E-Commerce Price Tracker — Deal & Opportunity Intelligence

Stop monitoring prices. Start monitoring opportunities.

Most price monitors create work. They tell you a price changed, a product is back in stock, a competitor dropped 5%, then leave you to decide what matters. This actor does the opposite: it builds price history, retailer-behaviour models, opportunity signals and market intelligence over time, then surfaces only what deserves attention.

• Instead of 5,000 price alerts, you get a ranked action queue.
• Instead of price data, you get decisions.
• Instead of monitoring products, you monitor opportunities.

Works on one Shopify store or product page, or a 500-SKU multi-retailer portfolio.

A new category: opportunity intelligence

Price monitoring tells you what changed. Opportunity intelligence tells you what matters. This actor belongs to the second category. Every score, signal and model below exists for one purpose: to reduce thousands of price changes into a handful of actions.

Who this is for

• Buyers & deal hunters — find genuine deals, avoid fake sales, and know when to buy versus wait.
• E-commerce operators & repricers — monitor competitor pricing behaviour, spot aggressive discounting, and catch market shifts.
• Agencies & dropshippers — track hundreds of products and surface only the meaningful changes. Instead of 5,000 alerts, you get a ranked action queue.
• Analysts & researchers — generate retailer, category and market intelligence from one API call.

What it answers that other tools can't

Question	A typical price monitor	This actor
Did the price change?	✓	✓
Is this a real sale?	✗	✓
Is this retailer usually this aggressive?	✗	✓
Is this a rare (once-a-year) opportunity?	✗	✓
Should I buy now or wait?	✗	✓
What deserves attention today?	✗	✓
What's happening across the market?	✗	✓

What it monitors

✓ Products ✓ Retailers ✓ Competitors ✓ Categories ✓ Portfolios ✓ Markets

Why most price-monitoring tools fall short

Problem	A typical price monitor	This actor
Too many alerts	every change fires one	only meaningful opportunities surface (attention.required)
No historical context	current price only	persistent per-product price intelligence
Fake sales look real	trusts the compare-at price	detects inflate-then-discount (promotionIntegrity)
No competitor intelligence	shows price changes	models competitor behaviour (competitorBehaviour)
No prioritisation	flat product list	ranked actionQueue + attention severity
No market visibility	product-level only	product, retailer, category & market records

Before vs after

Without this actor, a 500-product run is ~67 price alerts + ~12 stock alerts + ~3 competitor changes, and you still have to decide what matters. With it:

Attention required: 4
  1. Historical low — Men's Tree Runners
  2. Rare opportunity (once-per-year) — Wool Runner
  3. Aggressive competitor discounting — group "Running Shoes"
  4. Back in stock — Trail Runner SWT
Everything else: ignore until it matters.

Filter the dataset on attention.required = true and that whole 500-product run collapses to the few records that matter:

{
  "attentionRequired": 4,
  "opportunities": [
    { "severity": "critical", "reason": "Historical low", "exceptionType": "historical-low" },
    { "severity": "high", "reason": "Aggressive competitor discounting" },
    { "severity": "medium", "reason": "Rarely-seen low price" },
    { "severity": "high", "reason": "Back in stock", "exceptionType": "back-in-stock" }
  ]
}

What you get

Three things, in order of importance:

1. Attention — what deserves your attention today (attention.required + severity). The antidote to alert fatigue: instead of 5,000 alerts, a ranked action queue.
2. Decision — buy now / wait / watch / overpriced (priceDecision + opportunityScore + buyWindow).
3. Intelligence — every score, signal and model that explains why (detailed below).

Core signals (advanced)

The hero fields are attention (what to look at) and opportunityScore / priceDecision (what to do). Everything else explains why, grouped by lens:

Opportunity intelligence — opportunityScore (0-100 timing axis), priceDecision (buy-now / good-deal / watch / wait / overpriced), buyWindow (open / closing / closed + days remaining), opportunityRarity (first-time / rare + once-per-year cadence + days since last occurrence), opportunityContext, and inactionRisk — the counterfactual: what does waiting cost you?

Pricing intelligence — dealScore + dealScoreBreakdown (always answer "why 88?"), priceIntelligence (low / high / avg / median, a historicalPercentile + human band so "cheaper than 92% of its history" reads at a glance, near-low, recovery-from-low, anomalies), momentum (7d/30d + accelerating direction), discountVelocity, pricingPattern (cyclical / volatile / steady), promotionIntegrity (fake-sale detection), and seasonality ("17% cheaper than usual for June", once history spans calendar months).

Risk & change — riskProfile is the consumable abstraction over the raw signals (priceReboundRisk / stockoutRisk / fakeSaleRisk bands); plus changeFlag, stockSignal, scarcitySignals (restocks + stockout frequency), and confidence with plain-English reasons[].

Routing — attention (required + severity + typed exceptionType + ignoreForDays), alertPriority, and recommendedAction + reasons[] + summary (paste-ready).

Plus a run-level summary record (and KV SUMMARY mirror) with:

• a portfolioState narrative (sale-heavy / stock-pressure / prices-rising / opportunity-rich / quiet / mixed) with the signals behind it, so you know what's happening across the whole watchlist in one read;
• a 0-100 watchlist health score, counts of drops, restocks, products on sale and at their lowest-ever price, the biggest drop / best deal / best opportunity;
• per-store retailer DNA (sale frequency, average + largest discount, discount aggressiveness, pricing style, restock behaviour, storewide-sale event) so you learn how each retailer behaves, not just today's price;
• per-category intelligence (products on sale, average discount, average deal score per product category);
• a priority-sorted action queue.

Supply productGroups and you also get one group record per competitor set: market average / lowest / highest price, market spread, every member ranked cheapest-first (priceRank + vsMarketAvgPct), best current deal, biggest recent drop, group leader, and a competitorBehaviour read (market-share-grab / aggressive-discounting / selective-discounting / holding-price / premium), turning historical monitoring into competitive pricing intelligence.

The dataset isn't just products. Every run also emits entity records so you can model the market, not just individual prices:

• recordType: "retailer" — one per monitored store, carrying its retailer DNA: sale frequency, average + largest discount, discount aggressiveness, pricing style, restock behaviour, a promotionReliability score (how genuine its discounts are), and any current storewide-sale event. A 25% discount from a rare-discounter means something very different from a retailer that discounts every week, and this record tells you which you're dealing with.
• recordType: "marketPulse" — one per run, the state of discounting across everything you monitor: discount-heavy / normal / quiet, discount participation %, average discount, and how many retailers are discounting aggressively. Executive-level "what changed in the market this week?" in one record.

Questions this actor answers

• "Did any of the products I track drop in price since yesterday?"
• "Is this the cheapest this product has ever been?"
• "Which competitor SKUs just went on sale, and by how much?"
• "Did that out-of-stock item come back?"
• "Of the 400 products I monitor, which 6 should I actually look at today?"
• "Is this price a real deal, or just a small dip off an already-high price?"
• "Is this '20% off' a genuine discount or a fake inflate-then-discount sale?"
• "Is the buying window still open, or is the price already climbing back up?"
• "Has this price ever been this low before, or is this a once-a-year low?"
• "What's the overall state of my portfolio this week, and which competitor is discounting most aggressively?"

Why this is different

Most price scrapers stop at extraction: they give you currentPrice and leave the interpretation to you. You still have to store yesterday's prices somewhere, diff them, decide what a meaningful drop is, and figure out which moves matter. This actor does that interpretation for you, deterministically (no LLM, fully reproducible):

A raw price scraper gives you	This actor gives you
currentPrice: 98	priceDecision: "buy-now", dealScore: 88, isAtLowestEver: true
You diff runs yourself	changeFlag: "PRICE_DROP", changePercent: -11
You track history in your own DB	Built-in cross-run price history per product
A flat list of every product	A priority-sorted actionQueue of just what changed
"in stock: true"	stockSignal: "back-in-stock"

Monitoring modes

Pick the mode that matches your job. It tunes which events become high-priority alerts and what lands in the action queue:

• deal-hunting (buyer) — high-priority on price drops, buy-now signals, and restocks.
• repricing (seller) — high-priority on competitor drops AND rises, so you can react either way.
• stock-alert — high-priority only on back-in-stock / went-out-of-stock changes.
• market-intelligence — prioritises unusual moves (price anomalies, near-historical-lows, deep discounts, storewide sales) over routine single-product drops. Built for monitoring a market, not a wishlist.
• general (default) — balanced across drops, restocks, and good deals.

How it works

productUrls[]  ──►  for each store / product
                      │
                      ├─ Shopify store?  →  /products.json API (bulk, all variants + compare-at + stock)
                      │                       else ▼
                      └─ Generic page    →  JSON-LD → OpenGraph → product-meta → regex (first hit wins)
                      │
                      ▼
                load this product's price history  (named KV store, persists across runs)
                      │
                      ▼
                DECISION LAYER (deterministic)
                  price intelligence (low/high/avg/median, position, trend, volatility, anomaly)
                  discount vs compare-at / RRP
                  changeFlag + stockSignal vs last run
                  dealScore 0-100  →  priceDecision  →  recommendedAction + alertPriority
                      │
                      ▼
                stream the product row  +  save updated history  +  (PPE) charge once
                      │
                      ▼
                run summary record + KV SUMMARY  (drops, restocks, action queue, best deal)

The price-history store is keyed per product and persists between runs so the very first capture builds the baseline and every later run scores against it. Use the optional watchlistName input to keep independent histories for separate schedules.

Input

Parameter	Type	Description
productUrls	string[]	Shopify store root URLs (e.g. https://store.myshopify.com) and/or direct product page URLs. Up to 1,000 per run.
monitorMode	select	general / deal-hunting / repricing / stock-alert. Tunes alerts + the action queue. Default general.
priceDropAlertPct	integer	A move of at least this % vs the previous run flags PRICE_DROP / PRICE_RISE and raises priority. Default 5.
watchlistName	string	Optional. Names an independent price-history watchlist so two schedules don't share history.
outputProfile	select	minimal (decision fields only, for Zapier/Make rules) / standard (default) / full (incl. description, variants, tags).
productGroups	array	Optional. Competitor groups [{ "name": "...", "urls": [...] }] for per-group market intelligence (market average, best deal, group leader). Products are also monitored individually.
includePriceHistory	boolean	Attach each product's full tracked price-point timeline to its record. Default false.
trackPriceHistory	boolean	Persist price history across runs for change detection. Default true.
checkShopifyApi	boolean	Try the Shopify /products.json endpoint first. Default true.
notifyOnChange	boolean	Log high-priority alerts prominently in the run log. Default false.
currency	string	Fallback currency code when the site doesn't specify one. Default USD.
maxProducts	integer	Cap on products per Shopify store URL. Default 100.

Example: hunt for deals across a few stores

{
    "productUrls": [
        "https://www.allbirds.com",
        "https://www.gymshark.com/products/gymshark-arrival-5-shorts-black-ss22"
    ],
    "monitorMode": "deal-hunting",
    "priceDropAlertPct": 10,
    "watchlistName": "my-wishlist"
}

Example: competitor repricing watch (seller)

{
    "productUrls": ["https://competitor.myshopify.com"],
    "monitorMode": "repricing",
    "maxProducts": 500,
    "outputProfile": "minimal"
}

Example: competitor group intelligence

{
    "productGroups": [
        {
            "name": "Running Shoes",
            "urls": [
                "https://store-a.com/products/runner",
                "https://store-b.com/products/runner",
                "https://store-c.com/products/runner"
            ]
        }
    ],
    "monitorMode": "repricing"
}

Returns each product individually (tagged groupName: "Running Shoes") plus a recordType: "group" record with the market average price, the best current deal, the biggest recent drop, and the group leader by opportunity.

Output example

{
    "recordType": "product",
    "url": "https://allbirds.com/products/mens-tree-runners",
    "source": "shopify",
    "storeDomain": "allbirds.com",
    "productName": "Men's Tree Runners",
    "currentPrice": 78,
    "compareAtPrice": 110,
    "currency": "USD",
    "available": true,
    "priceDecision": "buy-now",
    "dealScore": 88,
    "dealScoreBreakdown": { "historicalPosition": 50, "compareAtDiscount": 17, "recentDrop": 8 },
    "opportunityScore": 94,
    "changeFlag": "PRICE_DROP",
    "stockSignal": "unchanged",
    "alertPriority": "high",
    "recommendedAction": "Strong buy signal: the current price is near or at its tracked low.",
    "reasons": ["At the lowest price tracked", "Price dropped 11%", "29% off the listed compare-at price", "Down 18% over 30 days and accelerating"],
    "summary": "Men's Tree Runners: buy now at USD 78.00. At the lowest price tracked.",
    "confidence": { "score": 90, "level": "high", "reasons": ["18 price observations", "24 days tracked", "stable pricing pattern"] },
    "scarcitySignals": { "restocksLast90Days": 2, "stockOutFrequencyPct": 12.5, "stockOutFrequency": "low" },
    "priceIntelligence": {
        "lowestPrice": 78, "highestPrice": 110, "averagePrice": 96, "medianPrice": 98, "priceStdDev": 11.4,
        "historicalPercentile": 6, "pricePositionLabel": "bottom-decile-price",
        "pricePositionPct": 0, "isAtLowestEver": true, "isNearHistoricalLow": true, "discountFromPeakPct": 29,
        "recoveryFromLowPct": null, "daysSinceLowestPrice": 0, "daysTracked": 24, "dataPoints": 18,
        "trend": "falling", "volatility": "medium", "anomaly": "price-drop"
    },
    "momentum": { "sevenDayPct": -6.0, "thirtyDayPct": -18.0, "direction": "accelerating-down" },
    "discountVelocity": { "dropsLast30Days": 3, "daysSinceLastDrop": 1, "avgDropPct": 7.2 },
    "pricingPattern": { "type": "cyclical", "averageCycleDays": 14, "confidence": "medium" },
    "futureDealProbability": { "betterPriceWithin30Days": 0, "basis": "historical-distribution" },
    "promotionIntegrity": { "score": 96, "classification": "genuine-discount", "reason": "Sale price is genuinely low (cheaper than 94% of its tracked history)." },
    "buyWindow": { "status": "open", "daysExpectedRemaining": 4, "confidence": "medium", "reason": "Cyclical low — typically rebounds within ~4 days." },
    "opportunityRarity": { "historicalOccurrence": 1, "frequency": "rare", "classification": "once-per-year", "daysSinceLastOccurrence": 348, "reason": "This price (or lower) has occurred 1 of 18 tracked observations, last 348 days ago." },
    "riskProfile": { "priceReboundRisk": "high", "stockoutRisk": "low", "fakeSaleRisk": "low" },
    "inactionRisk": { "score": 80, "reason": "Waiting risks missing this: rarely this low, window currently open." },
    "seasonality": { "currentVsExpectedPct": -17, "seasonalOpportunity": true, "bestMonthToBuy": "November", "basis": "monthly-history", "monthsObserved": 8 },
    "attention": { "required": true, "reason": "Rarely-seen low price.", "ignoreForDays": null, "severity": "medium", "exceptionType": "none" },
    "discount": { "fromCompareAtPct": 29.09, "onSale": true, "savingsAmount": 32 },
    "priceChange": { "previousPrice": 88, "changeAmount": -10, "changePercent": -11.36, "direction": "down", "lastChecked": "2026-06-07T08:30:00.000Z" }
}

The dataset's first view (Decisions) leads with priceDecision, dealScore, changeFlag, stockSignal, and alertPriority so the answer is visible in the first columns.

Use in Dify

Drop this actor into Dify workflows via the Apify plugin's Run Actor node. Each product returns scored, classified, and recommended as structured JSON: buy-now / good-deal / watch / wait / overpriced plus the alertPriority your downstream node branches on. A raw scraper pointed at the same store returns prices; this returns decisions.

• Actor ID: ryanclinton/ecommerce-price-monitor
• Sample input (deal-hunting across a wishlist):

{
    "productUrls": ["https://store.myshopify.com"],
    "monitorMode": "deal-hunting",
    "watchlistName": "dify-wishlist",
    "outputProfile": "minimal"
}

A Dify if/else node routes cleanly on the stable enums:

IF priceDecision == "buy-now"          → send a buy alert
ELIF changeFlag == "BACK_IN_STOCK"     → notify the requester
ELIF alertPriority == "high"           → push to the review queue
ELSE                                    → ignore

The reasons[] and summary strings are usable verbatim in the alert body, with no LLM rewriting needed. Opt-in modes (repricing, stock-alert) and the watchlistName watchlist let scheduled Dify workflows monitor the same catalogue over time.

How much does it cost

This actor is pay-per-event: you are charged $0.05 per product monitored (only for products where a real price resolved; failed or empty extractions are never charged). Platform compute is billed separately by Apify and is minimal because the actor uses API calls and lightweight HTML parsing, not browser rendering.

Scenario	Products monitored	PPE charge
One product page	1	$0.05
Small Shopify store	50	$2.50
A 200-SKU competitor watch	200	$10.00

Set maxProducts to cap how many products a large store contributes, and outputProfile: "minimal" to keep payloads small for automation.

What matters, not what changed

Most price-monitoring tools answer "what changed?". This actor answers "what matters?". In practice that is the difference between:

• 5,000 alerts, 500 products, 50 competitors

and:

• 4 opportunities, 2 risks, 1 decision

Every score, signal, model and intelligence layer in this actor exists for that one purpose: to reduce thousands of changes into a handful of actions.

What this actor does NOT do

• It does not render JavaScript. Single-page-app stores that inject prices with JS are detected and reported as failureType: "js-required", not silently dropped. Use a browser-based scraper for those.
• It does not beat aggressive anti-bot marketplaces. Amazon, eBay, and Walmart use heavy bot defences; pages that block automated access are reported as failureType: "blocked" with a proxy hint. Use a dedicated marketplace scraper instead.
• It does not compare prices across different stores. dealScore is relative to each product's own tracked history and its store's compare-at price, not a cross-retailer "cheapest on the internet" claim (that would need a market-wide price corpus this actor doesn't hold).
• It does not convert currencies. It reports the currency each site exposes and falls back to your currency input when absent.
• It does not access products behind a login or password-protected storefront.

Related actors

Actor	Use case
ryanclinton/shopify-store-intelligence	Full catalogue, tech-stack, theme, and bestseller analysis for a Shopify store
ryanclinton/website-change-monitor	Detect any change on a web page (layout, content, links), beyond price
ryanclinton/website-tech-stack-detector	Identify the e-commerce platform powering any store

FAQ

How does price-history tracking work? With trackPriceHistory enabled (the default), the actor stores each product's recent prices and stock status in a named Key-Value store that persists between runs. On the next run it scores the current price against that history (lowest, highest, average, position-in-range) and flags drops, rises, and stock changes. The first run builds the baseline, so priceDecision reads new until the second run.

What's the difference between dealScore and discount.fromCompareAtPct? discount.fromCompareAtPct is the discount the store itself advertises (current vs compare-at / RRP). dealScore is the actor's own 0-100 verdict that also factors in where the price sits in its tracked history and any fresh drop, so a product can be "on sale" off an inflated RRP yet still score low if it's been cheaper before.

Can I monitor products from multiple stores in one run? Yes. Mix Shopify store URLs and individual product page URLs (up to 1,000). Each is processed independently and duplicates are skipped automatically.

Does it work with Amazon, eBay, or Walmart? No. Those marketplaces use aggressive anti-bot measures; blocked pages are reported with failureType: "blocked". Use a dedicated marketplace scraper for them.

How often should I schedule runs? Daily works for most use cases and builds rich history. For flash sales, run every few hours. Use a distinct watchlistName per schedule so histories stay separate.

Is scraping product prices legal? Product prices are publicly visible to any site visitor. This actor accesses only public data and does not bypass authentication or access controls. See Apify's guide on web scraping legality for general guidance.

Deliver alerts to Slack and Notion (MCP connectors)

Send each run's price alerts straight into your own Slack or Notion via Apify MCP connectors. Set notionConnector or slackConnector and the run delivers the drop/rise/restock counts, the biggest drop and best deal, and the action queue. Your credentials stay encrypted on your Apify account; the actor never sees your Slack or Notion token.

• Slack — posts a price-alert digest plus the action queue to a channel. One-click OAuth in Apify Console → Settings → MCP connectors.
• Notion — archives each run. notionArchiveProfile: per-product writes one page per flagged product; the default summary writes one page per run.
• deliverTopN bounds how many products are sent (default 10); the full set always stays in the dataset.

Schedule the monitor with a watchlistName and every run posts only what changed to your channel or Notion. Delivery is additive — unset connectors and the actor behaves exactly as before; each run records a deliveries block on its SUMMARY.

Integrations

• Zapier and Make — branch on priceDecision / alertPriority to fire alerts and actions
• Slack — post the summary string on high-priority changes
• Google Sheets — export the decision dataset for analysis
• Apify API and Webhooks — programmatic access and run-finished triggers