Historically, one of the most quoted, used and abused SEO metrics was ranking. It is a cornerstone of reports that are sent to clients, one of the first things we go to check for clients’ sites and usually cause of alarmed phone calls when the ranking drops. Over the time, SEOs have learned to steer away from rankings as the major reported metric, although I have yet to find someone in the industry that doesn’t check them.
Another exercise that we like to engage in is predicting how much traffic ranking in the top 10 will bring to the website. It quickly became obvious that not every position in the top 10 brings the same amount of traffic. It is quite self-evident that the positions 1-3 will bring more traffic than positions 7-10 but how much more? Effort involved in pushing the site from #5 to #2 can be significantly larger than effort to bring the site from #50 to #10, so is the traffic/conversions worth the effort?
For this need, people have started investigating how the traffic divides across the top 10 locations. There were several methods used in these investigations, the two most prominent ones being eye-tracking and averaging clicks over locations data that comes from analytics or search engine/ISP (mistakenly) released data. I have reviewed a number of such studies and their predictions varied: the first 3 positions get in the neighbourhood of 60-80% of clicks, while the rest divides over the positions #4-#10. You can already see how the data in these studies shows a high degree of variability – 60%-80% is a pretty wide range.
All of that sounds well and good on the paper and the numbers can be transformed into pretty charts. More importantly we have numbers that we can translate into business goals and all clients like to hear that: “if we reach #3, we will get X visitors and if we put in more effort (AKA increase the SEO budget), we can get to the #1 position which will mean X+Y visitors, conversions, whatnot”. However, when one starts looking at the numbers out in the wild, the picture that emerges is significantly different. The numbers do anything but fall into precalculated percentage brackets and the division by percentages fluctuates so wildly that pretty soon, you find yourself tossing your predictions into the bin and all you can say is that improving your rankings will get you more traffic by an unknown factor. This is something that has not escaped the original researchers of CTR distribution: they are constantly trying to improve the accuracy of their findings by focusing the research on similar type of queries (informational, transactional, brand, etc.) or similar type of searchers, but there is a much greater number of parameters that influence the spread of clicks over different positions. I will try to outline a number of such parameters, as I encountered them with some of our own or our clients’ websites:
Universal search (local, shopping, images, video, etc.)
Introduction of different types of results in SERPs was Google’s attempt to cover as many user intentions as possible. This is particularly true of short search queries: when someone searches for [flowers] it is hard to tell whether they want pictures of flowers, instructions of how to care for flower pots, places to buy flowers or time-lapse videos of blooming flowers. Therefore, Google tries to include as many types of results as possible, hoping to provide something for everyone. iProspect’s study done in 2008, shows that 36%, 31% and 17% of users click on Image, News and Video results respectively, which means these results will seriously skew the CTR distribution over the SERPs. This is particularly true of queries with local intent where Google Places results are featured prominently in SERPs. Compare the following 3 SERPs (click to enlarge):
There is no chance that the #3 position gets the same percentage of clicks on each of these SERPs and the same holds true for every other position. Since serving of universal search results depends on geo-specific (IP geolocation) and personalized (location, search history) parameters, there is no way to accurately predict how the clicks will distribute over the different positions in SERPs
Different people mean different things when searching and shorter the query, wider the scope of possible intentions is. We can be pretty sure that someone searching for [online pizza order NY] is looking to order dinner tonight, but person searching for [pizza] may be searching for a recipe, in which case a pizza joint with online ordering website located at #1 will not get the recipe seeking clicks, which will either perform a second search or browse deeper into the SERP and on to 2nd, 3rd page of results. If the majority of searchers are looking for a recipe, then the #1 located pizza vendor will most definitely not get 40-50% of clicks. We have seen this happening with one of the clients that was ranked at #1 position for a single word keyphrase. Google Adwords Keyword tool predicts 110,000 exact match monthly searches in the US. During the month of May, while being ranked at #1 at Google.com for that keyword, the site received about 7,000 US Organic visits. That is 6.36% CTR on a #1 position. Google Webmaster Tools shows a 10% CTR on #1 position for that keyword. Compare that to 50.9% CTR that the eyetracking study from 2008 predicts for #1 position, which means site located at #1 should be receiving around 56K monthly visits and you can get a feel for the gap between the prediction and reality.
The problem with guessing intent of single query keywords is that it cannot be done before reaching any significant traffic creating positions and that task can be enormous. Low CTRs on the first position also put the whole perceived market value of single keyword exact match domains under question – the predicted amount of traffic that is expected from reaching top locations for the EMD keyword should take into account the difficulty to predict the intent of searchers.
One of the possible benefits of ranking for single-query keywords is appearance in SERPs even if your results are not clicked through. If your Title is unique and memorable, there are higher chances that people will click through it from a second search query SERPs if they have seen your site in the first query. For example, if you rank for [widgets] and your title says “The Cheapest Blue Widgets in the World!”, then there are higher chances of those visitors that perform a second search for [blue widgets] to click on your site, even if you are located in lower positions (granted that your title is not pushed below the fold by Local, Video, Image or other Onebox results). Yahoo has a nice keyword research tool that shows the most common previous and next queries after your keyword of choice (I think it is only relevant for US searches)
Brands outranking you
According to one of the CTR studies, dropping from #3 to #4 should cost you about 30% of your traffic (if the 3rd place gets 8.5% and 4th place gets 6.06% of total traffic), but is it always the case? Let’s see how drop from #3 to #4 and then back to #3 played on one of the SERPs:
The SERP for this keyword has no local results nor universal search (images, videos, news). Pay attention to the difference in numbers between the original #3 traffic and the later #3 traffic. The initial drop to #4 cost this site 76% of the original traffic (instead of 30% as the CTR studies predict) and the return to #3 got it back to about 47% of the traffic the site got when at #3 originally. So what happened? The site got outranked by a famous brand. Seeing the brand name in the title and and URL of the #1 listing, enticed searchers to click through that listing in much higher numbers than the 42% that the study predicts. Again, it is not only where you are ranked, it is also who is ranked with you that influences your CTR percentages. My guess is that even when a big , well-known brand doesn’t outrank you, but is located just below your listing, your CTR percentages will also be negatively affected. Seems like Google’s preference for brands is based in actual user behaviour. It would be interesting to see whether there are any significant differences in bounce rates from brand listings too.
There are many ways to make your listing on SERP more attractive to clicks. The linked article outlines the most important ones, however I would like to add some additional points to three of those tactics:
- Meta Description – In an eye tracking study, performed by a team including, among others, Google’s Laura Granka, (published in a book called Passive Eye Tracking, in a chapter Eye Monitoring in Online Search) it was found that the snippet captured the most attention of the searchers (43% of eye fixations), followed by the Title and the URL (30% and 21% of fixations respectively). The remaining 5% distributed over the other elements of the listing, such as Cached Page link. This finding shows the importance of controlling the content and appearance of your snippet for your main targeted keywords, as an eye-catching snippet can tip the scales of CTR in your favour and increase the number of clicks you get, relatively to your site’s ranking.
- Rich Snippets – the above figure of 43% of attention dedicated to the snipped probably becomes even higher when we talk about rich snippets. Google and other search engines have been investing a lot of efforts into encouraging adding a layer of classification onto the data presented on web pages and in case the information is labelled in an accepted way, it will be presented already in the snippet in SERPs. Check out the following SERP:
The showing of review grade stars in the snippet itself, makes this listing more attractive than the neighbors and it probably increases the CTR far beyond the few percent that the position #6 would get it, according to CTR studies. Similar situation can be observed with other microformatted data, such as event dates, recipes, airplane tickets, etc. Rich snippets will become even more widespread if and when Google starts accepting websites that implement the Schema.org tagging.
- Optimized Titles – adding a call to action to the Title or making it stand out in some other way, can increase the CTR percentages of a listing, beyond what you would usually get due to your ranking. This strategy, however, became less influential since Google started deciding what the best Title of your site should be, according to, among other things, anchor text of incoming links. As these changes will be implemented within Google’s discretion, it becomes very hard to predict how many people will click through to your site.
These are only some of the reasons that CTR % can differ greatly from the value predicted by the eyetracking or other studies. It is important to remember that as search engines try to index and incorporate into SERPs more and more types of results, it will be harder to predict the CTR over locations. Even averaging out the figures over a large number of results will give us a number that is not at all useful for predicting the traffic volume received from each position.
(Image courtesy of iamyung)