Pay-per-click (PPC) advertising data on Amazon can be a valuable tool for businesses to reach their target audience and drive sales. With Amazon being one of the most popular online marketplaces, utilizing Amazon PPC can be a game-changer for businesses looking to promote their products to millions of potential customers. However, gathering real-time data on your PPC campaigns can be challenging, especially if you're on a tight budget.
In this blog, we will show you how to access Amazon's PPC ad data for free, so you can make informed decisions about your campaigns and optimize your results. Whether you're a seasoned marketer or just starting out, this guide will help you get the most out of your PPC efforts. So, let's dive in and explore the world of Amazon PPC data!
What is Amazon PPC?
Amazon PPC (Pay-Per-Click) is a digital marketing strategy on the Amazon marketplace, where sellers promote their products and pay a fee only when a customer clicks on their ad. Sponsored Product ads are a type of Amazon PPC advertising displayed in fixed spots on the search results page for specific keywords.
Retailers bid for keywords and the highest bidder's product is listed as a sponsored product on the search results page when customers search for those keywords. The advertising algorithm that Amazon uses to determine the sponsored products' ranking takes into account various factors, including keyword and product relevance, ad spend, and historical performance.
PPC data provides businesses with insight into their ad performance, visibility, and return on investment, allowing them to make data-driven decisions. Additionally, businesses can use PPC data to evaluate competitors' strategies, such as the keywords their competitors are investing in, the types of products sold under each keyword, and use this data to optimize their strategy.
In this blog, we'll explore how to scrape the names of sponsored products that appear on the search results page for ten different keywords on Amazon.
Why do I need a web scraping service to get the PPC data?
To obtain real-time PPC data on Amazon, relying solely on Amazon PPC tools may not be sufficient. These tools typically index data once a day, resulting in obsolete data that can negatively impact campaign performance. For example, if data was collected at 6 am and used at 6 pm, a significant portion of the data may be outdated. That's why many leading brands use web scraping services and PPC tools together.
Web scraping services can help obtain PPC data from Amazon in a more efficient and accurate way than relying solely on PPC tools. Web scraping is the process of extracting data from web pages automatically, and it can be programmed to retrieve data from Amazon's search results pages in real time. This means that with web scraping, businesses can collect up-to-date information on keywords, products, and competitors' strategies to optimize their PPC advertising campaigns.
Moreover, web scraping services can retrieve data that is not available via Amazon's APIs or PPC tools. This could include data such as product descriptions, reviews, and pricing information, which can provide a comprehensive understanding of competitors' advertising strategies and the market as a whole.
By obtaining accurate and up-to-date data through web scraping, businesses can make data-driven decisions to improve their PPC campaigns' performance, increase visibility, and maximize their return on investment.
To help you understand, in this blog we'll explore how we helped a headphone manufacturer obtain PPC data for specific keywords using web scraping.
The Keywords
Headset
Headset wire
Headset with mic
Headset wired
Headset wireless
Headset bluetooth
Headset wired with mic
Headset wireless with mic
Headset for mobile
Headset for laptop
Scraping Process
In this blog post, we'll demonstrate how to extract PPC data from Amazon using Python and the Selenium library. Our goal is to extract the names of sponsored products for each keyword from the search results page and store the data in a CSV file.
To achieve this, we'll walk you through the code step-by-step to show you how to set up a web driver, perform a search, extract the relevant data, and store it in a CSV file. By the end of this tutorial, you'll have a working Python script that can scrape and store PPC data from Amazon for any given keyword.
So, let's dive in and explore the code!
Import Required Libraries
As a first step, we will be importing the required libraries. Here, we import the following libraries:
BeautifulSoup: It is a module of python’s bs4 library that is used for parsing and pulling data out of HTML and XML files.
lxml library: It is a python library used for the processing of HTML and XML files. An ElementTree or etree is a module in lxml used to parse XML documents.
Selenium library: It supports browser automation. The WebDriver API from selenium library is used to control web browsers and automate tasks such as opening and closing a browser, finding elements on a page, and interacting with them.
time library: It is used to represent time in different ways.
random library: It is used to generate random numbers or randomize a process in Python.
csv library: It is used for writing to and reading from csv files and for the manipulation of data in a simple and efficient way.
from bs4 import BeautifulSoup
from lxml import etree as ET
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from webdriver_manager.chrome import ChromeDriverManager
import csv
import random
import timeInitialization
To get started with the web scraping process, we need to first initialize two list objects named product_keywords and data_list.
product_keywords is a list of 10 keywords that we want to search for on Amazon, while data_list is used to store the list of sponsored product names for each keyword.
Once we have our list objects ready, we'll start a Chrome driver that allows us to automate the process of opening a browser and interacting with the Amazon website.
We'll also add the --headless option to the driver, which makes it run in headless mode, meaning the browser window won't be visible during the scraping process.
With the Chrome driver up and running, we can now navigate to the Amazon homepage, which serves as our starting point for the data extraction process. From here, we'll search for each of our 10 keywords and extract the relevant PPC data.
# Keywords to search for on Amazon
product_keywords = [
"Headset",
"Headset wire",
"Headset with mic",
"Headset wired",
"Headset wireless",
"Headset bluetooth",
"Headset wired with mic",
"Headset wireless with mic",
"Headset for mobile",
"Headset for laptop",
]
# List to store the data for each keyword
data_list = []
# Start a Chrome driver
options = webdriver.ChromeOptions()
options.add_argument('--headless')
driver = webdriver.Chrome(ChromeDriverManager().install(), options=options)
# Navigate to Amazon homepage
driver.get("https://www.amazon.in")Extracting Data
During the extraction process, we will loop through each keyword from the product_keywords list. For each keyword, we first print a message to let the user know for which keyword the data extraction process is going on.
Next, we locate the search box on Amazon’s website by using Selenium’s find_element() method and ‘ By ’ class. In this case, we use the name attribute of the search box’s element to locate it. Then we clear the search box, enter the keyword into it and search. The URL of the search results page is retrieved, and get_dom function is called with this URL.
The get_dom function gets the page source of the search result page and converts it into a BeautifulSoup object. It then uses the lxml library to convert the BeautifulSoup object into an lxml ET object, which makes it easier to extract information from the DOM using XPath. It returns the DOM of the search results page which is then stored in a variable named page_dom.
def get_dom(url):
"""
Get the page source as a BeautifulSoup object, then convert to an lxml ET object.
"""
driver.get(url)
page_content = driver.page_source
product_soup = BeautifulSoup(page_content, "html.parser")
dom = ET.HTML(str(product_soup))
return domA new list object named product_list is initialized with the keyword as its first element. This list is used to store the name of sponsored products that appear for that keyword. First, the sponsored products on the search results page are identified and stored in a list and assigned to a variable named sponsored_products. Then, for each sponsored product, its name is extracted and appended to the list product_list.
Once the name of all the sponsored products for that keyword is stored in the list, the list is appended to another list named data_list, and a time delay function is called. After iterating through all the keywords, the Chrome driver is quit.
# Loop through each keyword
for keyword in product_keywords:
# Enter the keyword into the search box
print("Extracting data for keyword", keyword)
search_box = driver.find_element(By.NAME, "field-keywords")
search_box.clear()
search_box.send_keys(keyword)
search_box.send_keys(Keys.RETURN)
# Get the URL of the search results page
url = driver.current_url
# Get the DOM of the search results page
page_dom = get_dom(url)
# List to store the data for each keyword
product_list = [keyword]
# Get the sponsored products for this keyword
sponsored_products = page_dom.xpath('//div[@class="a-row a-spacing-micro"]')
# Loop through each sponsored product
for ele in sponsored_products:
name = ele.xpath("./following::h2/a/span/text()")[0]
product_list.append(name)
# Add the data for this keyword to the overall data list
data_list.append(product_list)
time.sleep(random.randint(3, 5))
# Quit the Chrome driver
driver.quit()Time Delay
After extracting data for each keyword, a random time delay is given. Most of the websites do not allow scraping and therefore deploy certain anti-scraping measures. These measures can detect a scraper if it makes too many requests too quickly. Therefore, to avoid being detected, we give a time delay after each iteration of the loop.
time.sleep(random.randint(3, 5))Writing Data to CSV File
After extraction, we need to store the data so that it can be used for different purposes.
Here, we store the data in a csv file. For this, we first open a csv file named “ppc_data.csv” in the write mode as file. Then we create a writer object named writer and write a row to the csv file, which is the heading for each column, using the writerow() function. All of the extracted data is stored in the list named data_list . We write this data to the csv file using the writerows() function.
with open("ppc_data.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
writer.writerow(
[
"Keyword",
"Product 1",
"Product 2",
"Product 3",
"Product 4",
"Product 5",
"Product 6",
]
)
writer.writerows(data_list)Conclusion
In this blog, we have shown how to scrape Amazon-sponsored product names using Python and Selenium. This information is crucial for businesses to evaluate their campaign performance and understand competitor strategies. By making data-driven decisions, companies can stay ahead of the competition.
If you're not comfortable with programming, don't worry. At Datahut, we offer web scraping services to help clients obtain accurate and up-to-date PPC data. Our team can gather the data for you, so you don't need to go through the programming part. Whether you're a small business or a large enterprise, we can help you make the most of your data. Contact us today to learn more about our web scraping services and data extraction services.