Every time someone performs a search on Google, the search engine company holds an auction to determine an ads, Ad rank. This number defines where an Adwords advertisers ad will place within the Adwords search results. Ad rank is specifically determined for each and every search using the formula: MaxCPC x Quailty Score = Ad Rank. Quality score results are based on a scale from 1 to 10, 1 representing a poor performing ad and 10 being a top performer. This sliding scale quickly enables Google to determine in what position competing ads will appear. Generally, if your ad has a quality score of 3 or lower, Google will not run the ad no matter how much your Cost-Per-Click bid is.
The auction process gives in the even ground to Adwords advertisers’ bids and quality scores. The key factor in improving on your quality score is relevancy. Quality score is determined for a specific keyword using a number of variables including relevancy among the ads copy, the destination landing page, the and of course the keyword.
Ads with higher quality scores are rewarded by Google with a higher position and a lower Cost-Per-Click. Ads with a low quality scores risk not even running because they are considered, not relevant. This is why monitoring and managing you Adwords quality scores are so important. Using three or fewer keywords per ad, focusing on creating relevant ads for those keywords and performing accurate A/B tests for your ad groups are all vital factors to successfully managing your ad campaign.
Effectively managing quality score not only raises your ads position, leading to a greater number of click-thrus, but also holds the advantage of creating a higher return on investment (ROI) for your entire campaign. More relevant ads appear higher on the search engine results pages (SERPs), for less cost to the advertiser. That means more clicks for less money. An example of how this works can be seen below;
The Importance of Ad Rank
In the example above, our competitor will need to spend $2,410 in order to outperform our ad for every 1000 clicks. This is a drastic difference in cost and can have tremendous impact on your company’s bottom line. It is for this reason that focusing on relevancy and consistently working to improve quality score is so important.
So we’ve established that quality score is important, but how do we improve it? A simple solution is to A/B test your ads with a focus on improving relevancy and raising click-thrus. An A/B ad test is performed when you run an ad variation (an alternate version of a current ad) against a control ad (an ad currently being run in your ad campaign). Google Adwords allows its users to set as many ad variations as they’d like in order to test ads. These ad variations will appear in place of the control ad for its keywords at random intervals. Analyzing data such as Click-Thru-Rate (CTR) and conversions will help an advertiser decide which ad is performing better. Should the ad variation not perform as well as the control, scrap it and create a new one. If it out performs the control, the original ad should be replaced with the new one.
But simply running one ad against another is not an efficient way to test ads. Ad variations often don’t perform as well as control ads. If this underperforming ad variation is running 50% of the time, you could be losing money by running an underperforming ad. This creates a problem because we have to test our ads, but don’t want to lose money in the process.
In order to minimize loss and still evaluate our ad variations we can set up a trial that only allows our test ads to run 20% of the time. This will minimize the impressions our ad receives while still allowing for an efficient test. This can be accomplished by setting up our test so that we have three identical versions of our control ad and only run the ad variation against one of the copies. This way our control ad is shown only 20% of the time rather than the original figure of 50%. Utilizing this method, we still provide enough impressions to assees the performance of the control ad, while minimizing the potential loss of performing the A/B test.
Should the ad variation outperform its control ad, begin to eliminate the duplicate copies of the control. Continue this process until the control and the ad variation are running 50/50. Should the test ad continue to outperform, replace it with the control and start the process over with a new test ad. Should the ad variation underperform, which is usually the case, either create a new test ad and start the process over or remove the copies of the control and let the original ad run on its own. Below is a diagram of how we perform this sort of test.
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