I often get asked how much a keyword is worth. It’s a tough question to field but one that should constantly considered when developing marketing strategy. Knowing the possible return on targeting keywords in SEO campaigns will improve ROI.
The trouble is, there is no real way of calculating keyword value with complete accuracy. There are a few key datasets that can be used to estimate expected click through rates. Those numbers can then be combined with estimated CPC values to give a sort of functional estimate. It’s nowhere near being an exact measure—but it’s still incredibly useful.
- Keywords don’t have exact values but estimated figures are useful
- Click-Through-Rates for keywords aren’t a fixed number and are estimated based on several key datasets of user behavior
- The first several results receive the majority of clicks
- Estimated keyword value can be calculated using traffic, CTR, and CPC numbers
- Using estimated keyword value can help create higher ROI for search campaigns.
To create a formula for the accurate calculation of anything, one first needs data to draw insight from. For estimating keyword value, we can use data from past studies produced by various agencies. These studies have investigated how users interact with the Google search results page (SERP) and forecast estimated click-through-rates (CTR) based on position ranking.
Being able to estimate the CTR of results in the search results allows estimation of the value of ranking there. That’s to say; if you can estimate how many site visitors you might get from ranking number one for a keyword—you can estimate how much that keyword is worth.
Finding Relevant Data
Google is notorious for operating behind closed doors, offering little insight into their algorithm. To get the type of insights needed to make accurate keyword value estimations, we’re forced to rely on a handful of studies from agencies and companies that have access to relevant user interaction data.
There have been a handful of these studies in the past few years, but the length of their relevancy is greatly reduced by Google’s constant adjustment of their algorithm, the addition of new search features like Local Packs, and the ever-increasing shift towards mobile.
We use averaged numbers from each of these studies to help better account for variability, but each is able to offer some value on their own. Below, you’ll find a brief overview of the most relevant SERP CTR studies to date, and the insights they provide for estimating SEO keyword value.
2013 Chitika Study
Chitika is an advertising agency that has a network encompassing over 350,000 publishers across a variety of digital mediums. They serve over 4 Billion ads per month and integrate powerful targeting and analytics tracking technology within their services.
To gather their data, Chikita examined tens of millions of ad impressions where a user was referred to the site serving their ad by Google. By referencing this data, Chikita is able to determine where that site ranks within the SERP for the keyword the searcher was using, and can in turn measure the amount of traffic that comes from different search result positions.
It’s a bit of leg-work, but their endeavor offered up some very valuable insights from a very large set of data. Below are some of the highlights:
- 33% of clicks go to the 1st position
- 64% of all clicks go to positions 1-4
- 91.5% of all clicks are on first page
In our opinion, this set of general keywords not being actively monitored by SEO tracking likely paints a more accurate picture of practical search user behavior. When I do keyword value calculations, a significant amount of weight is placed on the insights gathered from this study. This survey uses data produced by proprietary Chitika technology, and avoids relying as heavily on data provided by Google.
Optify 2011 Study
Optify was a Software as a Service (SaaS) marketing tools business that closed it’s doors in late 2013. Beforehand though, they offered a very useful analysis of search engine data gathered from their analytical tools. This study, now most accurately summed up by Search Engine Watch, offered some unique insights such as CTR rates of ‘head’ keyword terms vs. ‘long-tail’ terms. For example, they found that typical CTRs of keywords receiving more than 1000 searches per month were larger than CTRs of keywords with 100 or less monthly searchers. Some general takeaways from this survey are as follows:
- 36.4% of clicks go to 1st position
- 66.3% of all clicks go to positions 1-4
- 85.6% of all clicks are on the first page
This study was conducted in late 2010 among 250 buyer to buyer (B2B) and buyer to consumer (B2C) websites that encompassed nearly 10,000 keywords. The general takeaway from this data was that being on the first page is critical, being in the top 4 results is vital for profound returns, and being the 1st result in the SERP will result in more traffic than the 3rd-10th positions combined. Again, this data was biased towards more commercial intent as it was provided by website data from commercial clients, presumably monitoring keywords more highly favored towards commercial intent.
AOL 2006 User Data Leak
In addition to the studies mentioned above, there have been several other studies conducted by SEO agencies and marketing firms that have offered some similar insights. One of the most notable, though likely now outdated, was the 2006 leak of AOL’s user search data. This data detailed the search habits for roughly 650,000 users over a period of 3 months, resulting in approximately 20 million searches. Some of the highlights of this study are as follows:
- 42.3% of all clicks go to 1st position
- 69% of all clicks go to positions 1-4
- 90% of all clicks are on first page
For a long time, this dataset was considered the gold standard for keyword value estimation and CTR trends among online searches. A lot has changed since then, especially in the past few years, but it’s remarkable how similar search trends were among this data and similar data collected nearly a decade later—from a different search engine!
2013 Catalyst Study
Catalyst is a Boston-based, large-scale marketing firm that services many Fortune 1000 companies. From time to time they produce whitepapers offering valuable industry insight. One such paper titled Google CTR Study: How User Intent Impacts Google Click-Through Rates offers a lot of insight into specific variables such as search intention and keyword length.
This study was conducted from data downloaded from 59 of their client’s Google Webmaster tools portals, and encompassed 17,500 unique keyword search phrases. In our opinion, this is a fairly small amount of data, and likely skewed largely towards certain user intent and keyword values. Some of the takeaways from this study are as follows:
- 83% of all clicks go to positions 1-4
- 17% of clicks go to the 1st position
- 48% of all clicks are on first page
In addition to these insights, the 2013 Catalyst study investigated differences in mobile vs. desktop searches, branded vs. non-branded searches (non-branded had higher CTR actually), CTR of informational searches, and CTR of coupon and discount code type searches.
They found that mobile searchers tend to be more likely to click the first result, coupon-related searches are more likely to click the first result, and that navigational type searchers typically click the first 2 results. It should be noted, this was before Google’s Local Pack SERP feature was seen in such heavy use as it is now.
Overall, this study offered valuable insight into the impact of different types of user intent on CTR, but in our opinion isn’t too useful in making practical predictions for average searches.
Averaging the Data
Using an average of several datasets provides a less biased figure reducing the influence of any user behavior specific to the sources of data. For example, using the datasets mentioned in this article we find the following average positions:
To further reduce bias, and account for variability in CTR as a result from SERP features that didn’t exist during these studies, one can average data for several positions. An example of this approach would be to use an average of click-through-rate for the top 4 positions when estimating keyword value. In this case, that figure comes out to be roughly 17%.
Calculating Estimated Keyword Value
With a functional CTR rate in hand, the next step is to bring in data for estimated monthly search volume and estimate cost-per-click bids. These data can be found in the Google Ads Keyword Planner tool or through varying third party tools such as SEMRush or ahrefs. In most cases, these numbers are going to be estimates as well. The formula for finding the value of a single keyword is as follows:
To get a better idea of how this formula can be applied, consider the following example for the keyword Cheap Car Insurance. This keyword gets roughly 165,000 monthly searches, and has a suggested CPC bid of $24.07 in the Google Keyword Planner tool. Using the above formula and the value of 37% for an estimated CTR, we can estimate the value of ranking in the first search position for this keyword as follows:
Using this formula, we can estimate that ranking in the first position of Google for the keyword phrase cheap car insurance would have a value equivalent to the advertising cost (CPC cost) of nearly $1.5 Million per month. At first glance this keyword seems much more valuable than it’d be practically worth.
Two or three years ago, the first page of Google would have been owned by Nationwide, Progressive, and State Farm for this keyword—but now it’s all local results. Sure, you may get your local State Farm Office in the results, but it won’t be for the State Farm website as a whole. If you’re a local car insurance provider the best way to calculate this value would be to use the location filter within the Google Keyword Planner to isolate your local search volume.
To show you just how dramatic these location packs can be, let’s take a look at the estimated keyword value for cheap car insurance from someone searching within New York City—which has an average of 3,600 monthly searches and an estimated $30.26 CPC:
As you can see, the keyword isn’t worth nearly what the entire United States keyword would be, though Local Packs make it somewhat irrelevant to target now anyway (for now). These examples illustrate how using the keyword calculation formula can provide useful estimates of overall keyword value, and can help budget for your SEO campaigns more appropriately.
For example, a local car insurance broker in New York would have to spend $40,000 per month to get the number of clicks they could expect from ranking in the first position for cheap car insurance for local searchers. When budgeting for their SEO campaign, they could invest $150,000 and be ROI positive (from a CPC perspective anyway) after the first 4 months of ranking number one.
After ranking number 1, they would essentially be getting $40,306 of targeted traffic to their website each month—without spending any more money! This is why SEO is such a powerful tool to help businesses grow, and why sometimes a little bit of risk is worth taking.
Accounting for Variables
The relevant data for keyword calculation certainly has its flaws and is to be used only as an estimate. User behavior is unpredictable, and technology interfaces have been rapidly changing. In 2006, there wasn’t a notable presence of mobile users conducting searches within the AOL datasets.
In 2011 even, mobile technology likely had little impact on the Optify study. More recent studies such as the Hitwise Mobile Search: Topics and Themes report estimate that mobile searches average as much as a 60% share of total searches. Google itself has now implemented real-time changes in ranking, with much more volatile position changes being reported by webmasters than in past years.
Other newer search features such as featured snippets, local packs, and instant answers all play a role in affecting these statistics as well. When using this data, it is best to aggregate as much as possible to normalize for fluctuations. An example of this would be estimating value in keyword groups rather than individual keywords.
There are no concrete ways to put a value on a keyword. The datasets discussed here only help to provide an estimated value that still fails to account for modern SERP features like local packs, featured snippets, instant answers, an more. It’s unclear how these specific features may influence CTR for organic results. Likely, they lower it.
As a practical measure, I usually estimate keyword value by using search volume groups of keywords and an averaged CTR (17%) for the first 4 positions to account for fluctuations in ranking. I find this offers an additional layer of accuracy, but still often falls victim to Google’s algorithm changes or new search features.