A relatively new type of tool analyzes the search engine results pages (SERPs) and provides recommendations based on a statistical analysis of similarities shared between the top-ranked sites, but some in the search community have their doubts. This is called Search Engine Results Page (SERP) Correlation Analysis. SERP analysis is research that analyzes Google search results to identify factors in ranked web pages.
However, the SEO community has found interesting correlations in the past by studying search results. One such analysis discovered that top-ranked sites tend to have Facebook pages with a lot of likes, for instance. But did they really rank because of the likes?
The author says no, and adds that just because the top-ranked sites share certain features does not mean that those features caused them to rank better. It's that lack of actual cause between the factors in common and the actual reasons why those sites are top-ranked can be seen as a problem. Just because web pages ranked in the search results share a word count, a keyword density or share keywords doesn't mean that those word counts, keyword densities, and keywords are causing those pages to rank.
Ten Blue Links
An additional problem with analyzing the top ten of the search results is that the search results are no longer just a list of ten ranked web pages. Bill Slawski is from GoFishDigital:
|"The data in correlation studies may be cleaned so that One Boxes and Featured Snippets don't appear within them, but it's been a long time since we lived in a world of ten blue links."|