Price Monitoring

What price monitoring does

A price monitoring system visits a defined set of public product pages — on competitor websites, marketplaces or reseller stores — at intervals you choose. It records the price, any visible discount, and often stock status, then stores each reading with a timestamp.

Because readings are kept as a time series, you can see trends and patterns rather than just today’s number: how often a competitor changes prices, how deep their sale discounts go, and who tends to move first when the market shifts.

Who benefits from price tracking

• An e-commerce seller matching or beating competitor prices on its best-selling products without checking dozens of pages by hand.
• A D2C brand watching whether resellers and marketplace sellers keep to its recommended retail prices.
• A consumer electronics or appliance retailer tracking price drops during festival sales.
• A distributor or wholesaler following public supplier and competitor price lists for key product lines.
• A travel or hospitality business observing publicly displayed rates for comparable offerings, where terms allow.

If someone on your team currently opens a list of competitor pages every morning and types prices into a spreadsheet, price monitoring replaces that routine with something faster, more consistent and far easier to analyse later.

What’s included

• A product watchlist matching your products to the equivalent listings on each competitor or marketplace.
• Scheduled collection at the frequency you choose — daily is common, hourly for fast-moving categories where it is appropriate.
• Price history storage in a database or sheet, with every reading timestamped and linked to its source URL.
• Alerts when a price crosses your threshold, drops by more than a set amount, or a product goes out of stock — delivered by email, Telegram, WhatsApp or Slack.
• A simple dashboard or report showing current positions and trends.
• Health monitoring so you know when a page has changed and a reading could not be taken.

How we set it up

1. Build the watchlist. You share your product list and the competitors or marketplaces to track. We match products carefully, because comparing the wrong variant makes the whole dataset misleading.
2. Review each source. We check page structure, robots.txt and terms, and agree a polite collection rate.
3. Define rules. Which price counts — list price, sale price, price after a visible coupon — and how to treat bundles, pack sizes and shipping.
4. Build and test. Collectors are built and run against the watchlist, and readings are spot-checked against the live pages.
5. Set alerts. Thresholds and channels are configured so the right person hears about the right change.
6. Run and maintain. Collection runs on schedule, with alerts if a source breaks.

Technology behind it

Collectors are written in Python, using plain HTTP requests where pages allow and Playwright where prices are loaded by JavaScript. Larger watchlists run on Scrapy for queueing and throttling. Readings are stored in PostgreSQL or a Google Sheet, and pandas handles comparisons, trend calculations and reports.

Every collector is rate-limited. We only read publicly visible prices, and we do not log in, bypass CAPTCHAs or get around blocks to reach member-only pricing.

What affects timeline and cost

• Number of products and sources — the watchlist size multiplies the work.
• Product matching difficulty — identical SKUs are easy; similar-but-different products need careful mapping.
• Check frequency — hourly monitoring needs more infrastructure than daily.
• Page complexity — JavaScript-rendered prices or prices that vary by location need extra handling.
• Reporting needs — a simple sheet versus a dashboard with charts and history views.

Common mistakes to avoid

• Poor product matching. Comparing a 500 ml pack to a 1 litre pack produces confident but wrong conclusions.
• Ignoring price context. A lower price with a long delivery time or no stock is not the same offer.
• Too many alerts. If every small change triggers a message, people stop reading them. Thresholds should reflect real decisions.
• No history. Only storing today’s price throws away the patterns that make monitoring valuable.

Price monitoring works well alongside competitor monitoring and product data collection. Our web scraping guide explains the wider picture. See all data services or share your watchlist with us.

Frequently asked questions