Many bloggers, as well as publishers, are struggling to increase their online ad revenues. You might be facing the same difficulty to increase the income that you get from your web ads. If you have not already applied A/B split testing, then I suggest you to do so. Why? With simple A/B split testing, you can easily improve your online ad revenues. Here are 5 simple A/B split testing methods to increase your ad revenues:
1. Image vs Text Ads
If you are using AdSense, you definitely need to compare Image vs Text Ads. Google recommends that you enable text as well as image ads, however, this is not correct and we have stats to back up this claim through A/B testing. Certain websites get better CTR on image ads while certain websites generate more revenue using Text ads. You should A/B test to compare and see what works best.
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2. Testing the Ad Placement
You can change the placement of your ads in several different spots. For instance, you can start placing your ads in your header as well as within your blog post. You can also try different spots that you’ve never tried before. For instance, in the middle of your post. Or, you can put your ads in the left sidebar or right sidebar. These changes in the layout and positioning of the ad can make a big difference in the revenue generated.
3. Testing the Ad Size
The size of your ad matters. Why? Sometimes, people are more attracted with bigger ads, and sometimes people are not attracted with those ads at all. It will depend on the website in which the ads are placed (sometimes, the layout of your website will also affect your ad performance). So, you can experiment with various sizes that you can use for your ads. Test different ad size can help you to increase your revenues or not. You can also check this case study by Google, which shows how ad sizes can effect your ad revenues.
4. Testing the Colour Scheme
If you are using AdSense to monetize your website or blog, it is important to experiment with the colour themes of your ads. Your ad colour scheme can give more click-through rate for your ads, therefore increasing your revenue. How to test? Create a variations which matches the colour scheme of your website, or try creating a contrasting version. You can also try keeping the background colour of the ads matching the background colour of your website.
5. Continuous Optimization
Optimization is a continuous process, you will notice a sharp rise in your revenues if are able to create a variation (ad size, location, theme) which works. However, with time the effectiveness of the new variation will decline as your visitors start developing banner blindness around the ads. So, optimization should be a continuous process and you should keep looking for better alternatives even before the performance of the current variation begins to decline.
Those are simple A/B testing methods to increase your ad revenues. If you want to make the split testing process more effortless (although you can always do it manually), you can use a good split-testing software that is designed specifically for website publishers such as Adpushup or Optimizely.
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Thanks for this amazing information
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This post provided me all the details about the AB Testing and clear of my confusion aboutAB Testing.
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I think the biggest thing I struggle with is placement. The whole above the fold thing is hard to judge when you use a few different computers. However, I have found that my website does a little better with image ads than plain text ads. I just need to work on my placement and then I truly believe I will see a rise in my income from ads.
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@carlalexander – To know your best positions for ad placement, look at your website as you would if you were a guest to it. Put aside the fact that you own it and just start reading the content as if you are a visitor. If the ads catch your attention, then you are doing something right. Alternatively, you can also have your friends and family help you out with this as well.
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Thanks for sharing this AB/ testing summary. We’re glad to see we’re not alone to think A/B testing is very important in order to increase online ad revenues.
Otherwise, we totally agree with you when you say A/B testing is useful to determine which kind of ads increase revenues. Often, it’s not because we have a big ad as an image than this ad can increase more efficiently revenue than a little text link.
Regards,
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Arise.io
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This all sounds easy on “paper” but how exactly are you supposed to know that your ad is in the best position? I mean, it takes a while before anyone clicks on the ad so how are you supposed to know that a certain type of ad works best in a particular location? Does it take a day, or several months? How long does it take before you know that your ad is in the best place?