Ecommerce Price Monitoring

When: User wants to track competitor prices across product pages and get notified when prices change.

Pipeline

1. Scrape product pages -> apify/e-commerce-scraping-tool
   • Key input: startUrls (competitor product page URLs), proxyConfiguration
2. Match products across sites -> tri_angle/e-commerce-product-matching-tool
   • Pipe: results[].url + results[].name -> matching input
   • Key input: source dataset ID from step 1, target product list
3. Compare vs. baseline (n8n logic: read Google Sheets last_price, compute % change, filter if changed)
4. Alert via Telegram/Slack node with price delta

Output fields

Step 1: price, currency, name, sku, availability, url Step 2: matched pairs with similarityScore, sourceProduct, targetProduct

Cost estimate

apify/e-commerce-scraping-tool is pay-per-result. For 200 product URLs daily, expect ~$0.50-$1/run depending on site complexity.

Gotcha

Many e-commerce sites use bot protection. If e-commerce-scraping-tool returns empty prices, fall back to apify/camoufox-scraper with residential proxy. Set sessionPoolName to reuse sessions and reduce blocks.

Amazon product and review tracking

When: User wants to monitor own or competitor Amazon listings for price drops or review score changes.

Pipeline

1. Extract Amazon data -> apify/e-commerce-scraping-tool
   • Key input: startUrls (Amazon product URLs), extractReviews (bool)
2. Compare vs. stored baseline (n8n: read last values from Sheets or DB)
3. Alert on new low price or rating drop (n8n: If node + Telegram/Slack send)

Output fields

price, currency, rating, reviewsCount, title, asin, availability

Cost estimate

Flat per-result pricing. 50 ASINs daily ~ $0.10-$0.25/run.

Gotcha

Amazon aggressively rotates prices and sometimes shows regional prices. Always store currency alongside price. For review text (not just counts), search apify actors search "amazon reviews" --user-agent apify-agent-skills/apify-ultimate-scraper for a dedicated Actor.

Supplier catalog extraction to draft products

When: User wants to pull new products from a supplier portal and create draft listings with AI-enriched descriptions.

Pipeline

1. Crawl supplier catalog -> apify/playwright-scraper (JS-heavy portals) or apify/cheerio-scraper (static HTML)
   • Key input: startUrls (supplier category pages), pseudoUrls (product URL patterns), maxCrawlPages
2. Extract product content -> apify/website-content-crawler (optional second pass for detail pages)
   • Pipe: results[].url -> startUrls
   • Key input: maxCrawlPages (1 per product), htmlTransformer: "readableText"
3. AI rewrite (n8n: OpenAI node generates SEO title + bullets from raw specs)
4. Create draft product (n8n: Shopify node POST /products.json with status: "draft")

Output fields

Step 1/2: text, url, metadata.title, inline image URLs

Cost estimate

Depends on catalog size. playwright-scraper is PPE; 500 product pages ~ $1-3.

Gotcha

Supplier portals often require login. Use apify/playwright-scraper with initialCookies or a pre-login script in preNavigationHooks. Never hardcode credentials - pass via Actor input from n8n credentials store.

Multi-site deal and coupon monitoring

When: User wants to detect when competitors run promotions or publish coupon codes so marketing can respond.

Pipeline

1. Scrape deals pages -> apify/e-commerce-scraping-tool
   • Key input: startUrls (competitor deal/sale page URLs), proxyConfiguration
2. Dynamic JS deal pages (fallback) -> apify/camoufox-scraper
   • Pipe: failed URLs from step 1 -> startUrls
3. AI extract promotion details (n8n: OpenAI node extracts discount %, promo code, expiry from raw text)
4. Dedup and alert (n8n: compare against stored deals DB, Slack notify on new deals)

Output fields

Raw: price, discountText, url; AI-extracted: promoCode, validUntil, discountPercent, category

Cost estimate

Light scraping - deals pages are few. Expect < $0.20/run for 20 competitor pages.

Gotcha

Promo codes and flash deals may only be visible after login or in geofenced regions. Test each target URL manually first. AI extraction of expiry dates is unreliable - treat as best-effort signal, not exact data.