Tracking AI Bots on Your Site with Log File Analysis

Consumer search behavior is changing before our eyes. In traditional search, a keyword or long-tail question prompts a list of website links to sort through. Now, generative AI-powered answers in search provide personalized, satisfying results that summarize site content in an ongoing conversation with the consumer. 

These AI-powered search results are becoming the rule, appearing alongside traditional results in Google and Bing search, in GenAI platforms like Perplexity and ChatGPT Search, and will integrate with Apple devices as well via Apple Intelligence.

How are generative responses created? What sources do they use to supply their answers? And how can brands control what appears there?

Both traditional and AI-powered search platforms use website crawlers to find, parse, and reference content. Understanding how those crawlers interact with your website is critical if you want your brand to appear in answers anywhere and everywhere consumers search.

That brings up a few questions:

• How do you know whether a search bot is visiting your website?
• Which content is it finding?
• What’s the crawler using the content for?
• How can you act on this data?

Although data on AI search bot behavior is scarce, there’s one source of truth: your website’s log files. Log file analysis can offer insights into the nuanced ways AI crawlers find and explore your content. Using the data within your log files, you can analyze AI crawler activity and fine-tune your optimization strategies.

What is log file analysis and how does it relate to AI in search?

Log files are the digital footprints left behind by every website visitor, whether human or bot. As first-party data that you own, you already have access to your log files; that data is accessible via your content delivery network (CDN). These logs provide a wealth of information about each website request, including:

• The time it occurred
• The specific URL requested
• The user's IP address
• The user agent responsible for the request

They help you see which parts of your site are being crawled, how often they’re visited, and whether there are any obstacles preventing efficient crawling. 

You can examine your log files manually by downloading the data and parsing it in a spreadsheet, or you can automate it via various solutions. 

Manual log file analysis

It’s always possible to perform log file analysis manually, but there are serious drawbacks that limit the return on your investment.

Manually analyzing your log files requires downloading your data (often from disparate sources that need to be centralized), organizing it, analyzing it to identify patterns, monitoring it to pinpoint any changes that could indicate a problem (such as an unnatural traffic increase), and of course reporting on the findings. This is difficult enough to do for small websites given the vast amount of data stored in log files, but it’s prohibitive, error-prone, and almost impossible to do at scale for large sites.

Manual log file analysis won’t get easier with time. There are millions of bots of all kinds crawling the web, and in 2022, 47% of all internet traffic was driven by bots. That was before AI crawlers entered the scene — your data will only get more noisy.

Automated log file analysis

There are solutions that automate the process, such as LogAnalyzer within Botify Analytics. Whether it’s automating data collection, identifying areas for optimization, tracking the impact of changes, or ensuring that your site is accessible and efficient for both users and bots, LogAnalyzer keeps you competitive in an AI-first search landscape:

• Automation, monitoring, and alerts: Automating the log file collection process can save time, reduce the risk of errors, and ensure consistency in data monitoring. LogAnalyzer continuously analyzes incoming log data and alerts you to significant changes or issues, allowing you to respond quickly to any potential problems.
• Data retention: Long-term data storage allows for historical analysis. Botify keeps your log data for up to 18 months, helping you track trends and changes over time. This historical perspective is invaluable for understanding how your site’s performance and bot interaction have evolved, and for making informed decisions about future optimizations.
• Data-driven insights: Botify Analytics aggregates data from a variety of sources while providing deep, actionable insights into your site’s performance, letting you focus on interpreting the results rather than getting bogged down in data processing.
• Data visualization: Rather than sifting through lines of data in your log files (timestamps, user agents, and status codes), you can visualize bot activity across your website. In LogAnalyzer, you can drill down into URLs crawled and visits by each search engine bot that you’re tracking. These charts help you easily interpret trends and identify anomalies in bot visits.

As GenAI answer platforms continue to grow, understanding crawler behavior on your website becomes even more important. After all, being crawled and indexed by these bots is how your content is found and shown to consumers. 

Beyond the traditional search engine crawlers, you can now track the following AI bots via your log files in Botify Analytics:

• Open AI:
  • ChatGPT-User
  • OAI-SearchBot
  • GPTbot
• Other AI bots:
  • Bytespider
  • ClaudeBot (by Anthropic)
  • Claude-Web (by Anthropic)
  • Anthropic-ai (by Anthropic)
  • CCBot
  • PerplexityBot
  • Amazonbot
  • FacebookBot
  • Meta-ExternalAgent
  • YouBot

Analyzing log files isn’t just a technical exercise — it’s a strategic one. By understanding bot behavior, you can identify potential performance issues, optimize your site for better efficiency, and improve its visibility on search and answer engines. 

Botify's advanced monitoring capabilities transform this analysis into actionable insights, showing you precisely how AI bots and traditional search engines interact with your website. Our analytics platform helps teams make informed technical decisions by understanding crawler behaviors across established search providers and newer AI platforms, demonstrating clear impact on your digital properties and search visibility.

“If your content isn’t being crawled, it won’t get indexed, and it won’t be used to train the AI models. Analyzing the raw data in the log files for search and AI bot requests is the first step to understanding if your content has a chance to rank in traditional search results, or be cited in AI summaries.”
‍Tim Resnik, VP Professional Services, Botify

How to use log files to find issues & opportunities

Insights into bot behavior

Which pages are crawlers visiting most often? Which pages are they ignoring? For instance, if certain pages are being crawled more often, they might be seen as more important by search engines or AI crawlers. Conversely, pages that are rarely crawled aren’t being prioritized for a reason. This is an opportunity to ask investigative questions:

• Is the content that isn’t being crawled important or valuable?
• What technical obstacles could be impeding site crawlers? Think internal linking, issues with rendering, robots.txt errors, and more.
• What content updates could result in increased crawl frequency?

Identifying performance issues 

If bots are encountering errors or dead ends, your site might have underlying issues with its architecture. Regular log file analysis allows you to catch these problems early, ensuring that both human users and bots have a smooth experience on your site.

To make sure bots are effectively crawling and indexing your pages, Botify Analytics customers can set up a custom alert in AlertPanel. This allows you to monitor any increases in non-indexable pages. 

Enhancing site visibility

Knowing which content is being crawled and indexed better aligns your optimization strategy with both traditional and AI-driven bot behavior. This could involve optimizing specific pages, improving your site’s navigation, or altering your content strategy to improve indexability.

How AI crawlers work in search

There are currently two kinds of crawler bots influencing generative results:

1. Bots that scrape and index the web for search engines like Google and Bing: Traditional search engine crawlers index data to refine search results, aiming to improve the relevance and accuracy of what users see. To be included in a web index and make sure your website is found, you need to make sure the bots that index content are finding your most valuable pages.
2. Bots that scrape the web and use that data to improve their large language models (LLMs) or provide up-to-date responses and links: AI platforms train their models with the data they scrape when crawling the web. They can also tap into a search index like Bing's to access and share the most recent, relevant content with searchers.

To be mentioned in the conversational and generative answers chatbots and AI platforms provide, the AI model needs to train itself on your content, because it can't discuss anything it hasn’t learned about within its training data. This makes it difficult for AI models to provide fresh, timely answers (think in-stock inventory or recent news stories), so they often work in conjunction with an index to reference content in responses. For example, when you enter a query on ChatGPT, it will ping Bing's index to get the most relevant recent search results before summarizing those results using its LLM. Other generative engines use a combination of Bing’s index and their own index.

While all crawler bots use the same methods to find and explore your website, AI-powered crawlers like GPTBot differ from traditional search engine crawlers in several key ways. Understanding these differences is essential for adapting your website optimization strategies to the new era of AI-driven search.

Content comprehension

Traditional search engine crawlers, such as Googlebot or Bingbot, primarily focus on indexing text, links, and metadata. They scan and store information about a page's content, structure, and relevance based on keywords and HTML tags. 

AI crawlers, on the other hand, go further. They use natural language processing (NLP) and machine learning to understand the context, intent, and nuances of the content. Because an AI model can only reference the data it knows about, it’s important to make sure AI crawlers are finding the right content to learn about your brand and products. If they don’t know about you, they can’t reference you in generative conversations with consumers.

Applications

Search engine crawlers use the data they index to improve search results, but AI crawlers have a range of end goals. As an example, OpenAI uses the information it gathers from GPTBot to train their large language models, the information from ChatGPT-User to provide fresh content and linked citations, and the information from their OAI-SearchBot “is used to link to and surface websites in search results in the SearchGPT prototype,” indicating a move towards more traditional search behavior. Meanwhile, Google recently launched an AI crawler of its own that “crawls on behalf of commercial clients of their Vertex AI product.”

As these crawlers become more sophisticated, the lines will blur between content creation, comprehension, and personalization. Brands should prepare for the following trends:

• Increased personalization: Search engines using AI crawlers have the capability to provide even more personalized search results, tailoring results to individual preferences and behaviors in real time.
• Greater emphasis on structured data: As AI crawlers become better at understanding complex data, the use of structured data will likely become even more important for ensuring that content is easily understood and indexed by AI systems.
• Ethical considerations: There’s already increased scrutiny on how LLMs interact with content, particularly in terms of content ownership, data privacy, and the ethical implications of AI-driven indexing and content generation. How AI tools use and cite the data they get from crawlers will be important to watch, especially as integrations like AI Overviews become more sophisticated.  

To stay ahead of these trends, you need to proactively optimize your site and focus on experimentation, adaptation, and understanding the evolving capabilities of AI.

Analyzing how crawlers move through your site

You can isolate the behavior of specific bot interactions from the rest by filtering your logs based on the user agents associated with specific bots. This allows you to see not just how often your site is visited by these bots, but also how they interact with your content, which helps you plan experiments and optimizations. 

Crawl patterns

Crawlers often follow specific patterns when indexing your site. Some may use a depth-first search approach, diving deep into your site’s architecture before moving to another section. Others might prefer a breadth-first approach, exploring wide across the top levels of your site before drilling down. By identifying these patterns, you can ensure that your most important content is accessible regardless of the crawler’s methodology.

Crawler efficiency

Analyzing how efficiently crawlers move through your site can reveal potential issues with your site’s architecture or linking structure. For example, if a crawler is repeatedly hitting the same pages or failing to reach deeper content, it might indicate problems such as poor internal linking, overly complex navigation, or broken links. 

To optimize your site for crawlers, focus on creating a logical and clean site structure. This includes using descriptive URLs, maintaining a consistent linking strategy, and ensuring that important content is easily accessible from multiple points within your site. These practices not only improve crawler efficiency, but also enhance user experience, making your site more navigable for everyone.

Remember, it’s important to strike a balance. Focus on maintaining a flexible and scalable site structure that can adapt as AI technologies continue to evolve, rather than making sweeping changes based solely on bot behavior. 

Understanding what crawler bots find on your site

It’s not enough to optimize for bot patterns — you also need to understand what they’re finding. Your analysis will depend on your website goals, but in general, strategies involve segmenting the URLs being crawled, visualizing the data to identify trends, and conducting overlap analysis to ensure that what the crawlers are seeing actually matches your intended site structure:

1. Segmentation: By classifying and flagging URLs requested by bots, you can see which sections of your site are getting the most attention, or if any sections are being ignored. This analysis can help highlight valuable content and problem areas, and allow you to focus your optimization efforts on specific, strategic areas of your site.
2. Visualization: Visualization techniques, such as heat maps and graphs, are great tools for tracking crawl volume and frequency. Visualization can make it easier to identify patterns in bot behavior and crawl frequency. 
3. Overlap analysis: Comparing the bot’s view of your site with your actual site structure can reveal discrepancies that might otherwise go unnoticed. For instance, you might discover orphaned pages that aren’t being crawled at all, or find that certain high-value pages aren’t being crawled as often as they should be. 

Controlling bot behavior

The idea of more bots hitting a website isn’t always a welcome one. There are valid concerns that go hand-in-hand with increased bot traffic:

• Is my content or data being scraped? 
• Is it being used ethically or in the brand’s best interests?
• Could increased traffic be a sign of fraudulent activity or a security threat (such as a DDoS attack)?
• Will more bot traffic slow down my website?
• What’s the associated cost and increased server load of more bot traffic?

Log file analysis helps you identify the specific bots visiting your website, the behaviors they employ, the content they’re accessing (and how often), and the actions they take. It’s the best way to separate malicious activity from harmless crawler traffic, and can give you insight into which specific user agents you want to allow and which you want to block (via methods like your robots.txt file, rate limiting, and more.)

Use log file analysis to optimize for AI crawlers

Start analyzing your log files today to understand AI bot behavior on your website and optimize it for the new world of AI-driven search. By staying proactive and leveraging the power of tools like LogAnalyzer within Botify Analytics, you can ensure that your website remains competitive and optimized in an ever-evolving search landscape.