Paid Search (PPC) Search Marketing

March 19, 2018 at 9:57 am

Data-Driven Decisions: Using Click-Through-Rate to Maximize Conversion

The click-through rate (CTR) is the ratio of clicks on a link to the impressions it makes. The impressions of a link is a count of how many times the ad appears on a results page or other sites within the Google network. The data on a website’s CTR is available on Google Analytics (GA). The average CTR is also available for data on a campaign rather than individual keywords.

A digital marketing agency is familiar with CTR as its one of ways to measure how effective is their pay-per-click (PPC) marketing strategy. Whether or not a site utilizes PPC with the help of Google Adwords is out of the question. Understanding the difference between individual CTR and the average CTR of the website helps marketers and site optimizers to do their job.

Utilizing Average CTR for Data-Driven Decision Making

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Image Credit

The average CTR is crucial in finding if a digital marketing campaign works. It’s the most important metric to monitor in regards to PPC advertising. It on contributes to a keyword’s expected CTR which is a component of AdRank. Each ad and keyword has their CTR in GA. The data will help you analyze its relevance to the what you advertise and which networks.

The average CTR is an indicator of performance. A high CTR shows that the link or ad pays off as it draws traffic to your site. Meanwhile, a low CTR may be the result of ineffective content, targeting the wrong audience, reaching an indifferent demographics, and using the wrong keywords. Still, other factors influence a site’s average CTR that is out of one’s control. Industry, ad format, ad position, and channel are some of the factors or benchmarks that can raise or flunk the CTR.

CTR Benchmark and its Effect on the Data

Based on the updated benchmarks per industry from Wordstream, the field for search network and display network became competitive as the average CTR for all industries on both networks decreased its percentage. Still, the CTR for ads on the search network remains greater than Google Display Network.

The reason behind these data stems from user behavior. Those on the search network are actively eyeing for content related to keywords, so the links and pages get high CTR. Meanwhile, the ads on the display network allow users to skip past the ads. Thus, they become passive and revert their attention to click on the ads or links. Some users also find ads annoying as it disrupts their overall viewing experience.

Display ad format and its position on the search engine results page (SERP) are also CTR benchmarks. The position of the ad or link in the results page can result in a high or low CTR. For instance, belonging to an industry with a high CTR doesn’t guarantee the same result. The site ends up with a lower CTR if it does not rank in the SERP. If the keyword is in high rank or top one, the site can have a higher CTR even if the industry average is low.

Last but not the least, channels significantly influence CTR. Users from different platforms vary in behavior. Those coming from Facebook comes into the site for a different reason to those from Google search. Looking into which channel a page gains the highest CTR is a way to utilize the “last-click wins” approach. That way, the site makes the CTR SEO factor to conversion.

CTR as Part of SEO

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Sample CTR data of

These are guiding principles to utilize the CTR as an essential SEO and PPC metric. Impressions form a great part of the CTR, but it won’t be complete without the clicks. Engaging users is the key to earning a high CTR. Take note that CTR and SERP ranking are inversely correlated. At the same time, some users prefer to use different channels.

As a webmaster or digital marketer, it’s essential to determine a baseline CTR and goal. Doubling SEO efforts to be or stay on page one of SERP can become the first task. Analyzing data is also crucial to become skilled in tracking the CTR by a selected period. CTR is one of the several data to get from GA for applying data-driven decisions.

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