Boosting Restaurant Launch Success with Pre-Scraped Zomato & Swiggy Data

This case study showcases how iWeb Data Scraping empowered a cloud kitchen brand to make data-driven decisions using Zomato Food Data Scraping Services and Extract Swiggy Food Delivery data. By leveraging our advanced scraping services, the client accessed actionable insights into food trends, competitor pricing, and promotions. Our tailored scraping APIs provided structured, location-specific data that fueled a successful multi-city launch. With real-time and historical restaurant analytics, the client optimized their menu, pricing, and promotional strategies — significantly improving launch performance and ROI.

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The Client

Our client is a modern cloud kitchen brand aiming to establish a strong presence in India’s top metro cities. With plans to launch simultaneously in Pune, Bengaluru, Delhi, Hyderabad, and Ahmedabad, they sought data-backed decisions to fine-tune their menu offerings, pricing, and marketing. To stay ahead in the highly competitive food delivery landscape, the client turned to iWeb Data Scraping for deep insights from Zomato and Swiggy.

Client's-Challenge

The Challenges

Launching in diverse markets without access to real food delivery data presented several challenges for the client. Without local benchmarks, their pricing strategy was largely based on guesswork, leading to potential mismatches with customer expectations. Additionally, they lacked regional food insights, making it difficult to identify the most popular cuisines and dishes in each city. The absence of data also meant missed promotional opportunities, as there was no visibility into which offers were driving conversions locally. Operational planning suffered as well, with delivery time variability and logistical complexities going unaccounted for. To overcome these hurdles, the client needed a reliable solution that could deliver accurate, location-specific insights. iWeb Data Scraping addressed these needs through its specialized Food Delivery Data Scraping Services and Food Delivery Data Intelligence, enabling real-time, scalable insights to guide strategic decisions and ensure launch success.

The Solutions

iWeb Data Scraping deployed a customized scraping API that collected structured data from top-performing restaurants on Zomato and Swiggy. Our Food Delivery Data Scraping Services captured key parameters by city, cuisine, and popularity, enabling precise analysis of local markets.

What We Scraped:

  • Menu items with prices and food variations
  • Promotions, discounts, and campaign structures
  • Ratings, reviews, and “most ordered” tags
  • Cuisine-specific popularity rankings
  • Delivery charges and estimated delivery times

Sample Comparative Insights (Delhi vs Hyderabad):

Item Pune Price Mumbai Price Promotion
North Indian High Medium Delhi: ₹460, Hyderabad: ₹390
Biryani Medium High Delhi: ₹420, Hyderabad: ₹350
Chinese Low High Delhi: ₹480, Hyderabad: ₹440
Our-Solutions--Travel-Data-Scraping
Web-Scraping-Advantages

Web Scraping Advantages

  • Real-Time Data Access: Keep up with changing food trends, menus, and offers.
  • City-Wise Customization: Drill down into hyper-local demand and price points.
  • Scalable API Access: Effortlessly retrieve large volumes of structured data.
  • Actionable Insights: Enable smarter menu curation, pricing, and promotions.
  • Operational Planning: Use delivery time insights for better logistics and staffing.
  • The Strategy: Data-Driven Launch Blueprint

    • 1. Menu Personalization

    Launched city-specific menus:

    • Hyderabad: Focused on biryani and fast movers.
    • Delhi: Emphasized North Indian thali and combos.
    • Bengaluru: Introduced fusion wraps and beverages.

    2. Tiered Pricing Approach

    Pricing adapted to local markets:

    • Pune: Slightly lower prices to attract budget-conscious consumers.
    • Delhi & Bengaluru: Maintained mid-range pricing aligned with competitors.

    3. Intelligent Promotions

    Promotion strategies based on scraped campaign data:

    • ₹75 OFF on first orders (to match platform trends).
    • Lunch-hour discounts in Hyderabad due to high midday order volumes.

    4. Operational Optimization

    City-wise delivery data helped forecast:

    • Peak delivery times
    • Packaging delays due to traffic
    • Staffing needs per region

Final Outcome

  • 2.4x Increase in First-Month Orders Compared to previous launches without data intelligence.
  • 18% Lower Customer Acquisition Cost Effective pricing and promotions led to higher conversion rates.
  • 25% Faster Break-Even Timeline Thanks to efficient inventory and margin optimization per city.
  • 40+ Hours Saved Monthly Manual research replaced by automated, structured restaurant data feeds.
Final-outcome

Client Testimonial

"Working with iWeb Data Scraping gave our team the clarity and edge we needed. We didn’t just guess—we knew what our customers wanted in each city. The data on cuisines, pricing, and delivery patterns helped us customize every part of the launch. It’s the difference between launching and launching smart."

– Head of Strategy, Cloud Kitchen Brand

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