Web Scraping for Price Monitoring: Build an E-Commerce Price Tracker (2026)

Contains affiliate links. Disclosure.

Price monitoring is one of the highest-ROI applications of web scraping. Retailers, brands, and e-commerce teams use it to:

• Track competitor pricing in real time
• Automatically match or undercut competitor prices
• Identify price drops for affiliate marketing alerts
• Monitor MAP (Minimum Advertised Price) compliance

This guide builds a complete price tracker: scraping with Apify, storage in a database, and alerting via Make.com.

Architecture Overview

[Apify Actor: Scrape product prices]
        ↓ (runs on schedule)
[Apify Dataset: Store raw data]
        ↓ (API webhook on completion)
[Make.com: Receive webhook, process data]
        ↓
[PostgreSQL/Airtable: Store time-series prices]
        ↓ (check price change > threshold)
[Slack/Email: Alert team on price change]

---

Step 1: Define Your Products to Track

Create a product list with URLs:

[
  {
    "product": "Sony WH-1000XM5",
    "sku": "B09K3ZXSGH",
    "competitors": [
      "https://www.amazon.com/dp/B09K3ZXSGH",
      "https://www.bestbuy.com/site/6517780.p",
      "https://www.walmart.com/ip/123456789"
    ]
  }
]

---

Step 2: Scrape Prices with Apify

Use Apify to scrape product pages. Either use a pre-built Actor (Amazon, Best Buy, etc.) or write a custom CheerioCrawler:

import { Actor } from 'apify';
import { CheerioCrawler } from 'crawlee';

await Actor.init();

const { products } = await Actor.getInput();

const urls = products.flatMap(p =>
  p.competitors.map(url => ({ url, userData: { product: p.product, sku: p.sku } }))
);

const crawler = new CheerioCrawler({
  async requestHandler({ $, request }) {
    const { product, sku } = request.userData;

    // Generic price extraction — adjust selectors per site
    const priceText = (
      $('[data-testid="price"]').first().text() ||
      $('.price').first().text() ||
      $('[itemprop="price"]').first().attr('content') ||
      ''
    ).replace(/[^0-9.]/g, '');

    const price = parseFloat(priceText) || null;

    await Actor.pushData({
      product,
      sku,
      url: request.url,
      price,
      currency: 'USD',
      scraped_at: new Date().toISOString(),
    });
  },
});

await crawler.run(urls);
await Actor.exit();

---

Step 3: Schedule Daily Runs

In Apify Console → Schedules → New:

• Cron: 0 8 * * * (8 AM UTC daily)
• Actor: your price monitor Actor
• Input: your product list
• Webhook: trigger Make.com on completion

---

Step 4: Store History in a Database

Use Apify's webhook to push data to Make.com, then to Airtable or PostgreSQL:

Make.com scenario:

1. Trigger: HTTP Webhook (from Apify)
2. HTTP: GET https://api.apify.com/v2/datasets/%7BdatasetId%7D/items?fpr=use-apify
3. Iterator: Process each price record
4. Airtable: Create record (product, price, date, URL)
5. Filter: Check price < previous_price * 0.95 (>5% drop)
6. Slack: Send alert if price drop detected

// Slack alert message
"Price drop alert: {product} is now ${price} on {url} (-{change}% from yesterday)"

---

Step 5: Visualize Price Trends

With historical data in Airtable or PostgreSQL, build a simple price chart:

import pandas as pd
import matplotlib.pyplot as plt

# Load from database
df = pd.read_sql("SELECT product, price, scraped_at FROM prices WHERE sku = 'B09K3ZXSGH'", conn)
df['scraped_at'] = pd.to_datetime(df['scraped_at'])

# Plot price over time by source
for url, group in df.groupby('url'):
    plt.plot(group['scraped_at'], group['price'], label=url.split('/')[2])

plt.title('Price History: Sony WH-1000XM5')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.savefig('price_chart.png')

---

Cost Estimate

Scale	Pages/day	Apify cost/month	Total
10 products × 3 sites	30	~$1	Minimal
100 products × 5 sites	500	~$3–5	Low
1,000 products × 5 sites	5,000	~$30–50	Medium
10,000 products	50,000	~$300–500	Use Apify Scale plan

For Amazon specifically, proxy costs are higher due to anti-bot measures. Use Apify's residential proxies which are included in the platform subscription.