10 from 40 – My Picks from The Google Search Quality Highlights

This week Google announced over 40 updates and improvements to search, the largest update since they have been releasing updates monthly. Of course the two big take-aways include the latest iteration of Panda and the vague announcement that one of the link signals that Google has been using for several years has been dropped. There is a lot of speculation on that one but it is doubtful that the link signal that was dropped was a very strong one to begin with. Also, as with any update, new changes in spam protection.

While most SEO’s are focusing on the big changes, I think there are quite a few hidden gems in this announcement, especially if you are concerned with local SEO, keyword diversification, or freshness.

More coverage for related searches. [launch codename “Fuzhou”] This launch brings in a new data source to help generate the “Searches related to” section, increasing coverage significantly so the feature will appear for more queries. This section contains search queries that can help you refine what you’re searching for.

Data refresh for related searches signal. [launch codename “Chicago”, project codename “Related Search”] One of the many signals we look at to generate the “Searches related to” section is the queries users type in succession. If users very often search for [apple] right after [banana], that’s a sign the two might be related. This update refreshes the model we use to generate these refinements, leading to more relevant queries to try.

These are both significant for anyone doing keyword research. The big question for the coverage change; what data source is being brought into play? Maybe something dealing with Google + and related searches? Who knows. Since we no longer get keyword data from logged in users, any additional indicators for trending keywords is welcome. Hopefully, related searches will be more accurate and refreshed more frequently.

“Site:” query update [launch codename “Semicolon”, project codename “Dice”] This change improves the ranking for queries using the “site:” operator by increasing the diversity of results.

I use the “Site:” query daily so any improvements are welcome. I hope that this removes duplicate pages that are common to blog queries that include “tag” and “category” pages.

Disabling two old fresh query classifiers. [launch codename “Mango”, project codename “Freshness”] As search evolves and new signals and classifiers are applied to rank search results, sometimes old algorithms get outdated. This improvement disables two old classifiers related to query freshness.

Improvements to freshness. [launch codename “iotfreshweb”, project codename “Freshness”] We’ve applied new signals which help us surface fresh content in our results even more quickly than before.

Fresher images. [launch codename “tumeric”] We’ve adjusted our signals for surfacing fresh images. Now we can more often surface fresh images when they appear on the web.

Consolidation of signals for spiking topics. [launch codename “news deserving score”, project codename “Freshness”] We use a number of signals to detect when a new topic is spiking in popularity. This change consolidates some of the signals so we can rely on signals we can compute in realtime, rather than signals that need to be processed offline. This eliminates redundancy in our systems and helps to ensure we can continue to detect spiking topics as quickly as possible.

The freshness algo seems to be getting a lot of updates this time around. It would be interesting to know what freshness signals are now outdated. Pretty good bet that social signals may make older methods obsolete.

Improvements to ranking for local search results. [launch codename “Venice”] This improvement improves the triggering of Local Universal results by relying more on the ranking of our main search results as a signal.

Improved local results. We launched a new system to find results from a user’s city more reliably. Now we’re better able to detect when both queries and documents are local to the user.

More locally relevant predictions in YouTube. [project codename “Suggest”] We’ve improved the ranking for predictions in YouTube to provide more locally relevant queries. For example, for the query [lady gaga in ] performed on the US version of YouTube, we might predict [lady gaga in times square], but for the same search performed on the Indian version of YouTube, we might predict [lady gaga in India].

National brands dealing with skewed local results will welcome the first change. Not sure how the localized docs improvement will work. Will have to keep a look out for those results. Have you started localizing your YouTube videos? No time like the present. Be sure to utilize the map function when uploading your videos if you want Google to recognize them as localized.

Which of these improvements will have the most effect on your day to day SEO or marketing activities?