How to Scrape Amazon PPC AD Data in Real-Time and Completely Free?

Amazon PPC – The Concept

Amazon PPC is the digital marketing approach on the Amazon marketplace, in which sellers promote products and recompense some fees only when customers click on the ad. Sponsored Product ads are one kind of Amazon PPC advertising shown in the fixed spots on search result pages for particular keywords.

PPC data gives businesses insights into ad performances, visibility, and ROI, permitting them to make data-driven decisions. Furthermore, businesses can utilize PPC data for evaluating competitors' tactics, like keywords their opponents are investing with and product types sold under every keyword, and utilize this data for optimizing their strategies.

Why Do You Require a Web Extraction Service to Have PPC Data?

Web extraction services can assist in obtaining Amazon PPC data more efficiently and precisely than depending on PPC tools. Data scraping is the procedure of scraping data from web pages, and it may be programmed to get data from Amazon search results in pages in real-time. With data scraping, businesses can gather up-to-date data on products, keywords, or competitors' tactics to optimize PPC advertising campaigns.

With accurate and updated data using web extraction, businesses can make data-driven decisions for better PPC campaigns' performances, increased visibility, and making the most of their ROI.

The Keywords

• Headset
• Headset Bluetooth
• Headset for Laptop
• Headset for mobile
• Headset wire
• Headset wired
• Headset wired with mic
• Headset wireless
• Headset wireless with mic
• Headset with mic

Scraping Procedure

The blog post will show how to scrape Amazon PPC data using Selenium and Python library. Our objective is to scrape names of sponsored products for every keyword from search result pages and store data in the CSV file.

To get this, we'll help you go through the code to show how to set a web driver, do a search, scrape the applicable data, and save it in the CSV file.

So, let's start the journey!

Import Necessary Libraries

Initially, we will import the necessary libraries:

BeautifulSoup: This is the module of the Python bs4 library, which is used to parse and pull data out from XML and HTML files.

CSV library: This is used to write and read from CSV files and for data manipulation quickly and effectively.

lxml Library: This Python library processes XML and HTML files. An etree or ElementTree is the module in lxml utilized for parsing XML documents.

Random library: this is used for generating random numbers in Python.

Selenium library: This helps in browser automation. A WebDriver API from the Selenium library controls web browsers and automates tasks like opening or closing the browser, getting a page element, and interacting with them.

Time library: This is used to represent time in various ways.

Initialization

To start the web scraping procedure, we initially want to initialize two listed objects called data_list and product_keywords.

product_keywords is the listing of 10 keywords we wish to search on Amazon, whereas data_list stores a listing of sponsored product names for every keyword.

When we ready our listing objects, we'll use a Chrome driver, which helps us automate browser opening and interaction with an Amazon site.

We'll add a --headless alternative to a driver that makes it work in headless mode, meaning that a browser window won't get visible during a scraping procedure.

Using a Chrome driver, we can navigate to an Amazon homepage as the starting point for the data scraping procedure. From there, we'll look for all ten keywords and scrape applicable PPC data.

Data Extraction

We will go through every keyword from a product_keywords listing during the scraping procedure. For every keyword, we print the message to allow a user to know which keywords a data scraping procedure is proceeding with.

Next, we find a search box on Amazon’s site using Selenium’s find_element() technique with the ‘ By ’ class. Here, we utilize the name attribute of the search box element to find it. After that, we clear a search box, enter a keyword, and search. The search result page URL is retrieved with the get_dom function named with the URL.

This returns a DOM of search result pages stored in the variable called page_dom. This get_dom function finds a page source of search result pages and converts that into the BeautifulSoup object. Then, it utilizes an lxml library for converting BeautifulSoup object into the lxml ET object, making it easier to scrape data from DOM with XPath.

One new listing object called product_list is prepared with a keyword like its initial element. The list stores the names of supported products that, Initially, sponsored products on search result pages are recognized and saved in the listing and assigned to the variable sponsored_products. After that, for all sponsored products, its name is scraped and appended to a product_list.

When names of all sponsored products for a keyword are saved in the list, that list is added to another list called data_list, and the time delay function is used. After restating through all keywords, a Chrome driver will quit.

Delay of Time

After scraping data for every keyword, a randomized time delay is provided. Most websites do not permit extracting and use definite anti-scraping measures. All these measures could find a data scraper if that makes so many requests very quickly. So, to avoid getting detected, we provide a time delay after every loop iteration.

Write Data into a CSV File

After data scraping, we have to store data so you can use it for various objectives.

Here, we save data in the CSV file. We initially opened the CSV file called “ppc_data.csv” in write mode. Then we make a writer object called the writer and write the row to a CSV file heading for every column, using a writerow() function. The scraped data is to a CSV file with a writerow. The scraped data is saved in a list called data_list .

Conclusion

We have revealed how to extract Amazon-sponsored product names with Selenium and Python. This data is essential for businesses to assess their campaign performances and know competitor tactics. By making data-driven decisions, companies could stay ahead of the competition.

If you don’t know to program, don't feel shy. Our team will collect data, so you don't have to go through the programming part. At Actowiz Solutions, we provide web scraping services to assist customers in getting accurate and updated PPC darts. Contact us now to learn more about our web scraping services and mobile app scraping services.