With a start in early 2020 and going through 2022, the Covid-19 pandemic has massively forced people's lives to undergo a facelift. The continuous shift toward online shopping has had a significant impact on businesses across the globe. However, in 2023, several major players emerged to give their best shot in the eCommerce business market. And with this, eCommerce Product data scraping is significantly gaining tremendous prominence. Customer satisfaction is one such aspect that drives business growth. Hence, for one business to get successful, the other must have enough data for comparison. In such an instance, it becomes essential to scrape eCommerce data. Shopee and Lazada are two well-known eCommerce platforms. Before delving deep, let’s understand Shopee and Lazada.
Shopee is a well-known, free, secure application for online purchasing and selling items. It is Southeast Asia’s trendy mobile-first platform. Customers can join millions of users who post for products and shop online. Online shopping with Shopee is entirely safe and secure. You can quickly get the ordered items and a money-back guarantee in case of return. iWeb Data Scraping offers the best Shopee data scraping services to scrape desired products.
Like Shopee, Lazada is Southeast Asia’s most popular online shopping and selling application again. This eCommerce leader in South Asia comprises 3,000 brands and 155,000 sellers to serve more than 560 million users. Its marketplace platform is highly adept with technology, advertising, and service offerings, from domestic goods to electronics products, food, cosmetics, toys, and sports items. Alibaba Group Holding Ltd owns the majority of Lazada Group. To collect data from Lazada, avail the best Lazada data scraping services.
Shopee and Lazada are the top online shopping platforms in SEA. As per the report by Blackbox Research and ADNA, they tied to become the top e-commerce platforms with 93% each in terms of user favorability in Southeast Asia. From the above diagram, it shows that convenience is the top reason for online shopping among Southeast Asian consumers at 43%, followed by discount sales at 33%, variety of choices at 25%, getting into the latest trends at 20%, things not available locally 19%, rewards program 17%, and price 11%.
Data collection from Shopee and Lazada is no more a hassle. iWeb Data Scraping offers the best data scraping services at a reasonable rate with the highest data accuracy. Our Shopee and Lazada product data scraping services allow you to collect data related to the brand, product, cost, specifications, product ratings, age, photos, and reviews. As the industry continuously evolves, the data scraping method eases the data collection process and makes it easy to keep pace with the latest market dynamics.
By scraping Shopee and Lazada, you get the following data fields.
Below mentioned are the steps to scrape product data from Shopee and Lazada.
Shopee Data Scraping
Here we are performing a scraping exercise on the Shopee website. We will scrape the most bought and affordable laptops available on Shopee.
Shopee has innumerable information, including product names, prices, ratings, etc.’
Find URLs of Laptops on Shopee.com
Step 1: Get the main page content.
Step 2: Find the laptop URLs on the main page.
Step 3: Add other child tags to search further
Step 4: Now, add .get(“href”) to obtain the href value.
Step 5: Using the findAll() function, collect all the product URLs on the main web page.
Below is the product page that we need to scrape using Shopee scraper.
We will loop the div, get all the necessary information, and save it within each data variable. Combine all these variables and put them within the data frame to get the list of all laptops.
The results will appear like this.
Product Name: In the original data that we extract earlier, the product name comprises emojis that aren’t useful. So, we will remove all data containing emojis in the product name, leaving only the test.
Price: Certain data reflects the price range rather than the price itself. We have to remove the data that do not consist of prices. We must also remove ‘RM’ and – in the price to get a readable data set. The price data need to be in float type.
We only need the numerical values without the favorite and () in the data. By removing those, we will get all numerical values and change them to the ‘int’ type. The missing data will convert to 0.
Some of the processor data have misplaced values. We have to remove or replace the missing values with another value.
The rating data also have some value that requires adding. After converting into float type, we will replace those with the mean of the total rating.
We will then visualize the data into a box plot, histogram, or scatter plot.
Price: In the below diagram, we can see that there are a lot of outliers in the boxplot. Some data could be better than others.
The below scatter plot shows a moderately strong, negative, and non-linear association between favorites and ratings.
In the above diagram, we can see the missing value as nan before performing any cleaning process. After the cleaning process, we obtain the following graph.
We will save to the target location. We will save the data frame into.CSV file.
Here we will describe how to use Lazada scraper to extract a list of products on Lazada.
Step 1: Install Chrome Extension
Step 2: Step a single product
You can get the product information from the Lazada home page, search results page, store page, and product details page. Search for the products, go to the Lazada search results page, hover the mouse on the product, and click the scrape.
Step 3: The collected products will be seen in 'Scrape Products' after scraping. Here you can edit and publish the scraped products from other Lazada stores. If you encounter a system error, fix it using the following steps.
Step 4: Select the product randomly. Click “Open link in new tab” and then slide to verify.
Step 5: Go to the previous error page and scrap the products.
For more information, contact iWeb Data Scraping now! You can also reach us for all your web scraping service and mobile app data scraping requirements.