People are now using their mobile devices for everything – from purchasing products online to copping sneakers and even taking orders for food.
This means that online data is becoming increasingly important to everyone, including food vendors. Food delivery platforms can use this data to set the best prices, provide the best customer experience and even get new customers.
But the data needs to be collected in large quantities and in real-time to hold any meaningful impact, and if you have ever tried to collect data from even a single webpage, then you know how difficult this can be.
This is why automated web scraping for food delivery data is important. It helps you acquire large quantities of data without stress and delivers all the data you need within minutes or hours of making the request.
And automated data extraction is perfected by using tools such as residential proxies, which help to solve the various challenges you may face when trying to collect this data from different sources.
What is Food Delivery Data Scraping?
Food delivery data scraping can best be described as using automated tools to collect large quantities of useful data from the restaurant industry.
This is opposed to the manual process in application some years ago. The manual process involves collecting data manually from one webpage at a time.
This process is tedious, inefficient and delivers less than accurate data, which businesses cannot rely on to make proper decisions.
But with automated food delivery scraping, companies can quickly and efficiently collect large amounts of data.
This data can be used in several ways by the brands that collect them.
How Companies Can Benefit From This Data
Some of the ways that a company can benefit from harvesting food delivery data include the following:
- Understanding Consumer Sentiments
Consumer sentiments and preference are as valid in this industry as it is in every other industry. And brands that can align their food with what customers are looking for are often more likely to dominate the market as they continue to win trust and patronage.
Food delivery data can be used to understand exactly what the consumer’s pain points are. Products and services can be adjusted to meet those needs better.
- Creating Proper Strategies
A business without a strategy is a business that may quickly fold. Brands need strategies to decide on the best food to deliver and the right way to reach more customers.
Collecting data from the food market can help your business create the necessary intelligence not only to continue to survive but even completely dominate the market.
- Monitoring The Market and Competition
Like every other product, the prices of food also determine to a large extent how much revenue a brand makes.
Companies that set uninformed prices can easily hurt their business by either pushing the customers to the competitors or losing profits.
Collecting data from the restaurant industry will help you market and competition closely and then decide how to dominate the market and outshine the competitors.
- Price Optimization
Another application of data collected from the food industry is optimizing prices to benefit both the company and its customers.
If prices are raised too high, buyers will run to other sellers, and if they are dropped too low, the company will quickly incur losses.
Data is often used to determine the best prices at each turn and for each food item to make the brand more successful.
Challenges of Food Delivery Data Scraping
Scraping food delivery data can also be a complicated process filled with several challenges, including the following:
- IP Blocks and Bans
Every device on the internet is enabled by an Internet Protocol (IP) address. This address helps each user to make requests and makes it possible for the internet to identify each user as a unique entity.
It is easier to send back results if all the users can be uniquely identified. However, it is also possible to block or ban a user if they can be identified with so much ease.
- CAPTCHA Tests and Anti-Scraping Measures
Some other common challenges that a brand may face while extracting food delivery data are anti-scraping technologies such as the CAPTCHA test.
This large-scale web scraping often involves scraping bots, and these measures are often set in place to inhibit bots from scraping the targeted content by presenting tests that human users can also solve.
- Complex Website Structures
The way a website is structured can also impede food data scraping. For instance, some scrapers find it almost impossible to render JavaScript content and since most websites are built using this language, these scrapers crash once they come in contact with these structures.
- Location-Based Restrictions
Lastly, some brands may face location-based challenges when trying to scrape food delivery information.
This type of challenge is generally referred to as geo-restriction and is often implemented to target and block users in certain parts of the world.
How Proxies Can Help Overcome These Challenges
Fortunately, the case is not completely hopeless as proxies such as residential proxies can be used to match and mitigate any of the above challenges. Read this from Oxylabs to learn more about proxies and how they can help you.
With this type of proxies, it becomes easier to use different IPs for different requests, thereby eliminating any need for IP blocks or bans. Using different IPs and locations can also help to bypass any location-based limitations, as residential proxies provide you with multiple IPs and locations to choose from.
Proper proxies like these are also excellent at understanding and navigating websites regardless of how they are developed. And because most of these proxies are designed using AI and Machine Learning, they can mimic human behavior and easily solve anti-scraping measures and CAPTCHA tests.
Conclusion
Automated food delivery data scraping makes the right data available at the right time. This data can be useful to both businesses and customers.
And if getting the data is burdened with various challenges, residential proxies can be used to solve the problems and clear the path to accurate data acquisition.