Plain-English definitions for the terms that come up in price monitoring, restock alerting, and ecommerce pricing intelligence.
Automated, ongoing monitoring of a product's price on a specific URL, with the goal of being alerted to meaningful changes.
Price tracking software checks a product page on a schedule (typically daily) and stores the observed price each time. Over weeks or months this produces a price history. Users set targets or thresholds; when a check shows the price has crossed the threshold, the service sends an alert (usually email).
A notification sent when a previously sold-out product becomes available again. Particularly useful for limited-supply categories like GPUs, sneakers, and consoles.
Restock alerts work by polling a product page on a schedule and detecting when its stock status flips from out-of-stock to in-stock. Modern restock-alert systems are size-aware for apparel and footwear — you only get pinged when the specific size you wanted (say, a men's 10) is back.
A user-defined price floor. The tracker only sends an alert when the observed price drops below this number.
Setting a target price filters out drops that aren't meaningful to you. If a $400 product drops to $390, that's not interesting; if it drops to $300 — your target — that's worth an email. Targets are different from threshold alerts, which trigger on percentage drops regardless of the absolute number.
An alert that triggers when a price drops by a defined percentage (e.g. 5%, 10%) regardless of the absolute target.
Threshold alerts compare today's price to the recent baseline (often the previous price) and fire when the drop exceeds your chosen percentage. A 5% threshold avoids penny-drop noise; a 10% threshold further filters to genuinely meaningful sales. Pair with a target price for the strongest signal.
The complete record of observed prices for a product over time, usually rendered as a chart.
Price history is the most useful single artifact a price tracker produces. It lets you distinguish real sales from fake "was" prices, identify seasonal lows (Black Friday, end of quarter), and decide whether today's price is genuinely a discount or a return to the historical average.
The retailer practice of changing prices frequently — sometimes multiple times per day — based on demand, competitor prices, or inventory.
Dynamic pricing is everywhere on Amazon, marketplaces, and increasingly on direct-to-consumer storefronts. Algorithms adjust the listed price to maximize revenue. For shoppers, this means a product's price five minutes from now may be different from now — which is exactly why automated price tracking is necessary.
Using data from automated price tracking to inform pricing decisions — typically by ecommerce operators or brands.
Price intelligence is the broader business term for what price tracking enables. A brand might track 50 competitor SKUs across three retailers to spot when rivals discount, or a buyer might track 20 supplier prices to time purchase orders. The toolchain is the same as consumer price tracking, but the use case is operational.
Monitoring competitor product pages (typically on the same retailer) to detect price changes, sales, or new product launches.
A specific application of price intelligence. Ecommerce brands run competitor price tracking to maintain price parity, undercut on key SKUs, or react to flash sales. The cheapest implementation: paste competitor URLs into a price tracker and get daily emails. The most expensive: a custom scraper plus BI pipeline.
Tracking whether retailers are selling below the brand's Minimum Advertised Price (MAP) — a contractually agreed floor.
Many brands enforce a Minimum Advertised Price for their products. Authorized retailers can sell below it in private (a quote, an in-cart price) but cannot publicly list a price below the MAP. MAP monitoring uses automated tracking to detect MAP violations across distribution channels — a brand operations problem rather than a consumer one.
Automatically extracting deal information (current price, original price, discount percent) from retailer pages or deal aggregator sites.
Deal scraping powers both consumer-facing deal feeds (like the public /deals page on PricePing) and B2B price intelligence platforms. The hard parts are: handling anti-scraping defenses, normalizing prices across currencies, detecting fake "was" prices, and filtering low-quality or expired deals.
A high-demand, limited-supply product release — sneakers, GPUs, gaming consoles — that typically sells out within minutes of going live.
Hype drops are the original case for restock alerts. Once a hype product sells out, the next chance to buy at MSRP is when the retailer restocks the SKU — often weeks later, often with no public announcement. Restock-monitoring tools poll the product page frequently and ping users the moment availability flips.
Identifying which currency a scraped price is in — by URL TLD, page text markers (₹, Rs, €, $), or structured data.
Currency detection matters because the same retailer may serve different currencies to different visitor IPs. If a tracker compares a price in USD against a previous price in INR without checking, it can produce nonsense alerts ("99% off!") when reality is that the units just changed. Robust trackers lock the currency on first scrape and reject mismatched updates.