Top 5 Examples of Using Web Scraping in the Food Industry in 2023

In recent years, the food delivery industry has undergone significant changes:

• In 2024, the online food delivery market is expected to generate revenue of 84 trillion rubles.

However, as the food delivery market expands, it becomes increasingly difficult for businesses to differentiate their brands and generate profit. To gain a competitive advantage, companies in the food delivery sector are seeking new opportunities and implementing new technologies into their workflow.

Web scraping tools allow food industry businesses to optimize and speed up the data collection process, reduce order fulfillment time, and minimize the human effort required to obtain information.

In this article, we explain what food delivery data is, how web scraping is used to collect food delivery data, and discuss the top five use cases for web scraping food delivery data.

What data can be extracted from food ordering platforms?

Web scraping is a data collection method that allows access to geoinformation data on food delivery, automatically extracting it from the source and downloading the extracted data in a structured format. With web scraping, you can access and extract the information listed below.

Food delivery data includes:

• Menu items
• Delivery time
• Business hours
• Discounts and promotions
• Menu images
• Prices/Ratings/Reviews

Restaurant data includes:

• Restaurant names
• Contact information
• Location
• Ratings/Reviews

How to scrape food and restaurant data?

Web scraping tools enable companies to collect food data from food delivery platforms such as Zomato, Uber Eats, Swiggy, Grubhub, and many others. Below is a general workflow for using web scraping tools to extract data from target websites.

1. Identify the website from which you need data.
2. The parser tool will navigate to the page specified in the URL input field.
3. Select the elements to extract, such as food prices, descriptions, and reviews.
4. The parser will extract all necessary data.
5. After scraping is complete, the extracted data will be saved in your chosen format, such as CSV, JSON, etc.

Most food delivery platforms, such as Zomato, Eat Street, Delivery.com, etc., split product data across multiple listing pages to improve page performance and user experience, a practice known as pagination. Pagination can be implemented as a "Load More" button, a "Next" button, infinite scroll, or numeric pagination without a "Next" button.

However, web scrapers often struggle with web page pagination. For example, when searching for "pasta" on UberEats, you may notice that the category page consists of multiple product pages. The web scraper stops extracting data at the end of each listing page. To handle paginated web pages, you can:

• Manually run the parser on each product page.
• Set a pagination selector to visit multiple web pages.
• Build a pagination loop to continue crawling other pages after finishing the current page.

Top 5 use cases for web scraping food delivery data
1. Setting market prices

Market-based pricing is a pricing strategy that enables price optimization. Web scraping allows companies to collect food pricing data, such as discounts and menu prices, from competitors' listing pages.

Before selecting the data you want to scrape, you need to identify competitors and the URLs of their product pages on target food service websites, which will serve as input for your parser. Below are the key steps to identify main competitors:

1. Identify the product you want to price based on current market prices.
2. Identify key competitors that sell the same products and target the same audience as your company. However, for a thorough market analysis, you should also consider indirect competitors. Indirect competitors target the same audience as your company but offer different products or services. Conducting customer surveys, keyword research, and exploring social media platforms and forums such as Quora or Reddit can help identify direct and indirect competitors. For example, McDonald's direct competitors include Burger King, Wendy's (WEN), Taco Bell, and others.
3. Determine which competitor product listing pages you want to scrape data from.

However, focusing solely on pricing to maintain competitiveness rather than on the consumer is not an effective strategy for sustainable growth. First, you need to understand the perceived value of your brand. For example, many customers prefer higher-priced brands that emphasize brand value. Setting a price too high will result in lost sales opportunities for your brand, while setting a price too low will lead to lost revenue.

2. Handling local competition

Local competition in metropolitan areas poses a particular challenge, especially for small and medium-sized businesses. To gain a competitive advantage, you need to understand how your competitors operate and what sets them apart. Web scraping tools allow companies to understand their competitors by extracting food delivery data and restaurant locations based on geographic coordinates.

For example, if you are a B2B company primarily working with local businesses, you can find nearby restaurants by filtering search results by country and city. Food delivery websites allow businesses to filter or find food service companies in a specific area to explore potential partnership opportunities. You can obtain their contact information, rating, website, delivery routes, and business hours to better understand how they operate and reach out to them.

3. Turning customer reviews into insights

73% of people cite customer experience as a factor influencing their purchasing decisions. However, only 49% of American consumers today believe that companies provide good customer service.

Extracting customer review data from various food delivery apps is a labor-intensive and tedious task. Web scraping allows companies to collect restaurant reviews from various food delivery websites. Companies can perform sentiment analysis on the collected customer review data using natural language processing. Extracted words from text data can be classified as negative, positive, or neutral either manually or with a data annotation tool. Businesses can gain insights into their brand, products, or services by performing sentiment analysis on the extracted review data.

4. Optimizing demand forecasting and management

Inaccurate forecasting leads to excess or insufficient inventory. This can be caused by data misinterpretation, changing patterns, and limited data availability. Applying AI-based forecasting in supply chain management reduces errors by 20-50%. AI-driven automated demand forecasting improves accuracy by using data collected in real-time from internal (e.g., ERP systems) and external data sources, such as social media platforms and food delivery platforms. Web scraping allows companies to collect large volumes of data on a weekly, monthly, and yearly basis to feed AI models.

5. Detecting trends in the food industry

Web scraping helps companies extract data from various web sources on menu items, dish descriptions, preparation times, and delivery routes. The obtained data enables companies to identify current trends in the food industry and monitor the ever-changing business landscape in this sector.

For example, in 2023, 85% of consumers changed at least one lifestyle habit to become more environmentally friendly, and 61% of consumers reduced their daily plastic use. Businesses have started adopting more sustainable practices to adapt to shifting consumption patterns.