#Scrape Swiggy and Zomato Data

[fooddatascrape1]

How To Scrape Zomato & Swiggy Data Using Python And BeautifulSoup?

Please read this blog to understand How to Scrape Zomato & Swiggy Data Using Python and BeautifulSoup? Food Data Scrape and use it for different business needs.

Know more : https://medium.com/@fooddatascrape/how-to-scrape-zomato-swiggy-data-using-python-and-beautifulsoup-aeb634bd77de

[iwebdatascraping0]

📊 Unlock Deeper Food Delivery Intelligence with City-Wise Menu Trend Analysis Using Zomato & Swiggy Scraping API

In today's dynamic food delivery landscape, staying relevant means understanding how preferences shift not just nationally—but city by city. By harnessing the power of our #ZomatoScrapingAPI and #SwiggyScrapingAPI, businesses can extract granular data to reveal #menu trends, #dish popularity, #pricing variations, and #regional consumer preferences across urban centers.

Whether you're a #restaurant chain planning regional expansion, a #foodtech startup refining your offerings, or a #marketresearch firm delivering insights to clients—real-time, city-specific menu analytics are essential.

With our robust scraping solution, you can: ✔️ Analyze which items are trending in key metro areas ✔️ Adjust your menu for hyperlocal appeal ✔️ Monitor competitor offerings and pricing strategies ✔️ Predict demand patterns based on regional consumption behavior

This level of #data granularity not only boosts operational efficiency but also helps refine marketing strategies, product positioning, and business forecasting.

[foodspark-scraper]

Foodspark is the best Zomato restaurant data scraping company. We always offer unique, real-time, and customized data as per your business requirements. We offer updated and unique data you can depend on.

[lensnure]

Lensnure Solution provides top-notch Food delivery and Restaurant data scraping services to avail benefits of extracted food data from various Restaurant listings and Food delivery platforms such as Zomato, Uber Eats, Deliveroo, Postmates, Swiggy, delivery.com, Grubhub, Seamless, DoorDash, and much more. We help you extract valuable and large amounts of food data from your target websites using our cutting-edge data scraping techniques.

Our Food delivery data scraping services deliver real-time and dynamic data including Menu items, restaurant names, Pricing, Delivery times, Contact information, Discounts, Offers, and Locations in required file formats like CSV, JSON, XLSX, etc.

Read More: Food Delivery Data Scraping

[actowizsolutions0]

Dynamic Pricing & Food Startup Insights with Actowiz Solutions

Introduction

In today’s highly competitive food and restaurant industry, the difference between success and failure often lies in the ability to adapt swiftly to market dynamics. Investors and food startups are leveraging data intelligence to fine-tune pricing models, optimize profitability, and enhance operational performance. At the forefront of this transformation is Actowiz Solutions, a leading provider of web scraping and data intelligence services.

Why Dynamic Pricing is a Game-Changer

Dynamic pricing, also known as real-time pricing, allows businesses to adjust prices based on demand, competitor prices, customer behavior, and other external factors. For food startups, this can be the difference between overstocked perishables and sold-out menus.

Key Benefits of Dynamic Pricing:

Increased Revenue: Charge premium rates during peak demand.

Inventory Optimization: Reduce food waste by adjusting prices on soon-to-expire items.

Improved Competitiveness: Stay ahead by responding to competitor price changes in real-time.

Enhanced Customer Segmentation: Offer tailored pricing based on user location or purchase history.

How Actowiz Solutions Powers Dynamic Pricing

Actowiz Solutions enables startups and investors to collect vast amounts of real-time data from food delivery apps, restaurant aggregators, grocery platforms, and market listings. This data is structured and delivered via API or dashboards, enabling easy integration into pricing engines.

Actowiz Dynamic Pricing Data Flow:

flowchart LR A[Food Delivery Platforms] --> B[Web Scraping Engine - Actowiz Solutions] B --> C[Real-Time Price Data Aggregation] C --> D[Analytics Dashboard / API] D --> E[Dynamic Pricing Models for Startups] D --> F[Investor Performance Insights]

Example Datasets Extracted:

Menu prices from Zomato, Uber Eats, DoorDash, and Swiggy

Grocery prices from Instacart, Blinkit, and Amazon Fresh

Consumer review sentiment and delivery time data

Competitor promotional and discount trends

Performance Tracking with Actowiz Solutions

Beyond pricing, performance tracking is vital for both investors and startups. Actowiz Solutions offers detailed KPIs based on real-time web data.

Key Performance Metrics Offered:

Average Delivery Time

Customer Ratings and Reviews

Menu Update Frequency

Offer Usage Rates

Location-wise Performance

These metrics help investors evaluate portfolio startups and allow startups to fine-tune their services.

Sample Performance Dashboard:

Metric Value Trend Avg. Delivery Time 34 mins ⬇️ 5% Avg. Customer Rating 4.3/5 ⬇️ 2% Promo Offer Usage 38% ⬇️ 10% Menu Item Refresh Rate Weekly Stable New User Acquisition +1,200/mo ⬇️ 15%

Real-World Use Case

Case Study: A Vegan Cloud Kitchen Startup in California

A vegan cloud kitchen startup used Actowiz Solutions to scrape competitor pricing and delivery performance from platforms like DoorDash and Postmates. Within 3 months:

Adjusted pricing dynamically, increasing revenue by 18%

Reduced average delivery time by 12% by identifying logistics gaps

Gained deeper insight into customer sentiment through reviews

The investor backing the startup received real-time performance reports, enabling smarter funding decisions.

Infographic: How Actowiz Helps Food Startups Scale

graph TD A[Raw Market Data] --> B[Actowiz Data Extraction] B --> C[Cleaned & Structured Data] C --> D[Startup Analytics Dashboard] D --> E[Dynamic Pricing Engine] D --> F[Performance Reports for Investors]

Why Investors Trust Actowiz Solutions

Actowiz Solutions doesn’t just provide data—it offers clarity and strategy. For investors:

See real-time performance metrics

Evaluate ROI on food startups

Identify trends before they emerge

For startups:

Get actionable data insights

Implement real-time pricing

Measure what matters

Conclusion

Dynamic pricing and performance tracking are no longer luxuries in the food industry—they're necessities. With Actowiz Solutions, both investors and startups can make informed decisions powered by accurate, real-time data. As the food tech space becomes more competitive, only those who leverage data will thrive.

Whether you’re funding the next unicorn or building it—Actowiz is your partner in data-driven growth. Learn More

[3idatascraping]

Monitor Competitor Pricing with Food Delivery Data Scraping

In the highly competitive food delivery industry, pricing can be the deciding factor between winning and losing a customer. With the rise of aggregators like DoorDash, Uber Eats, Zomato, Swiggy, and Grubhub, users can compare restaurant options, menus, and—most importantly—prices in just a few taps. To stay ahead, food delivery businesses must continually monitor how competitors are pricing similar items. And that��s where food delivery data scraping comes in.

Data scraping enables restaurants, cloud kitchens, and food delivery platforms to gather real-time competitor data, analyze market trends, and adjust strategies proactively. In this blog, we’ll explore how to use web scraping to monitor competitor pricing effectively, the benefits it offers, and how to do it legally and efficiently.

What Is Food Delivery Data Scraping?

Data scraping is the automated process of extracting information from websites. In the food delivery sector, this means using tools or scripts to collect data from food delivery platforms, restaurant listings, and menu pages.

What Can Be Scraped?

Menu items and categories

Product pricing

Delivery fees and taxes

Discounts and special offers

Restaurant ratings and reviews

Delivery times and availability

This data is invaluable for competitive benchmarking and dynamic pricing strategies.

Why Monitoring Competitor Pricing Matters

1. Stay Competitive in Real Time

Consumers often choose based on pricing. If your competitor offers a similar dish for less, you may lose the order. Monitoring competitor prices lets you react quickly to price changes and stay attractive to customers.

2. Optimize Your Menu Strategy

Scraped data helps identify:

Popular food items in your category

Price points that perform best

How competitors bundle or upsell meals

This allows for smarter decisions around menu engineering and profit margin optimization.

3. Understand Regional Pricing Trends

If you operate across multiple locations or cities, scraping competitor data gives insights into:

Area-specific pricing

Demand-based variation

Local promotions and discounts

This enables geo-targeted pricing strategies.

4. Identify Gaps in the Market

Maybe no competitor offers free delivery during weekdays or a combo meal under $10. Real-time data helps spot such gaps and create offers that attract value-driven users.

How Food Delivery Data Scraping Works

Step 1: Choose Your Target Platforms

Most scraping projects start with identifying where your competitors are listed. Common targets include:

Aggregators: Uber Eats, Zomato, DoorDash, Grubhub

Direct restaurant websites

POS platforms (where available)

Step 2: Define What You Want to Track

Set scraping goals. For pricing, track:

Base prices of dishes

Add-ons and customization costs

Time-sensitive deals

Delivery fees by location or vendor

Step 3: Use Web Scraping Tools or Custom Scripts

You can either:

Use scraping tools like Octoparse, ParseHub, Apify, or

Build custom scripts in Python using libraries like BeautifulSoup, Selenium, or Scrapy

These tools automate the extraction of relevant data and organize it in a structured format (CSV, Excel, or database).

Step 4: Automate Scheduling and Alerts

Set scraping intervals (daily, hourly, weekly) and create alerts for major pricing changes. This ensures your team is always equipped with the latest data.

Step 5: Analyze the Data

Feed the scraped data into BI tools like Power BI, Google Data Studio, or Tableau to identify patterns and inform strategic decisions.

Tools and Technologies for Effective Scraping

Popular Tools:

Scrapy: Python-based framework perfect for complex projects

BeautifulSoup: Great for parsing HTML and small-scale tasks

Selenium: Ideal for scraping dynamic pages with JavaScript

Octoparse: No-code solution with scheduling and cloud support

Apify: Advanced, scalable platform with ready-to-use APIs

Hosting and Automation:

Use cron jobs or task schedulers for automation

Store data on cloud databases like AWS RDS, MongoDB Atlas, or Google BigQuery

Legal Considerations: Is It Ethical to Scrape Food Delivery Platforms?

This is a critical aspect of scraping.

Understand Platform Terms

Many websites explicitly state in their Terms of Service that scraping is not allowed. Scraping such platforms can violate those terms, even if it’s not technically illegal.

Avoid Harming Website Performance

Always scrape responsibly:

Use rate limiting to avoid overloading servers

Respect robots.txt files

Avoid scraping login-protected or personal user data

Use Publicly Available Data

Stick to scraping data that’s:

Publicly accessible

Not behind paywalls or logins

Not personally identifiable or sensitive

If possible, work with third-party data providers who have pre-approved partnerships or APIs.

Real-World Use Cases of Price Monitoring via Scraping

A. Cloud Kitchens

A cloud kitchen operating in three cities uses scraping to monitor average pricing for biryani and wraps. Based on competitor pricing, they adjust their bundle offers and introduce combo meals—boosting order value by 22%.

B. Local Restaurants

A family-owned restaurant tracks rival pricing and delivery fees during weekends. By offering a free dessert on orders above $25 (when competitors don’t), they see a 15% increase in weekend orders.

C. Food Delivery Startups

A new delivery aggregator monitors established players’ pricing to craft a price-beating strategy, helping them enter the market with aggressive discounts and gain traction.

Key Metrics to Track Through Price Scraping

When setting up your monitoring dashboard, focus on:

Average price per cuisine category

Price differences across cities or neighborhoods

Top 10 lowest/highest priced items in your segment

Frequency of discounts and offers

Delivery fee trends by time and distance

Most used upsell combinations (e.g., sides, drinks)

Challenges in Food Delivery Data Scraping (And Solutions)

Challenge 1: Dynamic Content and JavaScript-Heavy Pages

Solution: Use headless browsers like Selenium or platforms like Puppeteer to scrape rendered content.

Challenge 2: IP Blocking or Captchas

Solution: Rotate IPs with proxies, use CAPTCHA-solving tools, or throttle request rates.

Challenge 3: Frequent Site Layout Changes

Solution: Use XPaths and CSS selectors dynamically, and monitor script performance regularly.

Challenge 4: Keeping Data Fresh

Solution: Schedule automated scraping and build change detection algorithms to prioritize meaningful updates.

Final Thoughts

In today’s digital-first food delivery market, being reactive is no longer enough. Real-time competitor pricing insights are essential to survive and thrive. Data scraping gives you the tools to make informed, timely decisions about your pricing, promotions, and product offerings.

Whether you're a single-location restaurant, an expanding cloud kitchen, or a new delivery platform, food delivery data scraping can help you gain a critical competitive edge. But it must be done ethically, securely, and with the right technologies.

[fooddatascrape]

Scrape Zomato and Swiggy data using Food data Scrape

Zomato and Swiggy are popular food ordering and delivery apps that have caught consumers' attention. Scrape data from Zomato and Swiggy using Food Data Scrape for restaurant name, restaurant type, menu, pricing, rating review, opening hours, discounts, and more.

Zomato is a rapidly growing restaurant discovering website established in 2008 by Pankaj Chaddah and Deepinder Goyal. Previously, it was named Foodiebay, but in 2010 it was finally renamed Zomato. It delivers information about nearby restaurants and offers facilities, including online ordering, table management, and reservation. Zomato serves 10,000 cities across 36 countries, with nearly 1.2 million famous restaurants having more than 80 million customers monthly. Available in 10 different languages, it has 10 million reviews with 18 million bookmarks. Overall, Zomato is the most comprehensive and user-friendly app allowing people to search nearby restaurants and cafes, order food online, and get it at their doorstep quickly.

Swiggy is a renowned Indian food ordering delivery platform. Started in 2014, the company is in Bangalore with operations in more than 500 cities. The data is as on September 2021. In addition to food delivery niche, Swiggy also delivers grocery on-demand under the brand Instamart and same-day delivery package service as Swiggy Genie.

Both Zomato and Swiggy are a pool of innumerable valuable data. Collecting the data via manual process is a tedious task. Hence, automating the process using web scraper can ease the process.

List of data fields from Swiggy and Zomato

Restaurant’s name

Restaurant’s ID

Address

City

State

Country code

Postal code

Menu

Price range

websites

Vote

Review

Rating

Email Id

Opening hours

Contact details

Why Scrape Swiggy and Zomato Data

There are several significant reasons why scraping Swiggy data is essential. A few of them are as follows:.

Swiggy and Zomato occupy the most significant marketplace when ordering food online. Owing to the threat of Covid-19, home dining increasingly became popular. It has given reason to customers the to order food in the comfort of their homes. The data produced by customers are essential to understand their sentiments and using it for enhancing business.

Scraping Swiggy and Zomato data allows you to find which menu is trendy among the customers and which restaurant offers types of cuisine, including fast foods, healthy foods, multi-cuisine, etc. Being a restaurant owner, you can use the data to add new cuisine to your menu list.

Discounts and offers often lure customers. Scraping data on Swiggy and Zomato lets you understand which restaurant offers discounts and to what extent.

Scraping Zomato and Swiggy Data with Python and BeautifulSoup

One of the advantages of web scraping is to collect data for restaurant lists from several sites. Here, we will retrieve hotel information from Zomato and Swiggy using BeautifulSoup. To scrape Zomato restaurant data or Swiggy data, we will first get the Zomato and Swiggy search result page and set up BeautifulSoup to use CSS selector for querying the page for essential data.

We will pass the user agent headers to avoid blocking to stimulate a browser call. Let’s get the Zomato and Swiggy search results for the desired destination. It will appear like this.

After inspecting the page, we get that each item HTML is in a class-result tag.

Now, break the HTML document into the parts that contain individual item information like this:

After running, we will obtain this.

It indicates that the code isolates the card’s HTML.

After inspecting further, you will see that the restaurant’s name has the class title. So, we will retrieve it.

We will get the names like this.

Now, let’s try to get other data.

After running, we get.

We have all the info, including ratings, reviews, price, and address.

Extracting Swiggy and Zomato Data

Over the years, the complete process of creating apps and websites has grown massively. The objective to scrape Swiggy restaurant data varies from business to business. Food Data Scrape provides a customized data extraction solution to help monitor the data per the requirements. The structured data is available in downloadable format in CSV, XML, Excel, and JSON files

For more information, contact Food Data Scrape now! You can also reach us for all your Food Data Scraping service and Mobile Restaurant App Scraping service requirements. Know more: https://www.fooddatascrape.com/how-to-scrape-data-from-zomato-and-swiggy.php

[iwebdatascraping0]

🚀 City-Based Zomato and Swiggy Scraping API: Fueling #FoodDelivery Intelligence

In the dynamic world of online food delivery, data is the ultimate driver of growth. Our #CityBasedScrapingAPI for #Zomato and #Swiggy delivers comprehensive, real-time #FoodDeliveryData to help businesses gain granular insights based on regional trends, customer preferences, and competitor activity.

With access to localized datasets across Indian cities, companies can: 🍽️ Track city-wise #RestaurantListings 📊 Analyze #MenuData, #PricingStrategies & #Ratings 🔍 Gain visibility into #CustomerBehavior and #OrderPatterns 📍 Compare #DeliveryCharges & #ServiceTimes across locations 💡 Enable smart decision-making for #MarketExpansion and #SalesStrategy

Whether you're a #FoodAggregator, a #CloudKitchen, or a #MarketResearch firm, leveraging real-time APIs allows you to refine your offerings and stay ahead in this fast-paced domain.

🔗 Dive deeper into the use cases and technical capabilities here: 👉 https://lnkd.in/dBD3AwZf

[foodspark-scraper]

The Swiggy API is like a special language that helps different computer programs talk to each other and work together. It’s a powerful tool that allows developers and businesses to connect their apps or websites with Swiggy’s food delivery platform. With the Swiggy API, developers can create amazing apps and websites that make ordering food from Swiggy easy. 

[iwebscrapingblogs]

iWeb Scraping provides the Best Web Data Scraping Services for Zomato, UberEats, Swiggy, Grubhub, Deliveroo, Just Eat, DoorDash, and Postmates.

[fooddatascraping]

Food Aggregator Scraping – Extract Food Aggregator Data

Food Aggregator Scraping of Food Data Scrape assists you in extracting food data from various food aggregator sites like Swiggy, DoorDash, Zomato, Postmates, Eat Street, Delivery.com, etc.

Know more :

[3idatascraping]

Online food delivery apps scraping

3i Data Scraping provides Food ordering data extractor to scrape online food delivery apps like DoorDash, Postmates, goPuff, Seamless, Zomato, Ubereats, Grubhub, Swiggy, etc.

[iwebdatascrape]

Web scraping Swiggy and Zomato restaurant and menu data provides valuable insights for optimizing operations and strategic planning.

Source: https://www.iwebdatascraping.com/web-scraping-swiggy-zomato-restaurant-and-menu-data-improve-menu-optimization.php

[mobileapp14]

Scrape Liquor Delivery Mobile App Data for Market Insights?

Mobile App Scraping offers Liquor Delivery Mobile App Data Scraping Services to extract data from popular Liquor Delivery Mobile Apps such as Swiggy, Hipbar, Zomato, Bottle Rover, Jhoom, BevQ, Living Liquidz, etc.

know more: https://mobileappscrapping13.blogspot.com/2023/09/how-to-scrape-liquor-delivery-mobile.html

[foodspark-scraper]

Foodspark is the best Swiggy restaurant data scraping company. We always offer unique, real-time, and customized data as per your business requirements. We offer updated and unique data you can depend on. Here are the data fields we can scrape

[foodspark-scraper]

Swiggy Restaurant Data Scraping | Scrape Swiggy Restaurant Data

Foodspark provides the Best Swiggy Restaurant Data Scraping services in the USA, UK, Spain and China to extract or Scrape Swiggy restaurant menu competitive pricing. Get the Best Swiggy Restaurant Data Scraping API at affordable prices