ecom-intel-engine

Category-agnostic e-commerce product intelligence. Trend detection, price-volatility, and opportunity/sourcing scores — for any product category.

It does not scrape. You feed it product records from any source (XCrawl, Keepa, Rainforest, Amazon PA-API, a CSV…) and it returns scores. Scrapers break and proxies get blocked; the scoring logic is the durable asset.

Why decoupled?

Real-time scraping is fragile (Amazon CAPTCHAs, proxy bans, layout changes). By separating data acquisition from analysis, the same engine keeps working no matter where the data comes from — and you can sell the analysis as data, an API, or an SDK.

Install

npm install ecom-intel-engine

Quick start

const { analyzeDataset } = require('ecom-intel-engine');

const products = [
  { asin: 'B01', title: 'Vitamin C Serum', price: 24.99, rating: 4.6, reviews: 8200, category: 'Skincare',
    reviewsHistory: [{ count: 6000, at: '2026-05-01' }, { count: 8200, at: '2026-06-01' }] },
  { asin: 'B02', title: 'Jade Roller', price: 9.99, rating: 4.3, reviews: 120, category: 'BeautyTools' },
];

const { products: analyzed, summary } = analyzeDataset(products);

console.log(summary.topOpportunities);
// [{ id: 'B01', title: 'Vitamin C Serum', score: 81.4, category: 'Skincare' }, ...]

What it computes

| Output | Meaning | |--------|---------| | trendingScore | Relative review growth over the available history window | | isTrending | True when baseline ≥ 100 reviews and growth ≥ 50% (noise-filtered) | | reviewVelocityPerDay | Reviews gained per day (needs timestamped history) | | priceVolatility | Coefficient of variation of price history | | opportunityScore | 0–100 sourcing/private-label viability (demand · margin · quality · competition) | | opportunityRank | Rank within the dataset |

Opportunity score dimensions

• demand — log-scaled review count (proven sales)
• margin — price headroom (sweet spot ~$20–60)
• quality — rating fit (4.0–4.8 ideal; suspiciously perfect penalized)
• competition — reviews vs. category median (below median = opening)

Weights are tunable:

analyzeDataset(products, { weights: { demand: 0.4, margin: 0.2, quality: 0.2, competition: 0.2 } });

API

• analyzeDataset(products, opts?) → { products, summary } (computes category medians for competition scoring)
• analyzeProduct(product, ctx?) → single product report
• detectTrend(product, opts?) → trend block
• priceStability(product) → price volatility block
• opportunityScore(product, ctx?) → { score, breakdown }

Input shape

{
  id | asin: string,
  title: string,
  price: number,
  rating: number,        // 0-5
  reviews: number,
  category?: string,
  reviewsHistory?: [{ count: number, at: string }],
  priceHistory?:   [{ price: number, at: string }],
  isBestSeller?: boolean,
  isAmazonChoice?: boolean
}

Want the data, not the code?

A ready-made dataset of 829 real Amazon products scored with this engine (CSV + JSON + market report) is available here: 👉 Amazon Product Opportunity Scores

License

MIT