Competitor Price Monitor — Price & Stock Change Alerts

Watch any competitor's product pages and get a clean, typed alert the moment something moves: a price drop, a restock, a sell-out, a sale starting or ending, a new or removed variant. This is a monitor, not a one-shot scrape — keep it on a schedule and each run reports only what changed since the last one, so you pay for signal, not for re-dumping the same catalogue every day.

What it does

On every scheduled run, for each product URL you give it:

1. Reads the product reliably. It first tries the store's own product endpoint (Shopify /products/<handle>.js — clean structured data with every variant's price, SKU and in-stock flag), then falls back to JSON-LD, OpenGraph/microdata, and finally a text parse. A resilient browser handles JS-only storefronts automatically. Every snapshot carries a parse_confidence so a degraded read is flagged, never silently mis-reported.
2. Compares against the previous run (state is persisted per URL in a named Key-Value store) and emits typed change events.
3. Builds a watchlist digest — cheapest in-stock item, a full price matrix, stock/sale counts, and this run's change summaries — one decision-ready record.

Outputs

Three record types land in the dataset (filter by record_type):

• snapshot — domain, title, price, currency, compare_at_price, in_stock, on_sale, lowest_price/highest_price, variants[] (each with price / compare-at / SKU / availability), parse_confidence, extraction_method.
• change_event — event_type, summary, previous_value, current_value, change_pct, direction, variant. Event types: price_changed, variant_price_changed, back_in_stock, out_of_stock, sale_started, sale_ended, title_changed, variant_added, variant_removed, page_changed_unparsed. (baseline_created is emitted on the first run of a URL and is informational, not billed as a change.)
• digest — one per run: cheapest_in_stock, price_matrix, in_stock_count, out_of_stock_count, on_sale_count, changes_by_type, change_summaries, notes.

Input

Field	What it does
productUrls	Product page URLs to monitor (Shopify, WooCommerce, BigCommerce, custom).
emitDigest	Emit the one-per-run watchlist digest (default on).
eventTypes	Restrict which change events are emitted/charged (empty = all).
kvStoreName	Named KV store that persists state between runs — keep it constant across scheduled runs.
forceBrowser	Always render in a browser (slower; normally auto-escalated only when needed).
proxyConfiguration	Apify Proxy; defaults to US residential so geo-localized prices stay stable run-to-run.

How to use it as a monitor

1. Add your competitors' product URLs and run once — this bootstraps a baseline (you'll see baseline_created records, no change events yet).
2. Schedule the actor (e.g. daily) with the same kvStoreName.
3. From the second run on, you get only the changes — wire the change_event records into your alerting (email, Slack, a webhook on new dataset items).

Reliability note

Keep the proxy country pinned (default US). A rotating exit country can serve a different currency for the same page, which would look like a price change and re-baseline your monitor. The actor already guards against a currency flip, but a pinned country keeps the data cleanest.

Pricing

Pay-per-event: one charge per product snapshot, one per change event emitted, one per watchlist digest. Platform compute runs on your own Apify account.

More scrapers from our toolkit

Building a data pipeline? These actors pair well with this one — each runs on your own Apify account with the same pay-per-result pricing, no subscription:

• Craigslist Scraper
• Doordash Scraper
• Ebay Scraper
• Etsy Listings
• Facebook Marketplace Scraper
• Grailed Listings

Chain any of them together from the Integrations tab (the Run succeeded trigger) to build a multi-step workflow — one actor's output feeds the next.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.