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How Smart Advertisers Are Using ML and AI to Optimize Campaigns

an agency using Machine Learning and Artificial Intelligence

The rapid evolution of the advertising marketplace has made it practically impossible for humans to optimize campaigns on their own. To complicate the situation even more, the auction-driven nature of the market means that most leading advertisers must place hundreds, if not thousands, of bids and then change those bids multiple times daily. Not only does this impact a brand’s performance, but actions by competitors play a role as well. For example, an ad campaign might be doing exceptionally well today and suddenly plummet tomorrow simply because a competitor has increased its bids.

As a result, more and more advertisers are turning to artificial intelligence (AI) and machine learning (ML) to drive better marketing results. But AI and ML are still relatively new technologies. As such, marketers can make critical mistakes and fail to capitalize on the technologies’ many benefits. At a time when competitor campaigns, new product launches, and even seasonal changes can heavily swing advertising performance, it is imperative for advertisers to understand how to get the most from AI and ML.

Leveraging AI to Better Manage Big Data

an agency making the most of Machine Learning and Artificial Intelligence
Is your agency making the most of Machine Learning and Artificial Intelligence in its campaigns?

There is no escaping the fact that this is the age of big data. Experts say that people produce 2.5 quintillion bytes of data every day. In other words, it is estimated that if all the data produced daily were put into book form, it would fill enough books to stretch from the Earth to the moon. With so much data being produced, it’s no wonder that advertisers are turning to AI and ML to manage it better.

Advertising is nothing if not data intensive. The more data a company has, the more it can fine-tune and target its advertising messages to reach its target audiences. Data insights allow companies to effectively change campaign parameters and drive maximum return on ad spend (RoAS). It’s no wonder companies have been quick to embrace the technology. Influencer Marketing Hub reports that 61 percent of marketers are now using AI, and 54.5 percent of all marketers believe AI can bolster their marketing efforts.

Google and Facebook use AI to analyze user data and show relevant ads. Smaller companies also benefit from AI and ML by using it in their cost per mille (CPM) and cost per click (CPC) advertising campaigns. AI allows companies to better understand platform performance, discover campaign anomalies, identify seasonal and other viral trends, harvest keywords for future campaigns, and better understand customer preferences. Another benefit of utilizing AI is that it allows companies to easily combine advertising data with marketplace data to make their campaigns—including cross-sells, upsells, and retargeting ads—even more targeted and successful.

Other prominent companies are also heavily into AI use. For example, AI and ML have dramatically influenced Netflix’s success. The company’s AI technology has provided customers with personalized viewing recommendations based on their preferences and past behavior. Research shows that 80 percent of the programs watched on the streaming service are now from its personal recommendations. Amazon is another big company that uses AI to dominate the marketplace. In addition, it relies on AI machine learning tech to make product recommendations to customers based on their past purchases and browsing history.

How Companies of All Sizes Can Put AI and ML to Work for Them

The first step for companies seeking to benefit from this new technology is to implement an AI/ML-based optimization platform. This platform can be built in-house or licensed from ad agencies such as Kenshoo, Teikametrics, and Pacvue. Once the platform is in place, companies can use AI to automate campaign actions. For instance, if a company’s RoAS is high but impressions are low, they can use AI to increase the bids to a level where the RoAS does not drop more than 10 percent. They can also utilize AI to better understand which keywords drive conversion and then increase bids on those keywords.

The insights generated by AI enable companies to stay on top of their marketing performance and monitor the competition at the same time. Advertising bid auctions are dynamic, and a company’s performance does not depend on its actions alone. Competitor actions and customer trends heavily affect campaign performance. AI and ML allow companies to quickly spot trends, such as seasonal changes, new product launches, and even surprise weather patterns capable of heavily swinging campaign performance. Thanks to AI, companies are able to act proactively instead of reactively to maximize profits instead of constantly playing catch up with the marketplace. That’s why companies should consider AI a long-term investment and a key part of their ongoing success. Properly utilizing AI can mean the difference between a company achieving or even surpassing a campaign goal or falling short.

Key Strategies for Getting the Best Out of AI

When implementing AI and ML in marketing, there are key strategies companies need to incorporate to increase their chances for success. The first is to recognize that every data point is crucial. That requires saving all data from past campaigns to drive future insights. Second, companies need to leverage cross-platform data. For example, a trending keyword on Google Search could be used to drive performance on Amazon or Walmart.

Other critical success strategies include combining data generated through other business operations with platform data to obtain a more comprehensive picture of the marketplace. Companies also need to practice patience. AI algorithms take time to mature. It is essential for companies to be consistent in their training cycles, focus on continual improvements, and update their AI algorithms every month or quarter.

Avoiding Common Mistakes Is Crucial

Another key to increasing the likelihood of success with AI and ML is to recognize common mistakes and how to avoid them. Many companies generate valuable insights but do not leverage them for campaign setup. This is why it’s important to always take advantage of the insights AI produces – for example, much of the success Starbucks has enjoyed can be traced to its use of AI predictive analytics to deliver personalized marketing messages to customers.

people rising above with Machine Learning and Artificial Intelligence
Both Machine Learning and Artificial Intelligence can give your agency the edge it needs to rise above your competitors.

Another common mistake companies make is not leveraging A/B testing to understand various trends. The good news is that while A/B testing used to be very time-consuming, AI has made it faster and easier. As an example, AB Tasty, a customer experience optimization and feature management company whose clients include USA Today, Disney, and Calvin Klein, uses AI in its platform to automatically send visitors to the winning test variation once it’s statistically viable. That means maximum results with minimal effort for A/B testers.

Many companies also neglect to set up anomaly detection to identify and understand performance drop. In addition, companies frequently fail to invest in infrastructure, data scientists, and other resources that can boost AI performance.

Realizing New Levels of Success with AI

All marketing campaigns have a specific goal, whether a particular conversion rate, specific RoAS, or more expansive reach or category coverage. AI algorithms leverage data to help marketers better understand which keywords, products, and bids are meeting those goals and which are not. Advertisers can then make changes based on that information to fine-tune their campaigns and produce better results. In other words, advertisers looking to get the best “bang for their buck” and respond quickly to market changes need to utilize AI. Those not taking full advantage of AI risk falling behind and becoming irrelevant.


About the Author:

Someshwar Bindu is a director of product – AI/ML at a major e-commerce company. He has extensive experience in AI, advertising, e-commerce, and product management. Someshwar holds an MBA from the University of North Carolina and completed the Product Management in AI Era program at Stanford University and the Post-Graduate Program in Artificial Intelligence for Leaders at the University of Texas. For more information, contact

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