How to Scrape ShopeeFood Restaurant Data for Competitive Intelligence?

As the food delivery business is increasingly driven by technology, competitive intelligence is a crucial element for food businesses, including restaurants, food chains, and market analysts. Competitive intelligence involves collecting and analyzing competitor data and offerings, including menus, prices, and reviews, so executives and business decision-makers can potentially gain a competitive advantage.

ShopeeFood, Southeast Asia’s leading food delivery platform, is packed with restaurant data and information, including food offerings, pricing, ratings, delivery details, promotions, and more. When this information is collected and analyzed, it can reveal how food businesses compare to their competitors, optimize their menu offerings, identify demand patterns in food, and summarize customer sentiments.

This blog will take you through the process of scraping ShopeeFood restaurant data to glean competitive intelligence – including data types, scraping issues, scraping systems, workflows, the legal aspects of scraping on ShopeeFood, and what future data scraping may look like. In the end, you will see not only how to scrape and develop learnings, but also the importance of doing any scraping behavior ethically and within the discretion of an ever-evolving digital space.

Understanding ShopeeFood Data Types

ShopeeFood themselves have a significant amount of system data, involving multiple layers of datasets – each a goldmine for competitive insights:

• Restaurant Metadata: Contains basic attributes of the restaurant, including name, address, type of cuisine, average rating, rating count, contact information, operating hours, and delivery area. The delivery area can identify where competitive gaps may exist.
• Menu Details: Menu type, including dish name, category, prices, discounts, offers, size of servings, and product description. Scraped menus from over time can reveal pricing changes and emerging food trends.
• Customer Reviews & Sentiment: Star ratings and customer-written reviews reveal trends in scores based on satisfaction, common complaints, and customer priorities. Timestamps of orders reflect busy times of day.
• Delivery & Pricing Data: Coverage of delivery fee, minimum spend for delivery, packaging fees, estimated delivery time frames, and promotions. Monitoring these will help you benchmark competitor strategies.

How to Scrape ShopeeFood Restaurant Data Using Python?

ShopeeFood is a widely renowned food delivery platform in Southeast Asia. If you are looking to analyze restaurants, menus, ratings, or delivery attributes related to ShopeeFood, you will want to ensure that you obtain structured data. Unfortunately, ShopeeFood does not have an official API, so web scraping in Python format is a valid option for gathering this data.

Be sure to read the ShopeeFood Terms of Service before scraping the website.

Use the data only for educational or personal research. Do not send multiple requests simultaneously. Receiving too many requests simultaneously may result in your IP address being blocked.

Initialize the Environment

Begin by installing the necessary libraries:

pip install requests beautifulsoup4 pandas

We will get:

Requests → for requesting ShopeeFood web pages

BeautifulSoup → for parsing HTML

pandas → for storing and analyzing a lot of scraped data

Realizing the ShopeeFood Structure

When opening a restaurant page on ShopeeFood, its contents are sent dynamically. ShopeeFood loads basic information about the restaurant and menu items through JSON API calls, utilizing APIs to access a hierarchical tree of information.

For example, using a ShopeeFood restaurant API endpoint:

https://foody.vn/__get/Restaurant/GetDetail?id={restaurant_id}

In this case, it means we can access and query these APIs directly without worrying about scraping from raw HTML.

Restaurant Data Scraper

Here is an example script for fetching restaurant details:

import requests
import pandas as pd

# Example: Replace with a real restaurant ID
restaurant_id = "123456"

# ShopeeFood API endpoints
detail_url = f"https://foody.vn/__get/Restaurant/GetDetail?id={restaurant_id}"
menu_url = f"https://foody.vn/__get/Restaurant/GetMenu?id={restaurant_id}"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
}

# Get restaurant details
detail_response = requests.get(detail_url, headers=headers).json()
menu_response = requests.get(menu_url, headers=headers).json()

# Extract restaurant info
restaurant_info = {
    "name": detail_response.get("Name"),
    "address": detail_response.get("Address"),
    "cuisines": detail_response.get("Cuisines"),
    "rating": detail_response.get("AvgRating"),
    "delivery_time": detail_response.get("DeliveryTime"),
}

Saving the data

You can perform a performance saving of the scraped data for later analysis:

# Save restaurant info
pd.DataFrame([restaurant_info]).to_csv("restaurant_info.csv", index=False)

# Save menu items
df.to_csv("restaurant_menu.csv", index=False)

Scaling Out: Scraping Multiple Restaurants

To obtain much data, you can loop over a few restaurant IDs.

For example, you can:

• Pull IDs from the ShopeeFood Search Result page.
• For each ID, loop over them and request access to these APIs in advance.
• Adding a delay (i.e, time.sleep()) to avoid overloading the server.

What Are The Technical & Legal Challenges of Scraping?

ShopeeFood has a sufficient level of insight, but there are technical and legal barriers to scraping.

Technical Challenges

• JavaScript renders dynamic content: The restaurant detail doesn’t load and/or refresh until the page is scrolled, so a full-page render step for detail scraping is needed.
• Anti-scraping measures: To prevent circumvention, anti-scraping measures will be implemented, including CAPTCHAs, logins, and browser fingerprinting.
• Rate limiting due to IP: Making multiple requests from the same IP address for an extended period may temporarily block or limit the scraping speed, adding difficulty.

Legal & Ethical Challenges

• robots.txt Restrictions: ShopeeFood’s robots.txt has rules in certain paths or domains disallowing crawling.
• Terms of service prohibit automated scraping or other methods that may jeopardize the account.

What Are The Tools & Techniques For Effective Scraping?

When scraping ShopeeFood data, it is essential to utilize robust scraping tools and a well-structured infrastructure.

Headless Browsers / Automation

Selenium, Puppeteer, and Playwright can render dynamic pages.

• Use stealth plugins to avoid detection that automation is happening.
• Rotate user agent and cookies to appear more authentic.

Proxy Strategy

• Residential or mobile proxy from the same region as your target.
• Continuously rotate IP addresses to avoid hitting the rate limit.

Handling CAPTCHA & Logins

• Services like 2Captcha or Anti-Captcha could be used to solve challenges.
• When scraping authenticated endpoints, you can use the same login flow as the application or web.

Delay Logic & Throttling

• Introduce a random delay or human scrolling logic (i.e., scrolling 30% of the navigation at a time).
• Don’t send the same request for a very short duration (know your limits!).

What are the Use Cases of Scraping ShopeeFood Restaurant Data?

• Market Research and Competitive Analysis

Businesses conducting competitive analysis and market research can utilize ShopeeFood’s data to analyze pricing, delivery times, promotions, and customer ratings, while referencing their competitors. Utilizing ShopeeFood data, they can explore market gaps and identify trends in specific locations.

• Food Delivery Performance Analytics

Being able to access delivery times, fees, and customer feedback allows the analyst to evaluate the quality of service at restaurants and analyze delivery metrics. Analysts can uncover patterns from peak-hour delays to areas with higher fees. They can also begin to assess restaurants that tend to be faster than others.

• Restaurant Business Benchmarking

Because ShopeeFood stores data on restaurants, restaurant owners can find themselves benchmarking their own business against competitors. By comparing their menus with those of competitors, as well as pricing and customer reviews, they can identify opportunities to adjust their offerings or enhance their competitiveness.

• Academic and Data Science Projects

The data extracted from ShopeeFood can be utilized by researchers or students who wish to create their own datasets for machine-learning models. For example, ShopeeFood’s expression analysis and projects relate to predictive models that, for instance, predict restaurant ratings or delivery times. They may also analyze sentiment to understand how to run a successful food delivery service.

• Urban and Geographic Analysis

Planners and data analysts may utilize data from another restaurant to better understand how food accessibility changes across neighborhoods. Researchers can map the density of restaurants to identify gaps in service, the concentration of certain types of cuisine, and the density of food and specific demographics, or examine the correlation between the diversity of cuisine available in an area.

• Pricing and menu tracking

ShopeeFood menu items can be tracked over time to analyze how restaurants adjust prices, introduce seasonal offerings, and implement promotional campaigns. Restaurants may find this helpful in monitoring food inflation or observing how menu items change during the holiday season or other busy periods.

• Application and Tool Development

Scraped data can be used to create application tools, such as discovery apps, dashboards, and price comparison applications. The user could refer to a list of available restaurants by filtering through the cuisine type, budget, or average rating. Businesses can use the data to gain real-time insights into market trends.

What Are The Legal, Ethical & Risk Considerations?

There are numerous potential benefits to scraping vast amounts of data from other competitors in the digital ecosystem, but it is essential to assess the ethics of this approach.

• Follow robots.txt and TOS to avoid disallowed endpoints.
• Use the front-facing API where possible to facilitate the easiest integration of the TOS.
• You should never collect any customer personal details.
• Throttle requests to not overload a system or disrupt a competitor’s system.
• Consider using managed services like Foodspark to remove compliance worries.

Conclusions

The potential to scrape ShopeeFood restaurant data, encompassing menus, prices, reviews, and delivery information, can provide valuable insights for competitive intelligence. While in-house scraping allows for flexibility, it requires considerable technical knowledge and skill as well as continued maintenance. Managed services like Foodspark offer structured, hyper-local datasets that cannot be achieved without considerable experience, time investment, and effort.

The ethical and legality of scraping cannot be overlooked. Taking courses of action, such as reviewing terms of service and throttling requests properly to avoid overloading systems, and being mindful of privacy, are key factors in ensuring effective management.

When smart scraping practices are combined with ethical measures, it unlocks actionable intelligence that provides data-driven insights to support better pricing, platform vs. meals vs. delivery strategies, upgraded menus, and a smarter market expansion strategy, all while respecting the digital ecosystem on which you are economically reliant.