# BasketWatch Irish Grocery Data - Aldi|Tesco|SuperValu|Dunnes (`hawkish_gym/basketwatch-irish-grocery-data`) Actor Weekly product, price, unit price and promotion data for the four major Irish supermarkets. Thin client — fetches from BasketWatch's existing API (so prices are the same weekly snapshot as the BasketWatch dashboard, no scraping happens at run-time). - **URL**: https://apify.com/hawkish\_gym/basketwatch-irish-grocery-data.md - **Developed by:** [BasketWatch](https://apify.com/hawkish_gym) (community) - **Categories:** E-commerce - **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## BasketWatch — Irish Grocery Data **Every SKU. Every shelf price. Every promotion. Across all four major Irish supermarkets — Aldi, Tesco, SuperValu and Dunnes Stores — refreshed weekly.** The only data feed on Apify Store that covers the full Irish grocery market in one place. **47,514 live SKUs** as of this week — **16,468 of them currently on promotion** — structured into clean JSON rows, with promotional pricing, loyalty-card prices, multibuys, was-prices and offer-validity windows already parsed and ready for analysis. > #### 🗓️ Refresh schedule > > **All four retailers are scraped every Friday morning at 02:00 GMT.** > Every actor run from that point in the week reads from the latest weekly > snapshot — the `scrape_date` field on each row tells you exactly when > the price was captured. Need a faster cadence (daily, twice-weekly, > on-demand)? See the contact details at the bottom of this page. ### Why this exists The Irish grocery market is dominated by four retailers running on three different e-commerce platforms — Aldi's bespoke storefront, Tesco Ireland's own stack, and the Mercatus platform shared by SuperValu and Dunnes Stores. Each anti-bots aggressively and emits prices in a different shape. Building this in-house means: - 4 separate scrapers, each with its own regex + class-name extraction rules - Anti-bot bypass infrastructure that survives Cloudflare and JavaScript challenges - Weekly maintenance as each retailer's markup drifts - Data-quality fixes for the inevitable per-unit-vs-pack-price misreads, cents-suffix parse errors, promo-badge collisions, etc. - Cross-retailer SKU matching by brand + canonical pack size We've done all of that and packaged it into one Apify actor. You call the endpoint, you get clean rows. ### What you get Pick a **retailer** and a **dataset** — get back structured rows you can load directly into pandas, BigQuery, Excel, your warehouse or pipeline: | Dataset | What's in each row | |---|---| | **products** | SKU id, name, brand, pack size + canonical units, current shelf price, **unit price (€/kg, €/L)**, full category breadcrumb, product URL, first-seen + last-seen dates | | **promotions** | Only products currently on offer — **offer label** (e.g. *"3 for €5"*, *"SAVE €0.75"*, *"Only €2.50"*), **was-price**, **validity window** (*"13th May - 15th Jun"*), **Tesco Clubcard prices**, **SuperValu Real Rewards** and Dunnes member-only pricing where promoted | | **new-products** | Products newly listed within a configurable window — range additions / launches | | **removed** | Products that have dropped off the catalogue — delistings / range cuts | | **changes** | Week-over-week price movements with delta and % change (compares the latest weekly snapshot to the previous one) | #### Available data per retailer Any combination below is valid input to the actor — set `store` and `dataset` to one of these and you'll get the matching rows: | `dataset` ↓    `store` → | **Aldi** | **Tesco** | **SuperValu** | **Dunnes** | |---|:---:|:---:|:---:|:---:| | `products` | ✓ 2,948 | ✓ 19,057 | ✓ 11,069 | ✓ 14,440 | | `promotions` | — *(Aldi doesn't publish multibuy promos)* | ✓ 7,089 on offer | ✓ 3,219 on offer | ✓ 6,160 on offer | | `new-products` | ✓ | ✓ | ✓ | ✓ | | `removed` | ✓ | ✓ | ✓ | ✓ | | `changes` | ✓ | ✓ | ✓ | ✓ | Counts are the **live current** SKU figures (refreshed weekly). `new-products` / `removed` row counts depend on the `days_back` window you pass; `changes` returns however many SKUs moved week-over-week. Example input: ```json { "store": "tesco", "dataset": "promotions", "max_products": 1000 } ```` Returns up to 1,000 Tesco products currently on offer — with Clubcard prices, was-prices, multibuy labels and offer-validity windows already parsed. **Search by product name** (e.g. `heinz`, `cadbury`, `kerrygold`) is supported on the `products` dataset via the `search` input. ### Sample output Input: ```json { "store": "tesco", "dataset": "promotions", "max_products": 50 } ``` Output (one row per product on offer): ```json { "id": "tesco:307552934", "store": "tesco", "name": "Heinz Baked Beans In Tomato Sauce 415G", "brand": "Heinz", "category_path": "Food Cupboard > Tinned Food > Tinned Beans, Pasta & Pulses", "pack_size": "415G", "price": 1.49, "unit_price": 3.59, "unit_basis": "1 KG", "offer_text": "Any 3 for £3 Clubcard Price", "was_price": 1.85, "clubcard_price": 1.00, "offer_valid": "13th May - 9th Jun", "url": "https://www.tesco.ie/groceries/...", "scrape_date": "2026-05-21" } ``` ### Use cases | Buyer | What they do with it | |---|---| | **CPG brand teams** | Track competitor pricing, promo depth and assortment share across all four retailers from a single feed | | **Retail suppliers** | Monitor own-brand and private-label pressure against your products, weekly | | **Market research & consultancies** | Continuous Irish grocery price index, cost-of-living tracking, basket analysis | | **Journalism & data investigations** | Cited, timestamped, reproducible source data for stories on food inflation, shrinkflation, promotional intensity | | **Public bodies & regulators** | Independent grocery price evidence for consumer protection, competition oversight and official statistics | | **Academic & policy research** | Household-budget research, food-policy studies, longitudinal price tracking | ### How it works This actor is a **thin client over a hardened weekly scrape pipeline**. The four retailers are scraped on a fixed weekly cadence — **every Friday at 02:00 GMT** — by infrastructure that handles: - **Mercatus storefront parsing** (SuperValu + Dunnes) with corrected `ProductPrice` selectors — no per-unit-price-as-headline misreads - **Anti-bot bypass** for Cloudflare-fronted endpoints - **Multipack canonicalisation** — `"4 x 330ml"`, `"12 Pack (330 ml)"` and `"2L"` all normalise to comparable units - **Promo capture** — offer label + was-price + validity window from `PromotionLabelBadge` / `ProductWasPrice` / `OfferValidMessage` elements - **Loyalty-tier pricing** — Tesco Clubcard prices, SuperValu Real Rewards pricing, Dunnes member offers - **Data-quality checks** — week-over-week catalogue-drop detection, per-product outlier sweeps, regression-tested parsers You don't pay for any of that scraping cost on a per-call basis — the pipeline runs once a week regardless. Each actor run reads from the already-fetched snapshot, so: - **Fast**: <2 seconds per page of results (vs 5-15 minutes for a live scrape) - **Cheap to fulfil**: marginal cost per row is fractions of a cent - **Consistent**: the same parser fixes and regression-tested extraction rules across every call The `scrape_date` field on every row tells you exactly when each price was captured. *Weekly (Friday 02:00 GMT) is the default cadence. **Daily or twice-weekly snapshots** can be stood up for buyers with a continuous-monitoring use case (e.g. live inflation tracking, real-time competitive-pricing dashboards) — the underlying scrape infrastructure already supports it; gating is on committed demand rather than engineering. Reach out to **basketwatchireland@gmail.com** if that's you.* ### Pricing Pay-per-result — only pay for rows you actually receive. Your Apify subscription tier determines the per-row rate: | Your Apify plan | Per result | Per 1,000 rows | Full Aldi (2,948) | Full Tesco (19,057) | |---|--:|--:|--:|--:| | **Free** (no subscription) | $0.05 | $50.00 | ~$147 | ~$953 | | **Starter** (Bronze) | $0.01 | $10.00 | ~$29 | ~$191 | | **Scale** (Silver) | $0.007 | $7.00 | ~$21 | ~$133 | | **Business** (Gold) | $0.005 | $5.00 | ~$15 | ~$95 | Plus a flat **$0.05 per run** (Actor start) — waived for the first 5 seconds of runtime. Platform compute is included; no separate platform charges on top. Maximum rows per run is configurable up to **100,000** — enough for a full multi-retailer pull in a single run if you need it. Need unlimited API access, bulk historical exports or a direct integration that bypasses Apify? Email **basketwatchireland@gmail.com**. ### Coverage (live figures, refreshed weekly) | Retailer | SKUs | On promotion now | Promotional data | Loyalty pricing | |---|--:|--:|---|---| | **Aldi** | **2,948** | — | Limited (Aldi doesn't publish multibuy promos) | — | | **Tesco** | **19,057** | **7,089** | Full incl. multibuys & "Tesco Finest" | **Tesco Clubcard prices** as a dedicated field | | **SuperValu** | **11,069** | **3,219** | Full incl. "3 for €X" multibuys, was-prices, validity windows | SuperValu **Real Rewards** member pricing captured in offer label | | **Dunnes Stores** | **14,440** | **6,160** | Full incl. multibuys, was-prices, validity windows | Dunnes member offers captured in offer label | | **Total** | **47,514** | **16,468** | | | Every actor run reflects the most recent weekly snapshot. The `scrape_date` field on each row tells you when that price was captured. ### Limits - Hard cap of **100,000 rows per run**. Large pulls scale linearly in cost. - Search filtering currently applies to the **products** dataset only. ### Support & contact 📧 **basketwatchireland@gmail.com** Email directly for any of the following — typical response within 24 hours: - **Scraper issues or data-quality questions** — wrong price, missing product, weird pack-size value, suspicious week-over-week jump - **Commercial enquiries** — custom scraping solutions, additional Irish retailers, scraping for other markets (UK, NI, EU), bespoke integrations - **Higher-cadence pulls** — daily / twice-weekly / on-demand snapshots - **Bulk historical exports** — multi-year price history, full back-catalogue - **Direct API access** — integrate against the BasketWatch API directly (skipping Apify) for unlimited pulls and lower marginal cost You can also use Apify's **Contact maintainer** button on this listing — it routes to the same inbox. *** *BasketWatch is not affiliated with Aldi Ireland, Tesco Ireland, Musgrave SuperValu or Dunnes Stores. Data is collected from publicly available sources for lawful market research and price comparison purposes.* # Actor input Schema ## `store` (type: `string`): Which Irish supermarket to pull data from. ## `dataset` (type: `string`): What kind of data to pull. - **products** — full product catalogue: SKU, name, brand, pack size, current price, unit price, category. - **promotions** — only products currently on offer (multibuy, clubcard, savings, was-prices). SuperValu / Tesco / Dunnes only. - **new-products** — products newly listed within the lookback window. - **removed** — products that have dropped off the catalogue within the lookback window. - **changes** — price moves between the latest weekly snapshot and the previous one (delta + % change). ## `max_products` (type: `integer`): Hard cap on dataset size. 500 is plenty to evaluate the data; a single retailer's full catalogue runs 3,000-20,000 rows. Set higher (up to 100,000) for full multi-store pulls. Pay-per-result pricing means cost scales linearly with this number — check the cost estimate before starting. ## `search` (type: `string`): Case-insensitive substring match on the product name. Example: 'heinz' returns only Heinz products. Only applied when dataset = 'products'. ## `days_back` (type: `integer`): Only used for 'new-products' and 'removed'. How many days back to look from today. Since scrapes run weekly, a value of 7 captures everything since the last snapshot; 14 captures the prior two weeks, etc. ## Actor input object example ```json { "store": "aldi", "dataset": "products", "max_products": 500, "days_back": 7 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("hawkish_gym/basketwatch-irish-grocery-data").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("hawkish_gym/basketwatch-irish-grocery-data").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call hawkish_gym/basketwatch-irish-grocery-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=hawkish_gym/basketwatch-irish-grocery-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "BasketWatch Irish Grocery Data - Aldi|Tesco|SuperValu|Dunnes", "description": "Weekly product, price, unit price and promotion data for the four major Irish supermarkets. Thin client — fetches from BasketWatch's existing API (so prices are the same weekly snapshot as the BasketWatch dashboard, no scraping happens at run-time).", "version": "0.4", "x-build-id": "XRQqgKHlO18ev1XnN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/hawkish_gym~basketwatch-irish-grocery-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-hawkish_gym-basketwatch-irish-grocery-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/hawkish_gym~basketwatch-irish-grocery-data/runs": { "post": { "operationId": "runs-sync-hawkish_gym-basketwatch-irish-grocery-data", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/hawkish_gym~basketwatch-irish-grocery-data/run-sync": { "post": { "operationId": "run-sync-hawkish_gym-basketwatch-irish-grocery-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "store", "dataset" ], "properties": { "store": { "title": "Retailer", "enum": [ "aldi", "tesco", "supervalu", "dunnes" ], "type": "string", "description": "Which Irish supermarket to pull data from.", "default": "aldi" }, "dataset": { "title": "Dataset", "enum": [ "products", "promotions", "new-products", "removed", "changes" ], "type": "string", "description": "What kind of data to pull.\n\n- **products** — full product catalogue: SKU, name, brand, pack size, current price, unit price, category.\n- **promotions** — only products currently on offer (multibuy, clubcard, savings, was-prices). SuperValu / Tesco / Dunnes only.\n- **new-products** — products newly listed within the lookback window.\n- **removed** — products that have dropped off the catalogue within the lookback window.\n- **changes** — price moves between the latest weekly snapshot and the previous one (delta + % change).", "default": "products" }, "max_products": { "title": "Maximum rows to return", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Hard cap on dataset size. 500 is plenty to evaluate the data; a single retailer's full catalogue runs 3,000-20,000 rows. Set higher (up to 100,000) for full multi-store pulls. Pay-per-result pricing means cost scales linearly with this number — check the cost estimate before starting.", "default": 500 }, "search": { "title": "Filter by product name (optional)", "type": "string", "description": "Case-insensitive substring match on the product name. Example: 'heinz' returns only Heinz products. Only applied when dataset = 'products'." }, "days_back": { "title": "Lookback window (days)", "minimum": 1, "maximum": 60, "type": "integer", "description": "Only used for 'new-products' and 'removed'. How many days back to look from today. Since scrapes run weekly, a value of 7 captures everything since the last snapshot; 14 captures the prior two weeks, etc.", "default": 7 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```