We extract store directories, regional pricing, nutritional information, and seasonal menus from IHOP. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Store Locations objects from ihop.com. All fields typed and schema-versioned.
"store_id": "3142", "address_line_1": "1001 E 17th St", "city": "Santa Ana", "state": "CA", "zip_code": "92701", "latitude": 33.7589, "longitude": -117.854
| # | store_id | address_line_1 | city | state | zip_code | latitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Menu Items objects from ihop.com. All fields typed and schema-versioned.
"item_id": "M-8492", "category": "Pancakes", "item_name": "Rooty Tooty Fresh 'N Fruity Pancakes", "base_calories": 580, "contains_allergens": "['Wheat', 'Milk', 'Eggs']", "customisation_options": "['Fruit Topping', 'Syrup Type']"
| # | item_id | category | sub_category | item_name | description | base_calories |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Nutritional Data objects from ihop.com. All fields typed and schema-versioned.
"item_id": "M-8492", "item_name": "Rooty Tooty Fresh 'N Fruity Pancakes", "calories": 580, "total_fat_g": 16, "sodium_mg": 1620, "total_carbs_g": 94, "protein_g": 15
| # | item_id | item_name | serving_size_g | calories | total_fat_g | sodium_mg |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Regional Pricing objects from ihop.com. All fields typed and schema-versioned.
"store_id": "3142", "item_id": "M-8492", "dine_in_price": 12.99, "delivery_price": 14.99, "currency": "USD", "is_lto": false, "price_timestamp": "2023-10-24T08:15:00Z"
| # | store_id | item_id | dine_in_price | delivery_price | currency | is_lto |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store Amenities objects from ihop.com. All fields typed and schema-versioned.
"store_id": "3142", "has_wifi": true, "has_delivery": true, "has_curbside": true, "open_24_hours": true, "online_ordering_enabled": true, "dining_room_open": true
| # | store_id | has_wifi | has_delivery | has_curbside | wheelchair_accessible | open_24_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our IHOP scraper handles every layer of the platform: store locators, regional pricing variations, nutritional profiles, and dynamic promotions — with geo-targeted sessions and API intercept built in.
Extract all 1,800+ IHOP locations including exact coordinates, address details, phone numbers, and timezone information.
Capture exact menu prices mapped to specific store IDs, accounting for franchise-level pricing variations across regions.
Monitor core menu items alongside Limited Time Offers (LTOs) like Pancake of the Month and seasonal specials.
Extract detailed macro-nutritional profiles, calorie counts, and specific allergen warnings for every item on the menu.
Track daily operating hours, 24-hour status, holiday closures, and specific times for dining room vs delivery availability.
Compare base dine-in prices against first-party delivery markups to analyse margin strategies.
Track specific promotional windows, discounted items, and availability of value-menu offerings per location.
Identify locations offering curbside pickup, free Wi-Fi, wheelchair accessibility, and online ordering capabilities.
Run daily or weekly pipelines to detect menu additions, price hikes, or discontinued items with automated change-logs.
Brief in. Clean data out.
Provide target regions, specific store IDs, or request a full national extraction. We design the schema together.
We configure Playwright crawlers, geo-targeted proxies, and API intercepts to simulate local user sessions on ihop.com.
Schema validation, price-outlier detection, and location completeness checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Restaurant chains use dynamic geo-routing and complex API payloads. Here's how we stay resilient.
IHOP pricing varies heavily by franchise and region. We use city-level residential proxies and inject specific store IDs into session cookies to guarantee the prices extracted match the exact location requested, bypassing generic national menus.
Rather than scraping raw HTML, our Playwright instances intercept the underlying GraphQL and REST API responses used by IHOP's frontend. This yields cleaner data, faster execution, and captures hidden fields like exact stock status and internal item IDs.
Pancake stacks come with dozens of modifier options. We extract the full tree of customisation possibilities, including upcharges for specific syrups, fruit toppings, and side substitutions, mapping them to a normalised schema.
Limited Time Offers appear and disappear rapidly. Our change-detection system flags new promotional categories immediately, ensuring seasonal menus are captured before they are removed from the site.
Every run emits structured logs. We alert on null-rate spikes, missing store directories, and schema drift if IHOP updates their frontend architecture. SLA uptime is contractual.
Rival restaurant chains track IHOP's regional pricing strategies, LTO rollouts, and menu inflation over time.
Food delivery platforms compare first-party pricing against third-party marketplace markups to optimise commission models.
Retail analysts map IHOP store density and operating hours against demographic data to identify expansion opportunities.
Health and fitness applications ingest macro-nutritional and allergen data to keep their food logging databases current.
Economic researchers track the price of core items (like the Rooty Tooty Fresh 'N Fruity) across regions to measure consumer price inflation.
Marketing agencies analyse the frequency, duration, and discount depth of Hoppy Hour and Pancake of the Month campaigns.
"IHOP's menu pricing and promotional strategies vary drastically across 1,800+ locations — but the data is invisible without localised extraction."
Most teams underestimate the investment required: reliable IHOP scraping requires localised residential proxies to bypass geo-routing, full JavaScript rendering for dynamic menus, and daily maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our ihop.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 intercepts API payloads and handles location-cookie injection for accurate regional data.
We maintain pools of US-based residential ISP proxies mapped to specific cities, ensuring the menu prices extracted reflect local reality.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About ihop.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from ihop.com is generally permissible under applicable law. DataFlirt targets only public, non-authenticated menu, pricing, and location data. We do not extract personal data or circumvent authentication walls. Clients should review IHOP's ToS and consult legal counsel for specific use cases.
We use city-level residential proxies and inject specific store IDs into the browser session. This simulates a local user selecting a specific restaurant, ensuring we capture the exact franchise pricing rather than generic national averages.
Yes. Our change-detection pipelines automatically flag new categories and items, ensuring promotions like Pancake of the Month are captured as soon as they go live.
We can configure pipelines to run daily, weekly, or monthly depending on your requirements. A full extraction of all 1,800+ US locations typically completes within a 4-hour window.
Yes. We extract the full nutritional profile (calories, fat, sodium, carbs, protein) and specific allergen warnings for every item on the menu.
Our minimum engagement typically starts with a full national extraction of all store locations and their associated menus on a weekly cadence. Contact us for a scoped quote based on your exact frequency needs.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off store directory dump or a continuous price-monitoring feed across all 1,800+ locations — we scope, build, and operate the pipeline. Tell us what you need.
