Submit the same SERP
Posted: Wed Feb 12, 2025 5:15 am
Record if the anomaly has been corrected (i.e.: position 3 is now ahead of position 2)
Iterate over ten thousand keywords and check various factors (backlinks, social shares, etc.).
So what are the advantages of this method? By looking at the change over time, we can see whether the ranking factor (related) is a leading or lagging feature. A lagging feature can automatically be ruled out as a cause because it occurs after the change in ranking. A leading factor has the potential to be a cause even though it may still be spurious due to other reasons.
We collect the search result. We record where the search result deviates from the expected predictions for a particular variable (like links or social shares). We then collect the same search result 2 weeks later to see if the search engine has corrected the unordered results.
Following this methodology, we tested 3 different uruguay number data correlations developed from the study of ranking factors: Facebook shares, number of root linking domains, and page authority. The first step involved collecting 10,000 SERPs from randomly selected keywords in our Keyword Explorer corpus. We then recorded the Facebook shares, root linking domains, and page authority for each URL. We noted every instance where 2 adjacent URLs (such as positions 2 and 3 or 7 and 8) were flipped with respect to the expected order predicted by the corresponding factor. For example, if position #2 had 30 shares while position #3 had 50 shares, we noted that pair. You would expect the page with the most shares to outperform the page with the fewest shares. Finally, 2 weeks later, we captured the same SERPs and identified the percentage that Google reordered pairs of URLs to match the expected correlation. We also randomly selected pairs of URLs to get a baseline percentage probability that any 2 adjacent URLs would change positions. These were the results...
Conclusion
Iterate over ten thousand keywords and check various factors (backlinks, social shares, etc.).
So what are the advantages of this method? By looking at the change over time, we can see whether the ranking factor (related) is a leading or lagging feature. A lagging feature can automatically be ruled out as a cause because it occurs after the change in ranking. A leading factor has the potential to be a cause even though it may still be spurious due to other reasons.
We collect the search result. We record where the search result deviates from the expected predictions for a particular variable (like links or social shares). We then collect the same search result 2 weeks later to see if the search engine has corrected the unordered results.
Following this methodology, we tested 3 different uruguay number data correlations developed from the study of ranking factors: Facebook shares, number of root linking domains, and page authority. The first step involved collecting 10,000 SERPs from randomly selected keywords in our Keyword Explorer corpus. We then recorded the Facebook shares, root linking domains, and page authority for each URL. We noted every instance where 2 adjacent URLs (such as positions 2 and 3 or 7 and 8) were flipped with respect to the expected order predicted by the corresponding factor. For example, if position #2 had 30 shares while position #3 had 50 shares, we noted that pair. You would expect the page with the most shares to outperform the page with the fewest shares. Finally, 2 weeks later, we captured the same SERPs and identified the percentage that Google reordered pairs of URLs to match the expected correlation. We also randomly selected pairs of URLs to get a baseline percentage probability that any 2 adjacent URLs would change positions. These were the results...
Conclusion