What’s next for machine learning in advertising?

As technology advances, machine learning will be able to make increasingly better correlations, such as understanding how the audience on one social platform corresponds with the audience on another and possibly reveal new audiences that were veiled from view.

Traditional analytic models are less effective with a high volume of data. Machine learning can easily analyse a high volume of data across different social platforms. The speed and efficiency of data analysis by machine learning means trends in customer behaviour are discovered more easily and campaigns can be optimized better.

On an ad level, machine learning predicts the success of each ad based on behavioural and contextual attributes and performance data. It defines the target audience and determines the bid for each ad. Juniper Research finds that machine learning algorithms used to drive efficiency across real-time bidding (RTB) networks will generate USD42 billion in annual ad spend by 2021, up from USD3.5 billion in 2016.

Machine learning has the potential to vastly increase the efficiency and speed of data analysis, increasing the performance of ad campaigns, cutting ad spend, and support human infrastructure. Simply put, machine learning means greater innovation, less stress and better results in less time.