{"id":38383,"date":"2023-03-30T17:28:08","date_gmt":"2023-03-30T21:28:08","guid":{"rendered":"https:\/\/digital.hec.ca\/?p=38383"},"modified":"2023-03-30T21:23:08","modified_gmt":"2023-03-31T01:23:08","slug":"ai-driven-ad-optimization","status":"publish","type":"post","link":"https:\/\/digital.hec.ca\/en\/blog\/ai-driven-ad-optimization\/","title":{"rendered":"AI-Driven Ad Optimization: Advertise Smarter, Not Harder."},"content":{"rendered":"\n

In the realm of digital marketing, advertisers race against each other for attention. To arrive first at the finish line, they ought to use ad optimization to improve their campaign’s performance. However, they don\u2019t have to do it alone. Just like athletes, marketers can employ a qualified coach, in this case artificial intelligence, to outpace their competitors and succeed first.  In this article, you will learn how AI-driven ad optimization can be used in the highly competitive arena of digital advertising.<\/p>\n\n\n\n

Predicting & optimizing the future with machine learning<\/h2>\n\n\n\n

With artificial intelligence comes a remarkable tool that can predict the future: machine learning. This subfield of AI involves continuously analyzing large quantities of data and recognizing patterns to make valuable and accurate predictions. Not only can this method process more data than humans, it can also do it faster, more accurately and objectively.  Additionally, because data is subject to change, the algorithm can continuously adapt and be adjusted while also being monitored. The more data is fed to the algorithms, the more performant it will be to predict the outcomes.  In the context of advertising, the data training the algorithm could include, for instance,  market trends, past ad campaigns and user behavior.<\/p>\n\n\n\n

Now that we know about machine learning, how could it be leveraged to train advertising campaigns to sprint passed competition? Here are some approaches:<\/p>\n\n\n\n

1. Data-Driven Ad Bidding Strategies<\/h3>\n\n\n\n

Undeniably, ad bidding is a key element of ad optimization. Before artificial intelligence, marketers would rely on  manual processes and trial and error to set bids. Those moves would require constant monitoring and be adjusted according to the ad\u2019s performance indicators, like conversions.  Because of persistent changes in the marketplace, those adjustments would often be delayed or inaccurate. However, with machine learning, this process is automated, and the bids adjusted according to the algorithms and the vast amount of data it trained on. For instance,  Google offers automated bidding strategies, powered by advanced machine learning, called Smart Bidding<\/a>.<\/p>\n\n\n\n

2. Machine Learning-Powered Audience Segmentation<\/h3>\n\n\n\n

Likewise, machine learning can be leveraged by marketers to identify and segment target audiences that will help tailor the best advertising campaigns. While creating ad campaigns,  it is now easier to focus on the most accurate targeting parameters according to the data-proven appropriate audiences generated by advanced algorithms. For example, the IBM Watson Advertising Predictive Audiences solution<\/a> uses AI technology to predict which user is most likely to take the anticipated actions. Purchase history, media habits and customer demographics are among the type of data they manipulate.<\/p>\n\n\n\n

3. AI-driven Ad Personalization<\/h3>\n\n\n\n

With that being said, ad personalization is also made easy with artificial intelligence. Specific user data can be utilized to create personalized recommendations. For instance, Amazon Web Services is behind the machine learning service called Amazon Personalize<\/a>. Marketers can benefit from real-time personalized promotions recommendations curated specifically for their customer segments by the advanced algorithms<\/p>\n\n\n

\n
\"Path
This diagram illustrates the way Amazon Personalize<\/strong> exploit promotions through recommendations.<\/figcaption><\/figure>\n<\/div>\n\n\n

4. Data-driven Creative Decisions<\/h3>\n\n\n\n

Finally, machine learning can even be used to predict and adjust creative elements for ad campaigns optimization.  Indeed, marketers can trust algorithms to make creative decisions, such as the colors,  the design, the fonts, and the call to action, to improve advertising decisions. For example, IBM Watson Advertising Accelerator<\/a> offers AI-driven creative optimization, to find the optimal combination of creative variations based on data signals like target audiences. <\/p>\n\n\n

\n
\"Various
With these options of ad creatives, IBM Watson Advertising Accelerator\u00a0<\/strong>will train and be able to predict the most performing image to advertise, according to factors like the target audiences.<\/figcaption><\/figure>\n<\/div>\n\n\n

Conclusion: Use<\/strong> AI-Powered Ad Optimization<\/strong> for a Competitive Advantage <\/strong><\/h2>\n\n\n\n

Without a doubt, Artificial intelligence is valuable for advertisers wanting to develop their ad optimization. By automating processes like ad bidding, identifying targeted audiences, personalizing recommendations, and optimizing creative decisions, marketers have more chances to win the advertising race and all the attention with AI-driven ad optimization<\/a>. <\/p>\n","protected":false},"excerpt":{"rendered":"

In the realm of digital marketing, advertisers race against each other for attention. To arrive first at the finish line, they ought to use ad optimization to improve their campaign’s performance. However, they don\u2019t have to do it alone. Just like athletes, marketers can employ a qualified coach, in this case artificial intelligence, to outpace […]<\/p>\n","protected":false},"author":1169,"featured_media":38657,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[651],"tags":[1470,1474,1468,1413,1469,1475,1471,1323],"class_list":["post-38383","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-marketing-technology","tag-ad-bidding","tag-ad-optimization","tag-ad-personalization","tag-ai-2","tag-audience-segmentation","tag-data-driven-decisions","tag-digital-advertising","tag-machine-learning-en"],"acf":[],"yoast_head":"\nAI-Driven Ad Optimization: Advertise Smarter, Not Harder.<\/title>\n<meta name=\"description\" content=\"Explore how AI-driven ad optimization can be used to gain a competitive advantage in the rapidly evolving world of digital advertising.\" \/>\n<meta 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