Many SEO experts agree: content freshness is a ranking factor. But can this be said across the board? Here are some thoughts on this topic.
When talking to customers and my PR colleagues, the topic of content freshness or the timeliness of published content as a ranking factor comes up again and again. Google has officially confirmed that freshness is a ranking factor and the monthly Google updates make it clear that this area is always the focus of the Search Quality Team and is being improved. That is why I want to put forward some thoughts on this topic for discussion in this article.
Basically, the following questions need to be asked and answered:
What is Freshness for Google in terms of content?
How does Google identify freshness in content?
For whom is current content important and for whom is it not important?
In what form does current content make sense?
Table of contents [ Hide ]
1 What is Freshness for Google in terms of content?
2 How does Google identify freshness in content?
3 For whom is current content important and for whom is it not important?
4 In what form is current content useful?
5 Conclusion on Freshness as a Ranking Factor
6 Further Reading Tips
What is Freshness for Google in terms of content?
When it comes to freshness, Google differentiates between topical azerbaijan cell phone number list topicality and simply new content (see also reading tips at the end of the article). Topical topicality relates to trends and news, while fresh in relation to new content has nothing to do with the topic of the content itself, but rather with the constant indexing of new content relating to a domain.
How does Google identify freshness in content?
First of all, the general question is what Google could consider to determine whether something is up to date. Here are some possibilities:
release date
date of initial indexing
Frequency of publications/number of newly indexed pages in comparison over time
Content for new seasonal search queries or subject areas with short-term high search volume (trending topics)
intensity of user feedback via social signals