HoReCa Database India: How FMCG and Beverage Brands Build a Restaurant, Café and Bar List from Public Data (2026)

Introduction

A HoReCa database India teams can use is a de-duplicated list of hotels, restaurants, cafés, bars, pubs, clubs and QSRs, built from public business listings on Google Maps, Zomato, Swiggy and similar sites. Each outlet gets one record with name, address, pincode, outlet type, cuisine, ratings and source IDs, refreshed on a schedule.

FMCG, dairy, beverage and food-service brands use this list to size territories, plan distributor coverage and find outlets they do not yet supply. The market is large: the NRAI India Food Services Report 2024 estimated the sector at Rs 5.69 lakh crore in FY24, growing at about 8.1% a year to Rs 7.76 lakh crore by FY28 (IndiaRetailing on NRAI IFSR 2024).

Below: sources, fields, matching, pincode and cuisine counts, and the legal basics.

Who uses a HoReCa database in India, and why?

Anyone who sells into food service needs to know where the outlets are and what kind they are. A HoReCa database India sales teams trust turns scattered listings into a territory map they can act on.

• FMCG and dairy brands – plan which outlets a distributor should cover and spot white space by pincode.
• Beverage companies – target cafés, bars, pubs and clubs separately from family restaurants and QSRs.
• Food-service suppliers and equipment makers – find new openings and growing clusters, such as cloud kitchens.
• Startups and SaaS sellers – build a lead list of outlet businesses for POS, ordering or loyalty tools.

The organised segment is growing faster than the market overall. NRAI's 2024 report projected about 13.2% CAGR for organised food services, against 8.1% for the sector as a whole (Restaurant India on NRAI IFSR 2024). New chain outlets open every month, so a list built once goes stale quickly.

In the first week of October alone, we received five separate requests for this kind of data: a downloadable list of hotels, restaurants, bars, pubs, clubs and QSRs; HoReCa data for a startup; Indo-Chinese restaurant counts by pincode, city and state; outlet data for West Bengaluru; and a general food-outlet database.

Which public sources list Indian restaurants, cafés and bars?

No single source covers every outlet, so a HoReCa database India teams rely on combines several. Google Maps has the widest coverage, delivery apps add cuisine and delivery status, and the FSSAI FoSCoS portal shows licence records for food businesses.

FoSCoS is the FSSAI's online licensing system. Its public "FBO Search" option lets you look up a food business by licence number or by name with state and district FSSAI FoSCoS portal. It is most useful for spot-checking that an outlet is a real, licensed premises, rather than as a bulk source.

What fields should an India HoReCa outlet database include?

A useful HoReCa database India teams can segment has one row per physical outlet, with source IDs kept for every platform where that outlet appears. Collect business data only.

• Outlet identity – outlet name, brand or chain name, a stable outlet ID you create, and source IDs (Google place ID, Zomato and Swiggy restaurant IDs).
• Location – full address, locality, pincode, city, district, state, latitude and longitude.
• Outlet type – hotel, restaurant, café, bar, pub, club, QSR, bakery, cloud kitchen; plus dine-in, delivery or both.
• Cuisine and price band – cuisine tags (for example Indo-Chinese, North Indian, South Indian) and cost-for-two band where shown.
• Popularity signals – average rating and number of ratings per platform, used as a proxy for footfall.
• Status and hours – open, temporarily closed or permanently closed; opening hours; first-seen and last-seen dates.
• Business contact – website and the listed business phone of the outlet where shown; exclude numbers that belong to individuals.
• Source metadata – platform, URL, crawl date and match confidence.

One honest limit: order volumes are not public. When a request asks for "restaurant order data", the closest public proxies are rating counts, review velocity and delivery-app presence, and these should be labelled as proxies.

How do you de-duplicate outlets across Google Maps, Zomato and Swiggy?

You de-duplicate by matching each listing to one physical outlet using location, normalised name and supporting attributes, then keeping every source ID on the merged record. Without this step, one restaurant can be counted two or three times.

• Normalise names – lower-case, strip words such as "restaurant", "cafe" or "the", and standardise spellings ("Chinese Wok" and "Chinese-Wok").
• Normalise addresses – standardise locality names, extract the pincode and geocode to coordinates.
• Block by distance – compare only listings within a small radius, such as 100–200 metres, so the matching stays fast and precise.
• Score candidates – combine name similarity, distance, cuisine overlap and phone or website match into a confidence score.
• Review the middle band – auto-accept high scores, reject low ones and send borderline pairs, such as two branches in the same mall, to manual review.
• Roll up to brands – group outlets into chains so you can count "outlets per brand per city".

This is the method behind our restaurant outlet intelligence case study, which reconciled outlets across Google Maps, Zomato and Swiggy to produce outlet counts and rating counts per brand and city.

How do you count outlets by pincode, cuisine and channel?

Once outlets are de-duplicated, counts in your HoReCa database are simple group-bys on clean fields. The work is in defining each segment clearly, so a "bar" or an "Indo-Chinese restaurant" means the same thing in every city.

Cuisine tags need care: decide whether an "Indo-Chinese restaurant" is any outlet carrying the tag or only those where it is the primary cuisine, and state the rule next to the count.

How often should you refresh a HoReCa database?

A HoReCa database India teams rely on should be refreshed at the pace each segment changes. Delivery-app listings and cloud kitchens churn fastest, while hotels change slowly.

Keep first-seen and last-seen dates on every record. An outlet missing from all sources for two refreshes in a row can be flagged as likely closed rather than deleted straight away.

How do you validate outlet data before sales teams use it?

Validate by sampling, cross-checking sources and testing counts against what you expect. A small, regular quality check prevents a bad territory plan.

• Spot-check samples – review a random set of outlets per city against the live listings.
• Licence cross-check – for high-value accounts, confirm the premises through FoSCoS FBO Search.
• Category sanity checks – flag "bars" with no evening hours, or "cafés" with a cost-for-two far above the city norm.
• Count stability – investigate any pincode whose outlet count jumps or drops sharply between refreshes.

Is it legal to build a HoReCa database from public data?

Collecting publicly available business listing data for market analysis is common, but it must be done carefully. Use public business information only, respect each site's terms, and get legal review before you start.

• Business data only – outlet names, addresses, categories and ratings describe businesses. Do not collect personal phone numbers or emails of owners or staff, reviewer identities, or customer data.
• India's DPDP Act – the Digital Personal Data Protection Act, 2023 covers personal data. Many small outlets list an owner's mobile on their profile, so treat contact fields with caution and ask counsel.
• No logins or bypassing – do not log in to collect data, get around access controls or overload sites.
• Licence data use – use FoSCoS for verification, in line with the portal's terms, not as a bulk mailing source.

Build in-house or buy?

Build your HoReCa database in-house if you need one city once and have engineers who can maintain scrapers and matching logic. Buy a managed feed if you need many cities, several sources and regular refreshes.

What questions should you ask a HoReCa data vendor?

Ask questions that reveal coverage, method and compliance, not just headline outlet numbers.

• Which sources do you use, and can you show coverage by city and outlet type?
• How do you de-duplicate outlets across platforms, and do you share match confidence?
• How do you define cuisine and outlet-type segments?
• How often do you refresh, and how do you detect closures?
• Which fields do you exclude for privacy reasons?
• Can I get a sample for my own pincodes before I commit?

Actowiz runs managed outlet and menu collection through its Zomato data scraping and Swiggy data scraping services, combined with Google Maps listings where needed.

Frequently Asked Questions

What is a HoReCa database?

It is a list of hotels, restaurants and cafés (and usually bars, pubs, clubs and QSRs), with one record per outlet. A HoReCa database India brands use typically includes address, pincode, outlet type, cuisine and ratings.

How many restaurants are there in India?

There is no single official count of every outlet. Industry reports such as NRAI's estimate market size and growth, while an outlet count for a given area is best built from de-duplicated public listings.

Can I get restaurant counts by pincode?

Yes. After matching listings to unique outlets, you can count by pincode, city and state, and filter by cuisine, such as Indo-Chinese, or by outlet type, such as bars.

Does a HoReCa database include phone numbers and emails?

It can include an outlet's public business website and listed business phone where appropriate. Personal numbers and emails of owners, staff or customers should be excluded, and contact fields should go through legal review.

Can I get restaurant order volumes?

No. Order volumes are not published by delivery apps. Rating counts, review velocity and delivery presence can serve as labelled proxies for popularity.

How often should an outlet list be updated?

Monthly suits most restaurants and QSRs. Cloud kitchens need weekly or fortnightly checks, and hotels can be refreshed quarterly or half-yearly.

Conclusion

A reliable HoReCa database India teams can act on is built, not downloaded once. Combine Google Maps, Zomato, Swiggy and other public listings, match them into unique outlets, segment them with clear rules, refresh them on a schedule and keep personal data out. Start with one city and a sample before you scale.

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