We extract local store catalogues, price-per-kg metrics, Nutri-Score data, and promotional campaigns from Intermarche. Delivered as clean JSON or Parquet.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Core objects from intermarche.com. All fields typed and schema-versioned.
"ean": "3176840001004", "title": "Beurre doux gastronomique", "brand": "President", "price": 2.45, "currency": "EUR", "price_per_unit": 9.8, "unit_measure": "kg", "store_id": "03842", "in_stock": true
| # | ean | title | brand | price | currency | price_per_unit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Nutritional Data objects from intermarche.com. All fields typed and schema-versioned.
"ean": "3176840001004", "nutri_score": "E", "eco_score": "C", "calories_100g": 744, "fat_100g": 82.0, "sugar_100g": 0.5, "allergens": "['Lait']", "ingredients_text": "Crème pasteurisée (origine France), ferments lactiques."
| # | ean | nutri_score | eco_score | nova_group | calories_100g | fat_100g |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions objects from intermarche.com. All fields typed and schema-versioned.
"ean": "3176840001004", "promo_type": "remise_immediate", "original_price": 2.95, "discount_price": 2.45, "discount_pct": 17, "carte_avantage_benefit": 0.0, "start_date": "2026-10-01", "end_date": "2026-10-15"
| # | ean | promo_type | original_price | discount_price | discount_pct | carte_avantage_benefit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from intermarche.com. All fields typed and schema-versioned.
"store_id": "03842", "name": "Intermarche Super Paris", "store_type": "Super", "address": "12 Rue de la Pompe", "zip_code": "75016", "city": "Paris", "latitude": 48.8612, "longitude": 2.2785, "drive_available": true
| # | store_id | name | store_type | address | zip_code | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Hierarchy objects from intermarche.com. All fields typed and schema-versioned.
"ean": "3176840001004", "department": "Frais", "category": "Beurre, Crème", "sub_category": "Beurre doux", "aisle": "Crémerie", "breadcrumb": "['Accueil', 'Frais', 'Beurre, Crème', 'Beurre doux']", "position": 3, "scraped_at": "2026-05-12T09:14:33Z"
| # | ean | department | category | sub_category | aisle | breadcrumb |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Intermarche scraper navigates regional store variations, dynamic grocery pricing, and complex nutritional metadata with full JavaScript execution and localised session management.
Prices vary by zip code. We configure geolocated sessions to extract accurate local pricing for any specified Intermarche Drive or Super location.
Extract normalised price-per-kg and price-per-liter data to enable accurate cross-brand and cross-retailer comparison.
Capture official Nutri-Score ratings, Eco-Score grades, and NOVA classifications directly from the product detail pages.
Track standard discounts alongside 'Avantage Carte' specific benefits, multibuy offers, and seasonal promotional badges.
Extract full ingredient lists, allergen warnings, and additive information for FMCG compliance and nutritional analysis.
Scrape the full directory of Intermarche locations, including opening hours, Drive availability, and precise coordinates.
Monitor local store inventory signals to determine if a product is in stock, low stock, or unavailable for Click & Collect.
Map products to their exact department, category, and aisle using breadcrumb extraction for assortment analysis.
Run extractions daily or weekly to track inflation, promotional cycles, and assortment changes over time.
Brief in. Clean data out.
Provide target EANs, category URLs, or specific store IDs. We map the required data points and extraction frequency.
We configure Playwright spiders, French residential proxies, and session logic to bypass Datadome protections.
We run sample extractions to verify price accuracy, EAN matching, and null-rate thresholds before production.
Structured JSON, CSV, or Parquet delivered directly to your S3 bucket, Snowflake stage, or via webhook.
Grocery scraping requires precise geolocation handling and aggressive anti-bot circumvention. Here is how we maintain stable extraction.
Intermarche requires setting a specific store context to view prices and availability. We programmatically inject store IDs and zip codes into the session state before requesting the catalogue, ensuring you get accurate local data rather than generic national placeholders.
Intermarche uses Datadome to block automated traffic. We route all requests through high-reputation French residential proxies, managing TLS fingerprints and solving JavaScript challenges transparently to maintain access.
Much of the product metadata and pricing is hydrated client-side via React. We intercept the underlying XHR requests and extract clean JSON payloads directly from the frontend state, bypassing brittle DOM parsing where possible.
Grocery data is notoriously messy. We clean and normalise EANs, standardise weight measurements (converting grams to kilograms), and format promotional logic into queryable numeric fields.
For large store networks, pulling the entire catalogue daily is inefficient. We use hash-based change detection to emit only records where price, stock, or promo status has changed since the last run.
Food and beverage brands track retail execution, ensuring products are listed, in stock, and priced correctly across the store network.
Competing supermarket chains ingest local pricing data to adjust their own regional pricing strategies and match promotions.
Economic analysts and hedge funds monitor basket prices over time to calculate real-time consumer price index (CPI) metrics.
Trade marketing teams analyse discount depth, multibuy frequency, and loyalty card offers to optimise their own campaign spend.
Health tech applications scrape Nutri-Score, Eco-Score, and ingredient lists to power consumer-facing diet and wellness apps.
Category managers analyse shelf share and brand visibility within specific Intermarche departments to identify distribution gaps.
"Grocery retail is hyper-localised. A price in Paris is not the price in Lyon. If you cannot extract data at the store level, your market analysis is fundamentally flawed."
Most teams fail at grocery scraping because they ignore session geolocation. Intermarche requires setting specific store cookies and bypassing strict Datadome protections before rendering the catalogue. DataFlirt manages these localised sessions, residential proxies, and JavaScript hydration so you receive normalised, store-accurate pricing without the infrastructure overhead.
Everything supported by our intermarche.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles React rendering, store cookie injection, and interaction flows for the Intermarche frontend.
We maintain pools of high-reputation French residential IPs. Rotation happens per request to bypass Datadome rate limits and geo-restrictions.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling across thousands of store IDs, ensuring data is delivered on your exact cadence.
Data delivered to where your team already works — no new tooling required.
About intermarche.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing, product, and store data is generally permissible. DataFlirt targets only public, non-authenticated catalogue data. We do not extract personal consumer data or circumvent authentication walls for private accounts. Clients should consult legal counsel for specific commercial use cases.
Intermarche employs Datadome to block scrapers. We use French residential proxies, full Playwright browser sessions with realistic TLS fingerprints, and automated solvers to manage challenges without interrupting the data flow.
Yes. Grocery pricing varies by location. You provide the store IDs or zip codes, and we inject the correct session cookies to extract the exact pricing, promotions, and Drive availability for those specific locations.
Yes. We capture all nutritional metadata including Nutri-Score, Eco-Score, NOVA groups, full ingredient lists, allergens, and macronutrient tables per 100g.
We support daily or weekly cadences for full catalogue refreshes across selected stores. For targeted lists of high-priority EANs, we can configure intraday runs to monitor stock availability.
We extract the base price, the discounted price, the discount percentage, and any specific loyalty conditions (e.g., Avantage Carte). Start and end dates are included when surfaced by the platform.
Our minimum engagement typically starts with a defined list of categories or EANs across a set number of store locations, delivered weekly. Contact us to scope your specific volume requirements.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a national price index or local store assortment mapping, we scope, build, and operate the pipeline. Tell us what you need.
