Crex Data Scraping - Solving Accuracy and Data Consistency Issues in Cricket Analytics

Introduction

Eating out in Indian cities now comes with a layer of deals: flat discounts when you pre-book, extra savings with a certain bank card, happy-hour drinks, and passes that unlock offers at partner restaurants. These offers change by day, time slot and platform.

Restaurant offers data for India is a structured record of public dine-in deals listed on platforms such as District, Swiggy Dineout and EazyDiner: restaurant, locality, cuisine and cost for two, plus offer type, discount, time slot, bank partner and validity. Deal-comparison apps, restaurant groups and card programmes use it to compare offers fairly and see where discounting is heaviest. The delivery-side counterpart of this data is covered in our Swiggy vs Zomato city-wise menu price and delivery fee analysis.

Who uses dine-in offer data

Deal-comparison and food-discovery apps – show users the best public offer for the same restaurant and slot.
Restaurant groups – benchmark their discounts against nearby competitors in the same cuisine and price band.
Banks and card programmes – see which restaurants carry rival card offers and where coverage is thin.
Beverage and F&B brands – find outlets running drinks or happy-hour deals in target areas.
Analysts – track discount intensity as a signal of competition in the dining-out market.

How the main platforms differ

Platform	Typical public offer types	Data notes
District	Pre-book deals, walk-in offers, bank offers, pass-linked offers	Part of a broader going-out app (dining, movies, events)
Swiggy Dineout	Flat % off, pre-book slots, bank offers	Part of Swiggy's out-of-home business
EazyDiner	Flat % off, pre-book deals, payment offers	City and locality pages list cuisines and deals
magicpin	Local rewards and cashback	Check dine-in coverage by city before scoping

Platform features change often – confirm the current set-up before each project. Restaurant-chain level menu and pricing extraction is covered in our restaurant chains orders, menus and pricing API.

The fields to collect

Field	Description
platform / restaurant_id	Source and listing ID
restaurant_name / brand	Name and chain where applicable
city / locality / pincode	Location
cuisines / cost_for_two	Profile and price band
rating / rating_count	Public popularity signals
offer_type	Pre-book, walk-in, bank, pass, drinks
discount_pct / max_cap	Depth and caps
time_slot / days	When the offer applies
bank_partner	Card or bank named
valid_till / capture_time	Validity and capture time

How to collect and clean offer data

Step 1 – Scope

Choose cities, localities, platforms and restaurant types. One metro and a few localities is a good pilot.

Step 2 – Capture by slot

Offers vary by time and day, so capture lunch, dinner and weekend views on a fixed schedule.

Step 3 – Parse offers

Turn free text ("Flat 20% off on total bill, 12–4 pm") into structured fields: type, percentage, cap, slot.

Step 4 – Match restaurants across platforms

Match on name, address, locality and coordinates; review low-confidence matches manually.

Step 5 – Track changes

Compare each snapshot with the last to log new, changed and expired offers.

Cross-platform booking and menu data extraction — the layer underneath the offer feed — is covered in our food booking API and menu data extraction service.

Reading discount depth by locality

Locality view	Metric	Actowiz reading
Median headline discount	Typical offer, not the outliers	[Insert Actowiz dataset value]
Share with bank offer	Card-programme coverage	[Insert Actowiz dataset value]
Share with pre-book deal	Slot-based discounting	[Insert Actowiz dataset value]
Weekday vs weekend gap	Off-peak discounting	[Insert Actowiz dataset value]

Use medians, split by cost-for-two band, and report headline and "with bank offer" discounts separately.

Use cases

Best-deal display for comparison apps, by restaurant and slot.
Competitor benchmarking for restaurant groups in each locality.
Card programme mapping of partner restaurants and rival offers.
Happy-hour finder for beverage brands.
Market monitoring of discount intensity over months.

Legal and compliance basics

Collect only public listing and offer information without logging in. Do not collect reviewer or diner personal data, do not collect booking volumes (not public), and respect each platform's terms and rate limits. This is a market-intelligence feed, not a booking or POS integration.

Build vs buy

Slot-based snapshots, offer parsing and cross-platform matching take ongoing effort. A managed provider can run a one-city pilot, then scale to more cities with the same schema.

Frequently Asked Questions

What is restaurant offers data?

Structured data on public dine-in deals – offer type, discount, time slot, bank partner and validity – linked to restaurant, locality, cuisine and cost for two.

Which platforms list dine-in offers in India?

Mainly District, Swiggy Dineout and EazyDiner, with magicpin in some cities. Coverage varies by city.

Can booking volumes be provided?

No. Bookings are not public; ratings and rating counts are used as public popularity signals.

How often do offers change?

Often by day and slot; daily or several-times-a-week snapshots suit most comparison apps.

Can dine-in and delivery offers be compared?

Yes, with a channel field and restaurant matching across dine-out and delivery listings.

Conclusion

You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

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