Programmatic advertisers in 2022 face new challenges in understanding their audiences with data. Machine Learning (ML) is one aspect of digital advertising that has fundamentally changed the ways marketers deploy their campaigns.
What once required manual labor to compile data, organize it into spreadsheets, segment the information into usable categories, and analyze that data to make informed decisions about advertising – has become all but obsolete. This is thanks to the scalability and efficiency of ML data segmentation.
In order to understand the dynamics by which advertisers deploy their digital ad campaigns in 2022, advertisers must first understand how Machine Learning can assist in more accurately leveraging their data to better target campaigns and see an increased return on ad spend (ROAS). Here are a few key principles advertisers should keep in mind as they aim to leverage Machine Learning:
What is Machine Learning?
Before discussing how Machine Learning is changing the way advertisers understand audience data, let’s first define ML as a concept. Considered a facet of the Artificial Intelligence (AI) family of technological advances, Machine Learning finds trends and patterns to imitate the learning and behavior of humans. It is designed to offer projections and data when used in the advertising industry. Through evolving algorithms, ML is helping marketers compile data, learn from it, and produce predictions for future marketing efforts.
What Can Machine Learning Do for the Advertising Industry?
When it comes to the advertising industry, marketers can do a lot by applying Machine Learning to their data sets. For example, if you watch a few videos on YouTube on how to change your refrigerator’s water filter, you may notice that the next time you open the app on your phone or computer, it recommends future videos to watch on minor refrigerator repair and maintenance. This is a prediction on consumer needs and wants based on their past digital activity. And, it’s the basis for how Machine Learning gathers data on audiences and helps marketers better target them with relevant content.
As ad targeting technology advances with a slew of predictive data, the advertising industry has much to gain through machine learning. Formerly manual processes of compiling and analyzing data through human intervention can now be automated through machine learning. It helps advertisers more easily deploy campaigns at scale.
Machine Learning Boosts Ad Performance
Boosting the ad performance of any campaign starts with a deeper understanding of the audiences the campaign is targeting. One of the best ways to understand your target audience is by collecting demographic data. Demographic data is crucial to optimizing ad spend budgets. However, when sold through a third-party vendor, much of this data can contain bots, junk emails, and other fabricated information. This does not actually result in your ad being shown to any real person. Here’s where machine learning comes in.
Machine learning creates a streamlined process to cut through junk and organize your data into tangible segments to help programmatic advertisers better target and adjust their campaigns. When an advertising team understands their audience completely and knows where, when, and how those ads should be placed based on tracked data, the results are more aligned with financial goals.
Advertising Cost Reduction with ML
Another way programmatic advertisers can avoid wasting their marketing budgets on mistargeted ads is by using a deterministic data approach. As opposed to probabilistic data – which makes educated guesses of which identities are connected – deterministic audience data operates on known identity connections, meaning that advertisers can be sure that the audiences they are targeting are not only real people, but more specifically the people they intend to target.
Why waste your budget on ads targeted to the wrong audience at the wrong time in the buyer’s journey? Smarter targeting stems from thoroughly vetted data. It can be acquired and compiled by applying machine learning to deterministic audience data.
ML Reduces Human Error
The accuracy and scalability of Machine Learning algorithms gives programmatic advertisers an unprecedented opportunity to increase efficiency of campaigns. Removing the need for human intervention with data collection and prediction dramatically increases the likelihood of that information being accurate. It also paves the way for optimal advertising results.
For programmatic advertisers looking to upgrade their ad targeting, use tools, like Omni IQ, to better segment audiences by applying machine learning to advertising data. To learn more about how to resolve identities and better target your ad campaigns, visit: https://www.bdex.com/enterprise-solutions/omni-iq/
