Content Gap Analysis Using Crawl for AI

In this video, I walk through the final version of the content gap analysis leveraging Crawl for AI, an open-source crawler hosted on Google Cloud Platform. I explain my transition from FireCrawl to this crawler due to its superior deep crawling capabilities suited for our project needs. I begin by discussing the setup of the crawler, running workflows, and testing a specific website with focused keywords from our project. The crawler successfully scans the base URL and compares the content against 17 clustered keywords, addressing prior issues with rate limiting by implementing wait times to ensure the workflow completes without interruption. As the crawl progresses, I detail how the data is organized in an Excel sheet, highlighting the clustering of keywords, their prioritization, commercial intent, and suggestions for landing pages. I emphasize the cost-effectiveness and efficiency of this crawler compared to FireCrawl, noting that while FireCrawl is swift, it lacks the deep crawling functionalities that my current work requires. I limit the crawl to a maximum of 10 pages and a depth of 2 to prevent server overload, leading to the successful crawling of all items. Finally, I present the gathered data and the complete workflow for assessing content gaps, culminating in the synthesis of new insights and recommendations for content strategies.