (amazon.avgReviews<500) score+=20;
  if (amazon.priceMax>25) score+=20;
  if (google.buyingIntentSignals>3) score+=15;
  if (tiktok.trending) score+=20;
  const verdict = score>=60?'GO':score>=40?'MAYBE':'NO-GO';
  return {product, score, verdict, amazon, google, tiktok};
}

const result = await stressTest('portable blender for smoothies');
console.log('Product: '+result.product);
console.log('Score: '+result.score+'/100 -> '+result.verdict);
console.log('Amazon: '+result.amazon.competitorCount+' competitors, $'+result.amazon.priceMin+'-$'+result.amazon.priceMax);
console.log('TikTok trending: '+result.tiktok.trending);

Platforms Used

Amazon

Product search with prices, ratings, and reviews

Google

Web search with knowledge graph, PAA, and AI overviews

TikTok

Trending video, creator, and product discovery

Frequently Asked Questions

Launching an FBA product without stress-testing the niche is how sellers lose thousands on dead inventory. This workflow takes a product idea, searches Amazon for competitor density and price range, checks Google for search demand, and searches TikTok for trending signals. It outputs a go/no-go scorecard with hard data instead of gut feelings.

This workflow uses a on-demand per product idea evaluation.. On-demand.

This workflow uses the following Scavio platforms: amazon, google, tiktok. Each platform is called via the same unified API endpoint.

Yes. Scavio's free tier includes 250 credits per month with no credit card required. That is enough to test and validate this workflow before scaling it.

Amazon FBA Product Profitability Stress Test

Workflow that stress-tests Amazon FBA product ideas by checking competitor density, price range, review barriers, and cross-platform demand before committing inventory budget.