A client needing a structured view of Hyderabad's restaurant venues and menu prices on Zomato.

Use Case

Market research and menu benchmarking

Region

Hyderabad, India

Sources

Zomato

Number of sources

Venue Profiles

Locality by Locality

Menu Items & Prices

Restaurant by Restaurant

Contact Sales

Key Takeaways

Actowiz collected publicly listed Zomato Hyderabad restaurant menu data for a Hyderabad-focused food-business client in a one-time project kicked off on 8 September 2026. The output was a clean CSV and Excel file linking each venue's locality, cuisines, cost for two and ratings to its menu items, categories, prices and veg or non-veg flags, ready for local market analysis.

What did the client need?

The client, an independent consultant serving the Hyderabad food business, wanted a single picture of the city's restaurant scene. They needed to know which venues operate in which localities, what they serve, and how menus are priced.

Copying listings by hand was not practical. They asked for one structured file, in CSV and Excel, that joined venue details with full menus so they could filter, pivot and compare without further cleaning.

What made this hard?

Venue and menu are separate layers – restaurant-level facts and item-level menus had to be linked by a stable restaurant ID so no item lost its venue.
City-wide scope – Hyderabad spans many localities, so coverage had to be planned area by area to avoid gaps and duplicates.
Inconsistent menu structure – restaurants name categories differently and some list variants or add-ons, which complicates item counts and price comparison.
Text clean-up – item names and descriptions contain emojis, mixed scripts and spacing issues that break spreadsheets if left raw.
Public data only – the file had to contain business listing data, not reviewer identities or any personal information.

How did Actowiz solve it?

Actowiz ran a scoped one-time extraction with a validation pass before delivery. [Verify approach details below with delivery team]

Approach

Locality-based coverage plan – we mapped Hyderabad localities first and collected restaurant listings area by area, then de-duplicated venues that appeared in more than one area.
Two linked tables – a venue table and a menu table share a restaurant_id, and a flat combined sheet was also supplied for quick filtering.
Menu normalisation – prices were stored as numbers, categories were kept as listed, and the veg or non-veg flag was captured where shown.
Text cleaning – item names and descriptions were trimmed, encoded consistently and checked so the CSV opens cleanly in Excel.
Quality checks – we sampled records against live listings and checked for missing prices, duplicate items and empty menus. [Add QA sample size from delivery team]
Compliance first – only publicly visible restaurant and menu fields were collected, with no personal data, in line with India's DPDP framework (EY overview) and the platform's published policies.

Data Attributes / Fields

Field	Description
restaurant_id	Unique key linking a venue to its menu items
restaurant_name	Venue name as listed
locality	Hyderabad area or neighbourhood
cuisines	Cuisine tags shown on the listing
cost_for_two	Listed approximate cost for two (INR)
rating / rating_count	Aggregate rating and number of ratings
menu_category	Menu section, e.g. Starters or Biryani
item_name / item_description	Dish name and description as listed
item_price	Listed item price (INR)
veg_flag	Veg or non-veg marker, where shown
scrape_date	Date the record was captured

What were the results?

One analysis-ready file – venue and menu data for Hyderabad delivered in CSV and Excel, providing a structured dataset for restaurant and menu analysis.
Locality-level view – the client could compare cuisines and cost for two across different areas of Hyderabad without manual data collection.
Menu price benchmarking – item-level prices organised by category enabled like-for-like comparisons of popular dishes across restaurants.
Clean handover – linked tables and a consolidated flat sheet allowed the client to filter, sort and create pivot tables directly for further analysis.

Project at a Glance

Metric	Value
Use Case	Restaurant venue and menu intelligence for market research
Region	Hyderabad, India
Sources	Zomato (public restaurant and menu listings)
Metrics	Venue profile, cuisines, cost for two, ratings, menu items, prices, veg flag
Reconciliation / Method	Locality-based collection, venue de-duplication, restaurant_id linking, QA sampling
Cadence	One-time (kickoff 8 September 2026)
Output	CSV and Excel (venue table, menu table, combined sheet)

Client Feedback

“Actowiz Solutions provided us with a structured, analysis-ready dataset of restaurant venues and menu details across Hyderabad through Zomato. The consolidated file made it easier for our team to review restaurant offerings, compare menu prices, and derive actionable insights for our food business.”

— Business Analyst, Food Business Company

Frequently Asked Questions

Q: What Zomato data was collected for Hyderabad?

A: Public restaurant listing fields (name, locality, cuisines, cost for two, rating and rating count) and menu fields (category, item name, description, price and veg flag), plus a capture date.

Q: Was any personal data included?

A: No. The file holds business listing and menu data only. Reviewer names, customer details and order data were not collected.

Q: Can this be repeated on a schedule?

A: Yes. The same setup can run weekly or monthly to track menu price changes, new openings and closures.

Q: Can Swiggy or Google Maps be added?

A: Yes. For a cross-source outlet view, see our restaurant outlet intelligence case study, or explore our Zomato data scraping services.

Q: Is there a ready-made dataset instead?

A: For broader coverage, see the Zomato India food dataset or our menu data case study.

Need City-Level Restaurant Menu Data?

Tell us the city, platforms and fields you need, and we will scope a one-time or recurring file. Visit actowizsolutions.com to discuss your data requirement.

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