Click-through rate
What Is Click-Through Rate?
Click-through Rate (CTR) refers to the percentage of people that click on an element that they have been exposed to. Click-through rate is calculated by simply dividing the number of people who clicked on a given element by the total number of visitors to that page.
CTR is a metric that is used to analyze emails, webpages and online advertising (Google, Bing, Yahoo etc). CTR is normally used to measure the success of marketing efforts.
CTR examples
Some common examples of where CTR can be measured include:
- A call-to-action link in an email
- A hyperlink on a landing page
- A PPC ad on a Google search results page
- An ad on a social media site such as LinkedIn or Facebook
Click-through rate is calculated by the number of clicks on an element divided by the number of people who have seen that element. [Total number of clicks on an element / Total number of people who saw the element]
Click-through rate and search ads
CTR is a commonly used metric when evaluating search ads in Google Adwords and other ad platforms. Since search ads are most often pay-per-click (PPC) the average CTR helps to determine how a given ad will perform in driving traffic, and how much the ads will cost.
In addition to gauging performance of ad campaigns, the CTR of a search ad also has an impact on its quality score. The higher the click-through rate of an ad, relative to its ad position, the higher its quality score will be, leading to lower CPCs and lower cost ads.
Digital marketing professionals can improve the performance of low CTR ads by improving the ad copy and by providing content that is a better fit for their target audience.
Similarly, CTR can also be important for SEO as the click-through rate of a search result is believed by many to be one of the ranking factors in Google. The same is true of display ads and ads across other platforms such as Facebook ads.
Unique click-through rate
One form of click-through rate is unique click-through rate: this specialized CTR indicates that an individual click and user are only accounted for once in the formula. By default, click-through rate measures the total clicks a user performs. unique clickthrough rate is a more precise measurement of user behavior and interest.
An offline example of click through rate
Imagine you are walking down an empty street with nine other friends. Everyone passes by a coffee shop, a donut shop and a candy shop. Five of your friends go into the coffee shop, three of your friends go into the donut shop, and you and another friend go into the candy shop.
If we were to calculate the click-through rate of each shop, the coffee shop would have a CTR of 50%, the donut shop would have a 30% CTR and the candy shop would have a 20% CTR. CTR measures behavior, interaction and interest and is helpful when determining the effectiveness of marketing efforts.
CTR should not be mistaken for conversion rate. Conversion rate is the percentage of people who take a desired action. CTR, on the other hand, is normally associated with an action leading up to a conversion. In the offline example, we can see that five out of 10 people prefer to go into the coffee shop. Of those five people, however, it may be that only one person actually buys a cup of coffee.
Click-through rate & A/B testing
CTR can be used to gain useful insights in A/B testing, as a secondary metric alongside the primary conversion metric. For example, if a conversion expert is running an A/B test on the product display page of a clothing company, the conversion goal would be the number of purchases completed.
A secondary metric might be the CTR of shipping information. If there is a high CTR for shipping information, this could mean that users are interested in shipping. There may be opportunities to draw value from the shipping information that could influence conversions.
CTR is useful in conversion optimization. It can be used to identify user behavior, user interest and can be used as a micro-conversion to build insights during A/B testing. CTR can help paint a more holistic picture of user behavior in A/B testing.
