On a hotel booking website, we might create clusters for “landing pages,” “individual hotel pages,” and “category pages.” After a core update, we might notice that “landing pages” have lost traffic while “individual hotel pages” have improv. This kind of detail analysis would allow us. To better understand which areas of the site have been help or penaliz by the update and adjust our strategy accordingly.
To understand the effect of propos optimizations or perform A/B SEO testing
In our SEO experiments, such as A/B testing, we create specific clusters to separate URLs that have been modifi from those that remain unchang. This helps us observe the overall effect of the optimizations perform and make zalo database data-driven decisions.
Consider a car classifis site that makes
Changes to its vehicle detail pages to improve conversion rates. By clustering these pages into “optimiz pages” and “non-optimiz pages,” we could accurately measure the impact of the improvements by seeing whether the optimiz pages show غلبه بر مخالفت ها an increase in conversions compar to the control group.
To better understand the effects of link building
In our link building strategies, clustering allows us. S to separate URLs into different categories: those that are in the campaign. The campaign, and those that are isolat from it. This gives us a clearer view of the value of the links creat and their impact on the overall SEO of the site.
A practical case would be a link building
Campaign for a technology blog. By clustering the URLs into “promot articles”, “relat articles” and “other articles”, we could see how external links are tg data affecting the performance of each group. If we observe that the “promot articles” are increasing in visibility and traffic, while the “other articles” are not, we could infer that the campaign is achieving its goal of ranking the promot content higher.
