A deeper look at AI crawlers: breaking down traffic by purpose and industry
Search platforms historically crawled web sites with the implicit promise that, as the sites showed up in the results for relevant searches, they would send traffic on to those sites — in turn leading to ad revenue for the publisher. This model worked fairly well for several decades, with a whole industry emerging around optimizing content for optimal placement in search results. It led to higher click-through rates, more eyeballs for publishers, and, ideally, more ad revenue. However, the emergence of AI platforms over the last several years, and the incorporation of AI "overviews" into classic search platforms, has turned the model on its head. When users turn to these AI platforms with queries that used to go to search engines, they often won't click through to the original source site once an answer is provided — and that assumes that a link to the source is provided at all! No clickthrough, no eyeballs, and no ad revenue.
To provide a perspective on the scope of this problem, Radar launched crawl/refer ratios on July 1, based on traffic seen across our whole customer base. These ratios effectively compare the number of crawling requests for HTML pages from the crawler Continue reading