2019 Trends: Targeted Advertising, Machine Learning and Artificial Intelligence

In terms of improving the overall effectiveness of targeted advertising, machine learning (ML) and artificial intelligence (AI) hold a lot of promise. While machine learning systems have been all the rage already in 2018, we believe machine learning algorithms will become an even bigger trend with mobile in-app advertiser campaigns in 2019.

Note: This is the fifth post in our series on top trends for 2019. Our first post was on transparency, the second predictions post focused on OTT, our third post talked about in-app header bidding and our fourth blog discussed data monetization. Stay tuned for one more blog post all about what leading figures in the mobile advertising and marketing space think will become pervasive in the new year.

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Potential Benefits of AI and ML in Mobile Advertising

In 2019, here are just some of the ways in which ML and AI will help maximize lift and improve ad performance across the board.

For starters, AI can go a long way towards ensuring that ad creatives are the right fit for any target audience. Through advanced computer vision algorithms and extensive historical performance data, brands can determine what precisely it was about a particular ad creative that worked well in the past, and predict with greater certainty what kinds of creatives will perform well in the future.

“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,” Rajiv Bhat, Senior Vice President of Data Sciences and Marketplace at InMobi, wrote in VentureBeat in November. “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.”

Further, using AI in conjunction with location data can help improve the effectiveness of local campaigns. Through a combination of both greater data granularity plus smart insights on who frequents particular locations at given times and days, mobile marketers can more effectively find, reach and talk to their target audiences.

For example, let’s say a grocery store chain wanted to run a smart shopping campaign. While they can run ads to people already at or near their locations, that wouldn’t capture new visitors. But, using ML algorithms with advanced location data, they could determine where their ideal weekend shoppers are likely to be spending their time during the week, and then be sure they see their messaging at or near these locations.

AI and ML can also help brands better manage and mitigate brand safety concerns, which have been a growing worry especially among programmatic media buyers. In real time, ML algorithms can learn to determine what kinds of specific placements are brand safe or not for a particular advertiser, and thus ensure that brands stay out of hot water without blacklisting entire properties - and their potentially vast audiences.

“We see the limitless potential for machine learning in digital advertising. Current advancements are blurring the lines between human and machine as evident in applications like sentiment analysis – machines are increasingly able to identify and categorize the opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or neutral,” says Harmon Lyons, Senior Vice President of Global Business Development at IAS. “Nuance here is always evolving as language expands and includes things like sarcasm and emoticons to express meaning. Rapid advances in deep learning are allowing computers to process images and video in a more human-like way. These advancements will enable a more robust generation of brand safety and fraud solutions that will help to protect digital advertising investments.”

Conclusion

Throughout this year, few trends were as widely talked about - and hyped - as ML and AI. In the realm of mobile in-app advertising, we expect that to continue to be the case throughout 2019.

“Machine learning will continue to play a huge role across ad tech in 2019,” Lyons predicts.

Do you think AI and ML will be trending yet again in 2019, or are they all hype? Either way, let us know your thoughts on social media! We’d love to hear from you on LinkedIn on Twitter.