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  1. 1. NetBooster UK organic click curve analysis October 2014 One click curve to rule them all
  2. 2. 2 One click curve to rule them all NetBooster UK organic click curve analysis Contents Why should we care about click through rates? 3 What are the objectives of this study? The influence of changing search behaviour on CTR 5 Search is becoming more complex Previous research 8 Slingshot study Catalyst study Why should you care about NetBooster’s study? 10 Methodology 12 Data Acquisition Method Data Processing Questions Observations How does our study compare against others? A new click curve to rule them all 22 Non-brand click curve for positions 1-30 in organic search What about positions 11-30? The influence of brand bias on CTR What about the long tail? Mobile CTR How can you optimise your CTR? 30 Page Titles Meta Descriptions Rich Snippets (Schema) URL Optimisation What are the practical uses for this data? 33 Forecasting Benchmarking Creating your own click curve Conclusion 36 What could be the next steps?
  3. 3. Why should we care about click through rates?
  4. 4. 4 One click curve to rule them all Why should we care about click through rates? Understanding how people interact with Google and other search engines will always be important. Organic click curves show how many clicks you might expect from search engine results and are one way of us evaluating the impact of our campaigns, forecasting performance and exploring changing search behaviour. With this study, we’ll explore how the natural search landscape is evolving and how the increased visualisation of Google is influencing CTR in 2014. Our CTR curve is built on the largest ever dataset derived from Google Webmaster Tools and is based entirely on UK search queries. We go beyond the top 10 to demonstrate the value in lower positions and give a basis for evaluating campaigns and the impact of movement in result pages. It’s not just about going big: we wanted to be analytical, accurate and above all useful. We’ve segmented data by brand, long tail and short tail, meaning SEO professionals can hone in on opportunities in natural search for their brand. What are the objectives of this study? The objectives of this study are: Search behaviour is evolving rapidly and it’s vital as SEO professionals that we have up-to-date research that helps us understand the opportunities in natural search • Create a new click curve for the top 10 and beyond (including positions 11-30) • Compare this against previous CTR studies • Investigate the impact of brand bias on CTR (especially in lower positions) • Explore what this curve looks like for mobile and long tail searches • Create a methodology which will allow publishers to create custom click curves based on their own data and benchmark our curve against their own
  5. 5. The influence of changing search behaviour on CTR
  6. 6. 6 One click curve to rule them all The influence of changing search behaviour on CTR Search behaviour is fundamentally changing as users become more savvy and increasingly familiar with search technology, and engage in multi-tabbed browsing across multiple devices. Google’s results have also changed significantly over the last decade going from a simple page of 10 blue links to a much richer layout including videos, images, shopping ads and the innovative knowledge graph. In fact Google search timeline below highlights just how far Google have come in the last 15 years. This visualisation of search and Google’s almost perpetual beta testing of its UI has had a significant impact on how users interact with publishers and brands as well as how they start their journey. Ultimately this has influenced click through rates (CTR) in natural search and how much traffic publishers receive as a result.
  7. 7. 7 One click curve to rule them all Above: Google back in 1998; Right: an example of how Google looks now. Search is becoming more complex We also know that there are an increasing amount of touchpoints in a customer journey involving different channels and devices. Google’s zero moment of truth theory, which describes a revolution in the way consumers search for information online, supports this idea and predicts that we can expect the number of times natural search is involved on the path to conversion to get higher and higher. Using search query data from Google UK for a wide range of leading brands based on millions of impressions and clicks, we can gain insights into the how CTR in natural search has evolved beyond those shown in previous studies by Catalyst, Slingshot and AOL. The influence of changing search behaviour on CTR
  8. 8. Previous research
  9. 9. 9 One click curve to rule them all Previous research CTR studies are not new and have been around since the leaked AOL data back in 2006. Since then we have seen several studies from a number of sources that have given us some valuable insights into the changing landscape. Here are a few of the more notable studies. Slingshot study The Slingshot study from 2011 has been referenced many times in the digital community but only investigated the CTR for the top 10 and showed a very clear drop in CTR beyond the top 3. The sample size for the Slingshot study was based on 324 keywords, with 30 keywords in each of the top 10 ranking positions. The data used generated a total of 170,000 actual user visits over a 6 month period. The Slingshot study was also based on Authority Labs ranking data: clickthrough-rates Catalyst study This study by Catalyst is the most recent and uses a similar methodology to our own, which uses average position data from Google webmaster tools. Their study showed a similar curve to Slingshot with a slightly higher CTR for positions 3, 4 & 5. Along with previous studies this showed a very low CTR for positions 6-10. The Catalyst study was also one of the largest studies to date with over 17,500 unique search queries analysed. click-through-rates Honorable mentions also go to studies from Chikita, Optify and Enquiro which have also investigated this subject.
  10. 10. Why should you care about NetBooster’s study?
  11. 11. 11 One click curve to rule them all Why should you care about NetBooster’s study? We wanted to make sure the size and depth of this study was bigger than anything else we had seen before and this study is certainly the largest dataset derived from Google webmaster tools. The study is based entirely on UK top search query data and has been refined by day in order to give us the most accurate sample size possible. This helped us reduce anomalies in the data and achieve the most reliable click curve possible. This study includes: Due to the increase in sample size, we’ve been able to accurately investigate CTR activity beyond the first page. Our click curves extend beyond the traditional top 10 and also show CTR for mobile, video and image results so we can better understand how people are discovering content in natural search across multiple devices and channels. (sites with a total of 1m+ branded keyword impressions) 65MCLICKS 311MIMPRESSIONS 45 11UNIQUE BRANDS HOUSEHOLD BRANDS DATA FOR RETAIL, TRAVEL & FINANCE VERTICALS 1.2MUNIQUE SEARCH QUERIES B IG B R A N D S
  12. 12. Methodology
  13. 13. 13 One click curve to rule them all Methodology Our methodology involved a thorough approach to building both a robust dataset and reliable process. Data acquisition Google Webmaster Tools was chosen as the source of keyword data. Although GWT doesn’t provide a complete listing of all your keyword data, it remains one of the most direct sources of Google search data.
  14. 14. 14 One click curve to rule them all Google Analytics is no longer viable due to the rise of ‘Not Provided’ and although the Google Keyword Planner could provide the average number of monthly searches as a proxy for impressions. However, in our experience the values provided by the Keyword Planner can vary significantly from actual values. Other methods include using PPC ads and rank tracking to accurately track impressions across a select few keywords. However, this method does not scale well due to cost involved and is still compounded with ‘not provided’ keyword issues when calculating organic keyword traffic. Although Google Webmaster Tools does not provide 100% accuracy due to sampled keyword data, it remains one of the most complete sources of natural search keyword data available. We observed that less sampling occurred when smaller data ranges were selected. As a result, by consolidating daily data, the final result is more detailed than if you were to export the entire date range directly from the GWT interface. We developed a method to extracted data day by day to greatly increase the volume of keywords and to help improve the accuracy of the average ranking position by ensuring that the average taken across the shortest timescale possible, reducing rounding errors. Methodology
  15. 15. 15 One click curve to rule them all Method The fantastic ‘PHP Webmaster Tools Downloads’ class was used as a basis to automate the export of data from GWT, one day at a time. This script was heavily modified to allow the automation of daily top query reports from hundreds of Google Webmaster Tools websites split across multiple Google accounts. Modifications made include: Running this process resulted in over 100,000 unique .csv files. • Creating a more organised folder structure • Filtering top query reports by WEB, MOBILE, VIDEO and IMAGE results in order to produce 4 csv files, per website, per day • Filtering results to include only United Kingdom data • Optimisation of the request/download process to improve speed and significantly reduce the chance of Google temporarily blocking access to the account (it happened!) • The optional functionality to write directly to a database was added but not utilised in the final study. Methodology
  16. 16. 16 One click curve to rule them all Data processing Due to the large volume of files and relatively large total file size, it was not viable to combine the csv files via a command line and then aggregate in excel. Something more powerful was required… Omniscope was used to crunch the numbers as it can easily process millions of rows worth of data. Each site’s web, image, mobile and video data extracts were placed into a site folder. This site folder was then brought into Omniscope where brand/non-brand query filters were applied. Naturally, with over a million unique keywords it is not possible to manually categorise brand/non-brand filters. By using a Levenshtein ‘fuzzy’ match we were also able to pick up a large majority of brand misspellings. Although we could never guarantee 100% brand/non-brand accuracy, of the keywords that were reviewed manually, we observed an extreme minority of incorrectly categorised keywords. It’s also worth noting that these incorrectly categorised keywords were due to severe misspellings and had a negligible number of impressions. Due to the fact that we’re aggregating data at a query level, we can be confident that these incorrect categorisations will have a minimal impact on the final CTR curve. The next step was to define size of each brand (large, medium and small), which was done based on the sum of brand query impressions for each individual site across the entire duration of the study. To contextualise your own website, extract one month of data, calculate the total number of impressions from your branded queries and see where you fit within the following bands: The final stage was aggregation. First the data was aggregated by query and average position, summing clicks and impressions for each unique query at every unique ranking position. This ensured that all of the 1.25m+ queries had an input into the average CTR. After that aggregation, the CTR is calculated (clicks/impressions). We chose to calculate our own CTR, rather than relying on GWT’s CTR, to greatly reduce rounding errors. The data was then aggregated by average position and CTR was calculated by finding the mean. Small = < 28.5k Large = 285k+Medium = 28.5k - 285k Methodology
  17. 17. 17 One click curve to rule them all Questions Why do we have a higher number of impressions for some of the lower positions? Impressions are reported by GWT at a query level. This means that an impression reported for a query ranking in position one will only count towards the total number of impressions for position one (not positions 1-10). In our study we are aggregating data to get the sum of impressions for all keywords that we have data for at any given ranking position. If more impressions are observed for any particular ranking position this will mean that: a) Our study had many keyphrases in this position and we therefore gathered comparatively more impressions at this position. b) The keyphrases that ranked in this position typically had a higher search volume than the surrounding keyphrases and therefore generated more impressions. Showing the sum of impressions for each ranking position helps us describe the distribution of our data. What about personalised search? How does that affect CTR? Based on our observations made throughout the study, we believe the personalised search results will be included in the aggregate web/image/mobile/video results provided by Google Webmaster Tools. Unfortunately, unless Google release more data to webmasters we will have no way of determining how much of this data is influenced by personalised search. However, we do know that personalised results work both ways. If you consistently engage and deliver a quality experience to your users to drive repeat business, you’ll likely benefit from preferential treatment to those users in their personalised search results. Fail to do so and you could be losing out to other brands. Methodology
  18. 18. 18 One click curve to rule them all Why does the sum of the CTR for all ranking positions equal more than 100%? Displaying data on a graph that totals 100% implies that the complete story is displayed on the graph. In reality, we know that there will be clicks being given to results below the 30th position therefore in order to ‘normalise’ the data accurately to show the complete distribution of clicks, we would need access to completely unsampled data, something that GWT does not provide. Additionally, CTR is a calculation of clicks / impressions for any given ranking position. For the sum of CTR to equal 100% would require that for every impression, only 1 click is attributed to 1 result. In reality, we know that this is unlikely and evolving search habits suggest users may click on multiple links within the serps from a single impression resulting in a greater number of clicks per impression. Further testing is required to determine exactly what constitutes a click or an impression. i.e. Does scroll depth matter when counting impressions? Do modern browser functionalities such as ‘open in new tab’ and CTRL+Click get counted consistently as clicks? Methodology
  19. 19. 19 One click curve to rule them all Observations We observed and learnt a couple of interesting things about Google webmaster tools data during the data gathering process. • Positions 1 to 9.9 are rounded to 1 decimal place. Positions 10-99 are to the nearest integer and positions 100+ are to the nearest 10. This shows that Google rounds down and we therefore where we had decimal places in our 1-9.9 rankings we rounded down to ensure rouding consistency across the study. • Image search results are also included in ‘WEB’ despite a separate filter being in place. We saw a keyword rank consistently in position #1 in the WEB data, receiving 60k impressions and not a single click. Closer investigation revealed this result was an image and the ranking domain did not appear within the top 100 ‘blue link’ results. • We hypothesise that likewise, video results firing within web search will also be included in web reports. Further analysis and study will be required to confirm this. Methodology
  20. 20. 20 One click curve to rule them all 2 3 4 5 6 7 8 9 10 0 5% 10% 15% 20% 25% 30% 35% 40% 1 How does our study compare against others? Let’s start by looking at the top 10 results. In the graph below we have normalised the results in order to compare our curve like-for-like with previous studies. Straight away we can see that there is higher participation beyond the top 4 positions when compared to other studies. We can also see much higher CTR for positions lower on the pages which highlights how searchers are becoming more comfortable with mining search results. NetBooster (2014) Catalyst (2013) Slingshot (2011)Smoother transition into the lower positions Methodology
  21. 21. 21 One click curve to rule them all Position NetBooster Desktop CTR Catalyst (2013) Slingshot (2011) 1 19.35% 17.16% 18.20% 2 15.09% 9.94% 10.05% 3 11.45% 7.64% 7.22% 4 8.68% 5.31% 4.81% 5 7.21% 3.50% 3.09% 6 5.85% 1.63% 2.78% 7 4.63% 1.09% 1.88% 8 3.93% 1.04% 1.75% 9 3.35% 0.44% 1.52% 10 2.82% 0.51% 1.04% Comparing our top 10 CTR data against previous studies Methodology
  22. 22. A new click curve to rule them all
  23. 23. 23 One click curve to rule them all 0 5% 10% 15% 20% 1 2 3 4 5 6 7 8 9 10 11 1213 1415 16 17 1819 20 21 22 23 24 25 2627 28 2930 A new click curve to rule them all Our first click curve is the most useful as it provides the click through rates for generic non brand search queries across positions 1-30. It shows that position one receives 19% of total traffic, compared to 15% at position 2 and 11.45% at position three. Non-brand click curve for positions 1-30 in organic search This curve shows much higher CTR for positions typically below the fold and also demonstrates that searchers are frequently exploring pages 2 and three. This gives us a better understanding of the potential uplift in visits when improving rankings from positions 11-30. We need to start thinking beyond the top 10!
  24. 24. 24 One click curve to rule them all What about positions 11-30? When we look beyond the top 10 we can see that CTR is also higher than anticipated with positions 11-20 accounting for 17% of total traffic. Positions 21-30 show higher than anticipated results with over 5% of total traffic coming from page 3. Again this highlights that searchers are becoming more comfortable with using search engines, and that they will look beyond the top 10 to find the right result. The prominence of paid advertising, shopping ads, knowledge graph and the onebox may also be pushing users below the fold more often as users attempt to find better qualified results. It may also indicate growing dissatisfaction with Google results although this is a little harder to quantify. But it’s also important we don’t just rely on one single click curve. Not all searches are equal. What about the influence of brand, mobile and long tail searches? A new click curve to rule them all Position NetBooster Desktop CTR 11 3.06% 12 2.36% 13 2.16% 14 1.87% 15 1.79% 16 1.52% 17 1.30% 18 1.26% 19 1.16% 20 1.05% 21 0.86% 22 0.75% 23 0.68% 24 0.63% 25 0.56% 26 0.51% 27 0.49% 28 0.45% 29 0.44% 30 0.36%
  25. 25. 25 One click curve to rule them all 0 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 11 1213 1415 16 17 18 19 20 21 22 23 2425 26 27 28 2930 The influence of brand bias on CTR Brand influence is also something that we need to consider. In particular, how does the size of the brand influence the curve? To explore this further we banded each of the domains in our study into small, medium and large categories based on the sum of brand query impressions across the entire duration of the study. When we look at how brand bias is influencing CTR for non brand search queries, We can see that better known brands get a sizeable increase in CTR. In comparison, small to medium sized brands are in fact losing out to results from well established brands. What is clear is that keyphrase strategy will be important for smaller brands in order to gain traction in natural search. Identifying and targeting valuable search queries that aren’t already dominated by major brands will minimise the cannibalisation of CTR and ensure higher traffic levels as a result. curves based on non brand search queries Know where and when to fight your battles A new click curve to rule them all Large Medium Small
  26. 26. 26 One click curve to rule them all 0 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 11 1213 1415 16 1718 19 20 21 22 23 2425 2627 28 2930 What about the long tail? So how does the curve change when we account for longer tail keyphrases? We looked at search queries with multiple words to see how engaged users were when they had refined their search. One word searches are likely dominated by larger more well known brands in higher positions so it’s no surprise we can see a high CTR for those types of queries. However we can see the highest CTR as primarily for queries with 4 or more words which indicates higher satisfaction as users refine their searches. Brand authority plays a big role for 1 word queries A new click curve to rule them all 1 word query 2 word query 4 word query 3 word query
  27. 27. 27 One click curve to rule them all 0 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 11 1213 1415 16 17 1819 20 21 22 23 24 25 2627 28 2930 Mobile CTR Mobile search has become a huge part of our daily lives and our clients are seeing a substantial shift in natural search traffic from desktop to mobile devices. According to Google 30% of all searches made in 2013 were on a mobile device and they are now expecting mobile searches to contribute over 50% of all searches in 2014. Understanding CTR from mobile devices will be vital as the mobile search revolution continues. It was however interesting to see that the click curve remained very similar to that of desktop. Despite the lack of screen real estate searchers are clearly motivated to scroll below the fold and beyond the top 10. A new click curve to rule them all
  28. 28. 28 One click curve to rule them all NetBooster CTR curves for top 30 organic positions Position Desktop CTR Mobile CTR Large Brand Medium Brand Small Brand 1 19.35% 20.28% 20.84% 13.32% 8.59% 2 15.09% 16.59% 16.25% 9.77% 8.92% 3 11.45% 13.36% 12.61% 7.64% 7.17% 4 8.68% 10.70% 9.91% 5.50% 6.19% 5 7.21% 7.97% 8.08% 4.69% 5.37% 6 5.85% 6.38% 6.55% 4.07% 4.17% 7 4.63% 4.85% 5.20% 3.33% 3.70% 8 3.93% 3.90% 4.40% 2.96% 3.22% 9 3.35% 3.15% 3.76% 2.62% 3.05% 10 2.82% 2.59% 3.13% 2.25% 2.82% 11 3.06% 3.18% 3.59% 2.72% 1.94% 12 2.36% 3.62% 2.93% 1.96% 1.31% 13 2.16% 4.13% 2.78% 1.96% 1.26% 14 1.87% 3.37% 2.52% 1.68% 0.92% 15 1.79% 3.26% 2.43% 1.51% 1.04% 16 1.52% 2.68% 2.02% 1.26% 0.89% 17 1.30% 2.79% 1.67% 1.20% 0.71% 18 1.26% 2.13% 1.59% 1.16% 0.86% 19 1.16% 1.80% 1.43% 1.12% 0.82% 20 1.05% 1.51% 1.36% 0.86% 0.73% 21 0.86% 2.04% 1.15% 0.74% 0.70% 22 0.75% 2.25% 1.02% 0.68% 0.46% 23 0.68% 2.13% 0.91% 0.62% 0.42% 24 0.63% 1.84% 0.81% 0.63% 0.45% 25 0.56% 2.05% 0.71% 0.61% 0.35% 26 0.51% 1.85% 0.59% 0.63% 0.34% 27 0.49% 1.08% 0.74% 0.42% 0.24% 28 0.45% 1.55% 0.58% 0.49% 0.24% 29 0.44% 1.07% 0.51% 0.53% 0.28% 30 0.36% 1.21% 0.47% 0.38% 0.26% A new click curve to rule them all
  29. 29. 29 One click curve to rule them all NetBooster Long Tail CTR for top 30 organic positions Position 1 word query 2 word query 3 word query 4+ word query 1 19.04% 15.39% 19.23% 22.63% 2 7.39% 11.58% 15.97% 18.05% 3 4.78% 8.87% 12.75% 13.32% 4 3.25% 6.79% 9.82% 9.99% 5 2.48% 5.49% 8.52% 8.20% 6 1.90% 4.59% 6.91% 6.61% 7 1.55% 3.70% 5.55% 5.11% 8 1.35% 3.17% 4.83% 4.18% 9 1.15% 2.70% 4.15% 3.51% 10 0.95% 2.36% 3.45% 2.92% 11 1.08% 2.51% 3.60% 3.49% 12 1.55% 2.65% 2.43% 2.15% 13 1.73% 2.65% 2.18% 1.73% 14 1.66% 2.44% 1.76% 1.47% 15 2.02% 2.26% 1.76% 1.33% 16 1.47% 1.87% 1.63% 1.06% 17 1.45% 1.79% 1.26% 0.89% 18 1.23% 1.79% 1.19% 0.88% 19 1.12% 1.69% 1.18% 0.71% 20 1.12% 1.48% 1.05% 0.69% 21 1.15% 1.27% 0.85% 0.48% 22 1.19% 1.21% 0.69% 0.41% 23 1.03% 1.11% 0.61% 0.39% 24 1.03% 1.06% 0.61% 0.29% 25 0.89% 1.04% 0.48% 0.26% 26 1.14% 0.95% 0.41% 0.22% 27 1.15% 0.82% 0.46% 0.22% 28 0.84% 0.80% 0.39% 0.23% 29 0.75% 0.72% 0.37% 0.26% 30 0.73% 0.62% 0.37% 0.13% A new click curve to rule them all
  30. 30. How can you optimise your CTR?
  31. 31. 31 One click curve to rule them all How can you optimise your CTR? Fortunately publishers have a number of options when it comes to optimising the CTR of their content. Page Titles Page titles add meaning and relevance for both human searchers and search engines. Using relevant keywords in page titles are heavily weighted by search engines. Meta Descriptions Although meta descriptions are not directly used by search engine algorithms they are an important way to influence CTR. It’s therefore vital you write compelling ad copy. • Page titles should be descriptive and relevant to page content • Keep titles under 55 characters. Google now uses pixel width to format its organic search results (482px for desktop) so anything over this will be truncated • Include keywords at beginning of title • Each page should have a unique title • Keep meta descriptions under 155 characters in length (including spaces) • Add as many unique selling points as possible • Include a clear call to action • Add a unique and relevant description for each page
  32. 32. 32 One click curve to rule them all Rich snippets (schema) A regular snippet displays the sites meta description or automatically generated description of the page content. A rich snippet, however, gives more information about a search result such as review information, product name or price. Aggregate star rating for example can really make sure content stand out and influence both CTR and conversion in a positive way. URL optimisation Although your URL makes up a relatively small part of the search snippet, it can still be a valuable way to make the snippet stand out to users. Applying breadcrumb Schema markup can be a great way to shorten the appearance of URLs without the need to format the actual URLs themselves. • The page content must contain the correct markup for search engines to understand the content and display the rich snippet • Microdata outlined on is the preferred form of markup. • Google currently supports rich snippets for people, events, reviews, products, recipes, and breadcrumb navigation How can you optimise your CTR?
  33. 33. What are the practical uses for this data?
  34. 34. 34 One click curve to rule them all What are the practical uses for this data? The underlying motive for this study was to create a methodology that helped us understand click through rates for individual clients rather than rely on a one size fits all approach. There are three important areas where we could see an immediate application of this data. Forecasting Having an accurate click curve can help us understand and estimate the number of visits clients could expect from higher positions. This is particularly helpful for forecasting the potential ROI from natural search campaigns. For example, by applying your average order value and conversion rate you can get a good idea of what revenue any incremental traffic will bring to a business. Benchmarking This study has allowed us to create several click curves based on the size of the brand that we can now use to compare against. This is vital to maximising the performance of content in natural search. Publishers can now compare individual CTR for keywords against these benchmarks to see whether they are underperforming across the entire top 30. Opportunity analysis By fully understanding the curve and knowing more about the influence of brand bias, you can apply a more intelligent approach to keyphrase selection and discover better ways to invest SEO budget or potentially outmanoeuvre larger brands.
  35. 35. 35 One click curve to rule them all Creating your own click curve This study will give you a set of benchmarks for both non brand and brand click through rates with which you can confidently compare your own click curve data. Using this data as a comparison will let you understand whether the appearance of your content is working for or against you. So by now you should have everything you need to create your own custom click curve and understand how visitors are interacting with your brand in natural search. We have made things a little easier for you by creating an Excel spreadsheet which allows you to drop your own top search query data in and automatically create a click curve for your website. Simply visit the NetBooster website and download our tool to start making your own click curve! What are the practical uses for this data?
  36. 36. Conclusion
  37. 37. 37 One click curve to rule them all Conclusion It’s been both a fascinating and rewarding study and we can clearly see a change in search habits. Whatever the reasons for this evolving search behaviour, we need to start thinking beyond the top 10 as pages 2 and 3 are likely to get more traffic in future. We also need to maximise the traffic created from existing rankings and not just think about position. Most importantly, we can see practical applications of this data for anyone looking to understand and maximise their content’s performance in natural search. Having the ability to easily create your own click curve and compare this against a set of benchmarks means that you can now understand whether you have an optimal CTR. There is however plenty of scope for improvement and we are looking forward to continuing our investigations and tracking the evolution of search behaviour. What could be the next steps? If you’d like to explore this subject further, here are a few ideas • Segmenting search queries by intent. How does CTR vary depending on whether a search query is commercial or informational? • Understanding CTR by industry or niche • Monitoring the effect of new knowledge graph formats on CTR across both desktop and mobile search • Annual analysis of search behaviour. Are people’s search habits changing? Are they clicking on more results? Are they mining further into Google’s results?
  38. 38. 38 One click curve to rule them all Ultimately click curves like this will change as the underlying search behaviour continues to evolve. We are now seeing a massive shift in the underlying search technology with Google in particular, heavily invested in entity based search (i.e. the knowledge graph). We can expect other search engines such as Bing, Yandex and Baidu to follow suit and use a similar approach. The rise of smartphone adoption and constant connectivity means that natural search is becoming more focused on mobile devices. Voice activated search is a game changer as people start to converse with search engines in a more natural way. This has huge implications for how we monitor search activity. What is clear is that no other industry is changing as rapidly as search. Understanding how we all interact with new forms of search results will be a crucial part of measuring and creating success! Conclusion
  39. 39. 39 One click curve to rule them all About NetBooster NetBooster is an independent international performance agency that makes its comprehensive expertise of digital marketing available to its clients to achieve the best possible performance for their investments. The agency invests in technology and covers the entire chain of online marketing through its European network: search engine optimisation and marketing, data and analytics (DnA), display, affiliation, online media, creation, eCRM and social networks, with a recognised expertise in tomorrow’s digital marketing (Social Media, Video, Ad Exchange, etc.). Shares in NetBooster are traded on the NYSE Alternext Paris. Authors Gary Moyle, Head of SEO UK Gary has over 10 years’ experience in digital marketing and has worked on both UK and international campaigns in the travel and retail industries. Gary joined NetBooster in 2008 and was appointed as UK Head of SEO in 2013. Rhys Jackson, SEO Executive Shortlisted for the Young Search Professional award in the UK Search Awards 2014, Rhys has been with NetBooster for a year and a half and has developed an in-depth knowledge of SEO, Google Analytics, data processing and tracking methodology. Matt Hallett, Digital Analyst With 5 years’ experience in online data analysis, Matt joined NetBooster this year as our first dedicated Digital Analyst. He works across platforms to visualise data and provide fresh insights to our clients.