How to Properly Analyze Ad Text Performance

 How to Properly Analyze Ad Text Performance

When setting up a paid search campaign, proper structure is essential for success. Creating tight ad groups with specific ad text that matches searcher intent can boost your results. Not only should you be optimizing for the user’s experience, but also for Quality Score.

From the outside looking in, simply looking at an ad text report generated from AdWords, Bing Ads, or your paid search management tool will not do it justice. By doing this, you can actually draw conclusions and pause, delete, or alter ad text that may be a strong performer. If you happen to have worked with a nosy creative agency or super-involved client or boss, you may have experienced this situation. When dealing with this, it is important to show them that simply running a report from the engines and looking at landing pages does not take into account the entire picture. By making decisions off of less than half the data, you could potentially be causing catastrophic problems for the campaign.

How to Properly Analyze Ad Text:
Does the Ad Text Work?

You can have a plethora of data, plus a mountain of clicks and impressions, but one of the simplest ways to gauge how ad text is performing is to get an understanding of if users who click on a specific ad are converting on your website. Too often SEM managers focus on keyword performance when they calculate their CPA performance. Ad text plays a large role in leading a user to conversion and should be looked at as a big part of the puzzle.

When it comes down to it, if you are or are not getting the conversion/sale is what matters. No matter your Quality Score, bounce rate, or other metrics are out of whack, if your ad text is "working," it should be kept live.

Searcher Intent

When deciding what call to action to use or decide what landing page to utilize, the intent of the searcher is key. Certain buying signals or key phrases that call out where a searcher is during their research/purchase cycle can help determine their intent and what ad text they are shown. Without this understanding, data such as how much traffic is directed to certain landing pages could be interpreted wrong.

For example, by just reviewing ad text performance and not looking at what keywords are directing traffic, you could make the assumption that too much traffic is being directed to certain Web pages. But if a granular structure and deep linking strategy are utilized to direct traffic to the most relevant landing pages, you would realize that the "high traffic" to certain Web pages is warranted.

Account Structure

The account structure utilized within the account can also play a large role in the performance data available. If structured properly, the searcher should see an ad that matches their search query and be directed to a landing pages that also matches up with their search query. Thus providing a common message throughout the process and providing a positive user experience.

Effect on Quality Score

Quality Score should be a large part of your thought process when you structure your campaign. Hopefully, your ad text matches up well with the keywords and search queries that could potentially drive users to click on the ad.

Chances are if you are testing out new ad creative and your Quality Score improves, diagnostic metrics such as click-through rate, impressions, and others have improved as well. While Quality Score should not be the only factor you think of when it comes to ad text performance, it should be a key part of the data you review to make decisions.

The Key to Success:

When looking to analyze ad text data, it is paramount to not do so in a silo. All of the information pointed out above should be taken into consideration when determining what ad text should be paused, created, or altered. There also may be some specific nuances to your specific industry or vertical that will need to be examined along with the data set. By not looking at the data from a holistic point of view, you may end up in a situation where you are making decisions off of non-statistically significant data.

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