Programmatic advertisers perpetually strive to create personalized and engaging experiences for their target audience. But with seamlessly endless amounts of data available to advertisers, it can be overwhelming to make sense of it all and determine how to use that data to improve targeting for a given campaign. Moreover, with ad fraud running rampant and as much as 42% of the internet’s traffic being bots according to Light Reading, advertisers are faced with the challenge of accessing new audiences while also aiming to avoid targeting bots and fake user domains. This is where machine learning can help.
Machine learning is a type of artificial intelligence that uses algorithms to analyze data and learn from it, improving its performance over time. In digital advertising, machine learning is becoming increasingly important as it helps to automate tasks, optimize campaigns, and ultimately, drive better results. In this blog, we’ll explore three main ways machine learning is being used to improve digital marketing in 2023 and beyond.
Personalizing Marketing Based on Past Activity
One of the biggest advantages of leveraging machine learning in digital marketing is the ability to personalize digital experiences based on a user’s past activity. By analyzing data such as search history, social media interactions, and website behavior, machine learning algorithms can identify patterns and make predictions about a user’s interests and preferences. This in turn can help programmatic advertisers more precisely target campaigns to individual users based on their expressed interests, rather than spraying a campaign at an outlet that you assume potential customers are following.
This allows marketers to deliver more relevant and personalized content, offers, and recommendations. For example, an online retailer can use machine learning to analyze a customer’s purchase history and browsing behavior to recommend products that are likely to appeal to them. By tailoring the shopping experience to individual consumers rather than by demographics or shared behaviors, marketers can increase engagement and drive sales.
Another way machine learning is improving digital marketing is through predictive analytics. By analyzing data on past customer behavior and campaign performance, machine learning algorithms can identify trends and make predictions about future outcomes. This information can then be used to predict which customers are most likely to take a particular action, such as making a purchase or subscribing to a newsletter.
For example, marketers can use machine learning to analyze data on past email campaigns to predict which subject lines, content, and calls-to-action are most likely to drive engagement and conversions. The ultimate outcome that machine learning drives is enabling marketers to optimize campaigns in real-time, thereby improving performance and the ROI they realize from deploying the campaign.
Fraud Detection and Removing Fake Data
One of the most utilitarian uses for machine learning in digital advertising today is using it to help detect and remove fraudulent data from identity graphs. As mentioned, the rise of ad fraud and fake traffic is placing major burdens on programmatic advertisers to ensure the data they’re using to target campaigns is representative of real customers or real potential customers, not bots. That in mind, it’s essential for marketers to ensure that their data is accurate and reliable by analyzing sources and vetting what the data represents. Machine learning algorithms can analyze data in real-time, detecting patterns and anomalies that indicate a fraudulent source or manipulated old data.
By identifying and removing fake data from their identity graphs, marketers can ensure that their campaigns are targeting real users, maximizing their ROI and minimizing wasted spend. This is particularly important in programmatic advertising, where automated bidding algorithms rely on accurate data to make informed decisions.
One of the best solutions on market for programmatic advertisers to remove junk data is BDEX’s CleanGraph™ – the largest & most accurate Identity Graph in the US, powering many of the top AdTech platforms in today’s ecosystem. BDEX’s CleanGraph™ improves performance by eliminating IDs linked to ad fraud, helping reduce ad fraud burden on marketer’s budgets and improve their return on ad spend (ROAS) by as much as 40%.
Whatever solution advertisers use to improve ROAS, machine learning will become increasingly important in digital marketing as it helps to automate tasks, optimize campaigns, and drive better results. By personalizing marketing based on past digital activity, using predictive analytics to make data-driven decisions, and detecting and removing fraudulent data, marketers can improve the customer experience, increase engagement and conversions, and ultimately, drive business growth. As the amount of data available continues to grow, machine learning will become an essential tool for marketers looking to stay ahead of the competition.
To learn more about how BDEX is using machine learning to improve ad targeting and programmatic advertisers’ return on ad spend, visit our Machine Learning archives here.