Source: seoresearcher.com

What is the distribution of clicks on a search engine results page? What percentage of clicks gets each search result according to its rank? How much more users’ attention gets the first listing compared to the second? Or how often do users click the listing below the page fold? The way users interact with SERPs is one of the most frequently discussed topics in the SEO community and is also a very important field of study for the search engine specialists. To answer the above questions researchers employ the so-called eye tracking experiments.

Eye-Tracking Studies

The objective of eye tracking studies is gaining insight into how users browse the presented abstracts and select links to click. The results of eye tracking research provide Internet marketers with information on clickthrough rates, thus allowing them to make correct predictions on traffic changes as their rankings are gained or lost. For SE engineers the results provide a basis for improving the interfaces of search engines and metrics to evaluate the relevancy of the presented search results.

To detect users’ interaction patterns the eye tracking experiment observes a number of indicators of ocular behavior using a CCD (charged couple device) camera similar to the appliance used to read bar codes. The indices of ocular behavior include eye fixations, saccades, scan paths and pupil dilation. Eye fixations are defined as a stable gaze lasting for 200-300 milliseconds representing visual attention to a specific area of a SERP. Pupil dilations or pupil diameter changes represent a measurement of interest in a particular listing. This variable is especially important as it helps interpreting an implicit user feedback to the relevancy of the presented search results.

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Source: selfseo.com

on the keyword combination or a single keyword entered into the search box. It can be useful to find some targeted keywords that are often misspelled by people on the search engines, Ebay misspellings and so on. It supports three keyboard layouts for various countries: english (QWERTY), french (AZERTY) and german (QWERTZ).

Wrong key typos
Typos based on a user hitting the wrong key that is near the intended key (like "jello" instead of "hello"), only uses characters valid in ascii domain names.

Missed character typos
Typos based on a missed key (like "ello" instead of "hello").

Transposed character typos
Typos based on transposition errors (like "ehllo" instead of "hello").

Double character typos
Typos based on hitting an intended key twice (like "heello" instead of "hello").

Try this tool at selfseo.com

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By: David Harry | Source: site-reference.com

The main goal of this document is to give SEO enthusiasts a stronger grasp of how Phrasing is dealt with in Search Engines, in an effort to help you further target and optimize your web sites. The theories and information relate well to keyword/phrase research as well as content creation and to a lesser extent back links text development.

The crux of the piece was based on analysis of an existing Google Patent on ‘Phrase based searching’, (see Resources at the end). That is as far as I shall go on the original Patent since it can lead to assumptions of what may, or may not be used in their indexing and retrieval processes (algorithms). Just because they filed the patent, doesn’t necessarily mean they have implemented it. I feel the main point here is to get a better idea of HOW search engineers think and WHAT may possibly be in place now, or in future Search technologies.

Why Phrase based indexing and retrieval is important

The problem facing search engines is that the direct "Boolean" matching of query terms is known to have its limitations. One problem is that it doesn’t identify documents that do not have the query terms, but have related words. IT is a very tightly defined result. A search on "Florida Snakes" doesn’t return results related to local species, (Black Pine for example) Conversely it is likely to also retrieve and highly rank documents related to ‘Florida’ rather then the desired or intended query.

Creating better clusters

The answer is a methodology that uses phrases to index, search, rank, and create descriptions for websites. It looks to identify phrases that have frequent and/or distinguished/unique usage. Using this methodology phrases of four, five, or more terms, can be identified. To establish a ‘predictive measure’ the system can identify phrases that are related to one another. A prediction measure is used that relates the actual usage to an ‘expected usage’ of the two phrases. In essence, the more ‘expected’ related phrasings there are within a document, the higher the score will be.

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