The Science Behind Customer Engagement: How Machine-Learning Drives Action

- The role of machine-based learning in personalization

- Tips to consider when selecting emotional language machine-based learning solution

- Case studies and results that can be expected when emotional language machine-based learning is applied to marketing and advertising

Thirty-eight percent of executives use machine-learning and predictive technology as part of their Data Management Platform (DMP) in advertising. Another 35% expect to implement within the next 12 months. Campaign Management solutions based on machine-learning are now common practice to help scale a marketer’s ability to segment audiences. For example, a platform can leverage behavioral data to find customers likely to cancel their subscription so action can be taken to mitigate losing a customer. The likes of Amazon, Facebook, and Google use machine-learning to recommend relevant products or content. Though theirs may be proprietary, machine-learning recommender systems are widely available and used across the internet. More on the cutting edge, Acquisio uses machine-learning to create a service they refer to as Bid & Budget Management. The platform monitors paid search campaigns, sets bids, adjusts bids for mobile, and ultimately achieves the lowest spend for the greatest outcome, all with minimal human input.