TIPS and TRICKS
Click popularity
Click popularity is a ranking system that is used by some search engines. In plain English, it means tracking the behavior of users and then using the information to determine which pages they found to be relevant to their queries. In order to explain the logic behind this system and demonstrate how it works, let's take an example.
If you do a search for "information on website promotion", we can safely assume that you want to visit sites that offer what you are searching for - information on website promotion. So, when you see the search results, you'll click on the ones that you believe to be relevant to your query and will ignore the others.
Should you happen to visit a page that seemed promising judging by the search results but in reality doesn't give you what you want, you are likely to use your browser's back button to return to the search results and continue your search. On the other hand, if you click through to a page and do not return, you probably found what you were looking for.
After the search engine has collected enough of data about the way users react to the search results they have been shown, the ranking of the pages can be adjusted accordingly. Let's say that our "information on website promotion" query brings up five pages, A, B, C, D and E. After one hundred people have executed a search using that keyphrase, the search engine can use the data it has gathered to create the following statistic:
| Name of page | Number of clicks | Number of returns |
| A | 13 | 5 |
| B | 2 | 0 |
| C | 38 | 9 |
| D | 6 | 2 |
| E | 41 | 39 |
Now, even while we have no idea what these pages are about, we can make an educated guess about how relevant they are to the keyphrase "information on website promotion" just by looking at the above figures. At first, it might seem as page E provides the best response to the query, since most people are clicking on its listing.
However, an overwhelming majority of those who visited page E from the search results returned to continue their search soon afterwards. Page C got almost as many clicks and appears to provide satisfactory information as only nine people of the 38 that visited it returned to the search engine. Pages A and D both seemed to interest a part of the searchers, while page B only received two clicks.
Based on this data, it can be concluded that page C is the best match for the query. Page A is second best, D comes third and B fourth. Page E might require a human from the search engine to take a look at it, as it gets a lot of clicks but isn't able to retain the interest of visitors once they arrive to the page. There are many reasons that could cause such a situation, but a good guess is that page E is no longer accessible.
We can now rearrange the search results based on our findings and offer more relevant results to the next people who use this keyphrase. The example you just read should give you a good idea on what click popularity is and what it does. In reality, the process can be and often is more complicated, but the basic model is the same we used above.
Why isn't everyone using click popularity?
Although it might seem to be a near-perfect system, click popularity has its share of problems that have limited its usefulness in search engine algorithms. For one, sometimes people behave strangely and click on results that have nothing to do with what they have searched for. This can be partly dealt with by using other methods to evaluate the relevancy of the page in addition to click popularity, but may still occasionally cause the search engine to display very odd search results.
Another, perhaps the most difficult issue of all, is that click popularity really measures how relevant the pages appear to be based on the information in their search listings instead of measuring the relevancy of the pages themselves. After the user has left the search engine, it is difficult to tell whether the site he selected from the search results really was what he was looking for.
Tracking whether the user comes back to the search engine promptly after leaving can be used as an indicator of how satisfied the user was, but it doesn't produce very accurate results. Sites can do various things to prevent users from easily returning to the search engine, such as using JavaScript to disable the browser's Back button.
On the other hand, in some cases a prompt return to the search engine might not mean that the user did not find the answer to his query. Another possible explanation is that he was simply trying to check out something quickly (such as the score of last night's football game) before continuing to search for information on other topics.
Finally, if the weight of click popularity in the search engine's algorithm is too high, it can cause the search results to become stale. Because very few searchers go beyond the third page of search results, sites that aren't in the top thirty don't get clicked too often even if they are really good. As new pages that have just been added to the index don't have any click popularity yet, their ranking won't be too high in the beginning.
This can easily start a vicious circle, because when a page is ranked poorly it won't get clicked on and when it doesn't get clicked on, its ranking won't rise. It is possible for the search engine to improve the situation by tweaking the click popularity algorithm so that it gives a larger boost per click to sites that have a poor ranking, but even that hasn't been able to completely level the playing field between new and old pages.
The future of click popularity
At the moment, click popularity is heavily used only by Direct Hit. Yahoo, Lycos and AltaVista also seem to be utilizing it to some degree, but its role in their algorithms is not nearly as important as it is in Direct Hit's. Some other major engines, such as Google, have not embraced the technology. The question is, will Google eventually add click popularity into its algorithm?
If you want my opinion, I believe that eventually all major engines will be using click popularity. However, due to the problems associated with it, I think that it will be used to supplement the current ranking algorithms instead of becoming the biggest factor in determining the ranking of your site. So, it is definitely something you should be aware of, but its importance should not be over-emphasised.
