It doesn’t do any good to rank well in search results if no one clicks through.
If you go to Google Webmaster tools, and see the list of queries a page of yours might rank well for, you might see some query terms or phrases that you want to show up in search results. Webmaster Tools will show you how many “search impressions” your page might receive, as well as how many people have clicked on it when they have seen it. So what if your page has received 10,000 search impressions for that term or phrase, but only 50 clicks?
One question you should probably ask yourself is if the term or phrase is one that is satisfied by your page.
Sometimes a query term has more than one meaning, and most people searching for it might be looking for a different meaning. For example, you create a page about Java the drink, and most searchers may be looking for the programming language.
When you perform a search at Google, and you have a set of search results in front of you, how do you decide what to click upon? How do you judge the page titles, the snippets, and the URLs that you see. How does Google decide what to show you? A little more than a year ago, Google Webmaster Trends Analyst Pierre Far wrote on the Google Webmaster Central Blog a post titled Better page titles in search results. There he told us that Google might sometimes rewrite the titles for web pages when showing them in search results. The post told us that Google might do some changing of titles when those had generic titles such as “home”, or no title at all, or:
We use many signals to decide which title to show to users, primarily the <title> tag if the webmaster specified one. But for some pages, a single title might not be the best one to show for all queries, and so we have algorithms that generate alternative titles to make it easier for our users to recognize relevant pages.
Before we consider how Google might decide when and how to change page titles (in a follow up post to this one), there’s another question about search results that needs some exploration.
When someone searches at Google, their query might not express the informational or situational need that they have. It might be too broad, too ambiguous, or vague in some other manner. A well-formulated query instead might contain terms returing resources addressing the searcher’s intent, which might be measured by performance metrics. For vague queries, search results that satisfy a searcher’s need for information might not be highly ranked, and may not be presented on a first page of search results.
Well Performing Queries as Search Suggestions
A search engine may identify well-performing queries from queries entered by users. This is especially true in cases where many searchers have selected results from those queries. These well-performing queries may be suggested to searchers when similar queries are presented to a search engine.
My last post, Not All Anchor Text is Equal and other Co-Citation Observations, was a response to a White Board Friday video posted a couple of weeks ago at the SEOmoz Blog, Prediction: Anchor Text is Dying…And Will Be Replaced by Co-citation. I didn’t expect my next post (this one) to revisit that post and its observation that the way certain words might co-occur on pages might be a possible ranking signal that Google may be using.
Rand noted that first page rankings for three different pages, which didn’t seem very much optimized for the queries they were returned for, might be ranked based upon a ranking signal that looks at how words tend to co-occur on pages related to those queries. My post in response explored some reranking approaches by Google that also might account for those rankings, including Phrase Based Indexing, Google’s Reasonable Surfer Model, Named Entity Associations, Category associations involving categories assigned to queries and categories assigned to webpages, and Google’s use of synonyms in place of terms within queries.
Google’s Phrase-Based Indexing approach pays a lot of attention to words (phrases, actually) that appear together, or co-occur, in the top (10/100/1,000) search results for a query and may boost pages in rankings based upon that co-occurrence, and seemed like a possible reason why those pages might be appearing on the first page of results. The other reranking approaches that I included also seemed like they might be in part or in full responsible for the rankings as well. Then I found a patent granted to Google this week that seems like an even better fit.
Last Friday, in a well received and thoughtful White Board Friday at SEOmoz titled
Prediction: Anchor Text is Dying…And Will Be Replaced by Co-citation (title changed at SEOmoz) Prediction: Anchor Text is Weakening…And May Be Replaced by Co-Occurrence, Rand Fishkin described how some unusual Search Results caused him to question how Google was ranking some results.
I’m a big fan of looking at and trying to analyze and understand search results for specific queries, especially when they include results that appear somewhat puzzling, and I think those provide some great fodder for discussions about how Google might be ranking some search results. Thanks, Rand.
If I were to tell you that the major search engines have a bigger and richer database full of information than their index of the World Wide Web, would you believe me? Chances are that you’re one of the persons who helped build it. The information that Google and Bing and Yahoo collect about the searches and query sessions and clicks that searchers perform on the Web covers an incredible number of searches a day. When Google introduced their Knowledge Graph this past May, they gave us a hint of the scope and usage of this database:
For example, the information we show for Tom Cruise answers 37 percent of next queries that people ask about him. In fact, some of the most serendipitous discoveries I’ve made using the Knowledge Graph are through the magical “People also search for” feature.
When someone performs a search for a query that doesn’t produce much results at Google or Bing, the search engines might remove some of the query terms to provide more results, or they might look for synonyms that might help fill the same or a similar informational need. But chances are that such approaches still might not produce the kinds of results that searchers want to see.
Can the quality of links that your pages or videos or other documents link to influence the ranking of your pages, based upon a reachability score? A newly granted patent from Google describes how the search engine might look at linked documents and other resources reachable from a page or video or image to determine such a reachability score.
Search rankings might be promoted (boosted) or demoted in search results for a query based upon that reachability score calculated based upon a number of different factors.
Someone clicks on a search result, and while there they find links to other resources that they might click upon. Different user behaviors recorded by a search engine might be monitored to determine how people interact with the first, or primary resource visited, and similar user behavior signals may also be looked at for pages or videos or other resources linked to from that resource. Reachability scores might also be calculated for those secondary resources linked to from the first resource, looking at the third or tertiary pages and other resources linked to from the secondary resources.
Calculating reachability scores may follow a process like the following:
Imagine that a search engine might insert place markers into a web page, perhaps with the use of something like the new Google Tag Manager? These markers could enable a search engine to calculate how long it might take someone to read that page. A newly granted patent from Google describes why they might insert such markers (without really telling how how it might insert those), to determine the reading speed of a page.
The process described by the patent might try to understand how different features associated with a page might cause it to take less time or more time for a visitor to read a page. It would then use that understanding to predict how such features might influence the reading of other pages that don’t have markers inserted into them. These types of features could include language, layout, topic, and the length of text of those documents. These are all things that could affect traffic across the web or at specific websites.