How to Scrape Bing Search Results for Valuable Business Insights
As the second largest search engine with over 1 billion monthly visits, Bing is a treasure trove of data waiting to be mined for competitive intelligence, SEO research, content marketing ideas, and more. By scraping and analyzing Bing‘s search results, you can uncover insights to inform your digital marketing strategies and tactics.
In this comprehensive guide, we‘ll walk you through everything you need to know to effectively scrape Bing search results at scale. From the benefits of mining this search data to step-by-step tutorials and code samples, you‘ll learn how to navigate the technical challenges and extract the specific data points you need.
Why Scrape Bing Search Results?
Search results pages contain much more than just a list of links. They hold valuable data about how Bing understands searcher intent, the types of content that rank well for queries, who your search competitors are, related keywords to target, and more.
By regularly scraping Bing‘s results for keywords relevant to your business, you can:
- Discover new keyword ideas to target based on related searches and "people also ask" recommendations
- Analyze top ranking pages to understand searcher intent and model your own content after what performs best
- Track your site‘s rankings over time to measure SEO progress
- Perform a gap analysis to find competitor keywords you don‘t rank for yet
- Monitor ads placements for competitor insight
- Get content ideas by seeing what topics and types of pages rank well
- Automate repetitive market research tasks
The insights gained from search result data can help with SEO optimization, content planning, media buying, affiliate marketing research, and more.
How to Scrape Bing Results
Now that you understand the value in Bing‘s search data, let‘s get technical and discuss how to actually extract it. There are two main approaches: 1) Coding your own scraper, or 2) Using a pre-built web scraping tool. We‘ll explore both options.
Option 1: Code Your Own Scraper
If you‘re comfortable with programming, you can write your own script to scrape Bing‘s search result pages. Python is the most popular language for web scraping due to its simple syntax and powerful libraries. Some of the key libraries you‘ll want to utilize include:
- Requests – Makes HTTP requests to fetch the HTML of search result pages
- Beautiful Soup – Parses the HTML to extract the desired data points
- LXML – Speeds up Beautiful Soup‘s parsing
- Pandas – Structures extracted data and converts it to other formats like CSV
Here‘s a basic Python script to scrape Bing results and export them to a CSV file:
import requests
from bs4 import BeautifulSoup
import pandas as pd
keyword = ‘scrape bing results‘
url = f‘https://www.bing.com/search?q={keyword}‘
headers = {
‘User-Agent‘: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3‘
}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, ‘lxml‘)
results = []
for result in soup.select(‘.b_algo‘):
title = result.select_one(‘.b_algo h2 a‘).text
link = result.select_one(‘.b_algo h2 a‘)[‘href‘]
snippet = result.select_one(‘.b_caption p‘).text
results.append({
‘Title‘: title,
‘Link‘: link,
‘Snippet‘: snippet
})
df = pd.DataFrame(results)
df.to_csv(f‘{keyword}.csv‘, index=False)This script searches Bing for "scrape bing results", parses the HTML using Beautiful Soup and CSS selectors, extracts the title, URL, and description snippet of each result, and saves the data to a CSV file.
Of course, this is a simplified version with no error handling, pagination, or ability to scale queries. More advanced scrapers would need to add functionality like:
- Accepting a list of keywords and iterating through them
- Paginating through results (Bing shows 10 at a time)
- Enabling searching by different countries/languages
- Randomizing user agent and other request headers
- Using proxies or the cloud to distribute requests
- Adding delays and retries when blocked
- Storing results in a database instead of CSV
- Extracting additional data points like publish date, number of ads, people also ask, etc.
You can find open source Bing scrapers on GitHub to use as a starting point. Or use a headless browser automation tool like Selenium to interact with the live search results and avoid some anti-bot countermeasures.
Option 2: Use a Web Scraping Tool
If you‘re non-technical or just want to save time, there are a number of pre-built web scraping tools and services that enable you to scrape Bing without having to write any code.
Some of the most popular options include:
- Octoparse
- Apify
- ScrapeStorm
- Mozenda
- ParseHub
- Scrapy Cloud
These tools provide a visual interface to configure your scraping jobs. You simply input your list of keywords, select the data fields you want to extract (title, link, snippet, etc), choose your output format, and schedule your scraping frequency.
Some, like Octoparse, even offer built-in templates specifically for scraping search engines like Bing. This enables you to extract structured data with just a few clicks.
The benefits of using a pre-built tool include:
- No coding required
- Easier to scale to large volumes of keywords
- Built-in functionality for pagination, exporting data, scheduling, etc.
- Avoids IP blocking by using proxies and the cloud
- Provides customer support
The downsides are that they cost money and provide less flexibility than writing your own code. But for most marketers, the time savings is well worth the price.
Scraping Best Practices
Whichever method you choose, there are some best practices to keep in mind when scraping Bing to avoid getting your IP address or account blocked:
Respect robots.txt: Bing‘s robots.txt file outlines the rules for what scrapers are allowed to crawl. Violating it can get you permanently banned. Most pre-built scraping tools have options to abide by robots.txt; make sure to enable this.
Slow down: Sending too many requests too quickly is an easy way to get rate limited or blocked. Add delays of a few seconds between each request and between each IP/proxy. Randomizing the delay a bit makes the traffic appear more human.
Randomize user agents: Bing can easily spot scraper traffic if all requests come from the same browser. Use a pool of user agents and rotate through them to blend in with other traffic.
Use proxies: Proxies enable you to distribute your requests across many IP addresses. Some scraping tools have this built-in. If coding yourself, you‘ll need to manage your own list of proxies. Look for proxies that are reputable and designed for scraping.
Set referrers: Most Bing scraper traffic has no referrer or comes directly from the Bing search page itself. To blend in, set your referrers to look like natural traffic sources for Bing searches, like a blog article or message forum.
What to Do With Scraped Bing Data
Once you‘ve scraped the data you need from Bing, the insights process begins. Read through your exported search results to spot patterns and trends, such as:
- Most common words in result titles and snippets
- Number of ads per keyword
- Types of sites that show up most (like news, ecommerce, blogs, etc)
- Length and structure of top ranking pages
- Percentage of video results
- Whether top results are more educational or commercial in nature
Some ideas to inspire your analysis:
Run the text of search result titles and snippets through a word cloud generator to visualize the most frequently occurring terms. Do they align with what you think searchers are looking for?
Bucket your keywords by search intent (informational, commercial, transactional, local) based on the types of pages that rank. Then align your content calendar to target the right stage of the funnel.
Evaluate the SERP composition for your top opportunity keywords. For example, if you see lots of video results, you may want to create a video to rank for that query. If it‘s mostly product pages, create a product page rather than a blog post.
Feed the scraped copy into an AI language model and have it generate titles and meta descriptions for you while keeping the top keywords, topics, and structure.
Use a sentiment analysis tool to gauge whether the ranking articles are more positive or negative in tone. Then slant your own content accordingly.
Calculate the average word count of the top 10 results for a keyword to get a sense of how long your content should be to compete.
Dive deeper into the full text of ranking articles to discover the topics covered, headers used, entities mentioned, and other on-page signals you should emulate.
There are dozens of other analyses you can perform with exported Bing data, from calculating keyword click-through rates to spotting regional search trends. The key is to slice and dice the data to uncover insights relevant to your business KPIs.
Visualization can help make scraped data more digestible for sharing with clients and colleagues. Consider creating graphs and charts showing ranking trends over time, search volume by keyword list, percentage of results with certain features, etc.
You can even feed your scraped data into machine learning models and enterprise SEO platforms for deeper analysis and recommendations.
The Future of Bing Scraping
As Bing grows in market share, so does the importance of mining its search data for marketing intelligence. By understanding what ranks in Bing and why, you can better optimize your content for visibility and traffic.
Bing is constantly evolving to provide more relevant results and thwart abuse by scrapers. As they introduce new SERP features and anti-bot measures, scrapers will need to keep pace and adopt the latest techniques and best practices.
The holy grail for Bing scraping is real-time data at massive scale. Low latency, high frequency scraping remains difficult and expensive, but advances in AI and cloud computing continue to push the boundaries of what‘s possible.
Expect to see more natural language processing and machine learning applied to analyze Bing data, as well as tighter integrations between scrapers and other marketing platforms. The easier it is to incorporate scraped data into existing workflows, the more valuable it becomes.
Bing may never overtake Google in popularity, but it remains a powerful source of search insights. By regularly scraping its results, you can stay one step ahead of your competition and make data-driven optimizations to your website and content. Just remember to follow best practices, stay within terms of service, and always put the end user first.