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How AI makes in-app ad creatives better

Why do some mobile in-app ad campaigns succeed, while others fall flat? In part, it’s because the creatives used are just ineffective. Too often, ads go unseen — and unclicked.

In the second quarter of 2018, Moat’s average valid and viewable rate was around 60 percent, while the average viewable rate noted by IAS in the same time frame was less than 50 percent. That means two of every five ads will never be seen in full by a real person.

The average click-through rate of an in-app ad is just over 1.5 percent. While it’s better than the average CTR for mobile web ads (1.12 percent), it still means that a lot of ads are not leading to sales, app downloads, and sign-ups.

So, is there a better way? What can advertisers do to make sure their ad spend yields real results?

This is one application where artificial intelligence (AI) and machine learning (ML) can help in a major way. By applying advanced analytical insights to the art of ad creatives, mobile marketers can be sure their ad campaigns are more appealing and thus more effective.

How AI can help save the day

What makes an ad effective? Ad agencies used to rely on intuition and experience, but this hit or miss approach doesn’t cut it today, as brands need to be smarter about their ad dollars.

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By applying advanced predictive analytics capabilities to the development of mobile ad creatives, however, mobile marketers can be more confident about the effectiveness of their campaigns. In such a system, data on past creatives and past campaigns is crunched to determine precisely what would work for ongoing efforts. With this application of AI, brands can get a better sense of how everything from messaging, fonts, colors, imagery, button sizes, or formats impact overall campaign performance.

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It can also help advertisers see how their target audience responds to different creatives under different scenarios. For example, it’s possible that creatives with more color contrast perform better at night, or that ads that feature sports stars do best on the weekend. AI can provide this level of granularity and insights to ad creative development and performance.

The speed of AI can also help brands ensure that their ad creatives always appear in brand-safe environments. As programmatic media buying becomes more popular, one unfortunate side effect has been the rise of brand safety concerns, with a company’s creatives unintentionally appearing next to objectionable content. AI can help with algorithms designed to quickly spot and stop potential clashes between ad creative and in-app content before the ad is served.

ML can help such a system become smarter and savvier over time. The more campaigns it analyzes, the more it learns what creatives work well and what creatives do not.

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In this kind of system, ad creatives are broken down into disparate parts for analysis. However, such a system can sometimes fail to account for the whole picture. In particular, manual efforts or computer programs designed to break down an image are prone to error and can miss crucial details in the analysis stage. Human eyes can see the forest for the trees, but that’s not how computers work.

But, this may be changing very soon. Recent advances in the field of computer vision are yielding exciting results, and could bring major advancements to the analysis of ad creatives, among other fields.

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With computer vision, computers are trained to recognize and analyze whole images, not just individual parts and components. This is a much more comprehensive way to approach image and video analysis, and could soon lead to major breakthroughs. In the field of ad creative analysis, computer vision can help make predictive analytics even faster and more accurate.

According to eMarketer, brands and their advertising partners are expected to spend more than $77 billion in the U.S. alone in 2019. However, ensuring this money is spent wisely is another matter entirely. Thanks to current and future AI applications, brands can be sure the creatives they deploy are optimized for success and likely to yield tangible, quality results for their business.

Source: VentureBeat

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