Marketing In The Age Of Machine Learning

Franki Chamaki,

There is no longer a delineation between digital and traditional customers. Anyone with a smartphone traverses between online and offline activities without a second thought. As they do, they gain a penchant for modern conveniences, such as speed, utility and real-time assistance. Along the way, they also become more and more impatient and demanding. To engage today’s customer takes a modern approach to marketing where advanced technologies and customer optimization set the stage for what I call “adviser brands.” And, they’re changing the game for everyone.

Adviser brands represent a shift away from a traditional focus on top-of-the-funnel campaigns and marketing-centric metrics. Now, leading marketing are using the likes of machine learning and other emerging technologies to deliver assistive experiences that converts intent and expectations to value-added engagement at every step of their purchase journey. Doing so drives more than marketing performance. Adviser brands are also contributing to business growth.

5 Rules for Winning with Automated Marketing

Earlier this year, I had the opportunity to interview CMOs and CDOs of leading brands who shared their work of what it takes to be relevant today. One of the common threads was that machine learning was the cornerstone to a new marketing construct. It helps marketers convert everyday digital signals to understand customer intent and preferences to automate the delivery of personalized, useful and productive experiences at the right time, in the right place and on the right device…at scale.

Google recently published a list of “5 rules for winning with automated marketing,” in which the authors, Nicolas Darveau-Garneau, chief search evangelist at Google and Adam Deif, head of industry, also discuss the advantages of machine learning. In their article, they share the five best practices among top performing brands using machine learning. The following is inspired by their list..

  1. Optimize marketing for growth instead of efficiency

As Google so rightly puts it, “machine learning is only as good as what you ask it to optimize.” In my discussion with Julie Rieger, 20th Century Fox Film president, chief data scientist and head of media on the subject, she shared how one of the biggest challenges to aligning machine learning with meaningful insights is breaking free of the cognitive biases marketers often bring to table. Many times, they tend to apply legacy-based mental models and measures that prevent marketers from seeing new opportunities for marketing. They exchange investments in innovation and growth for scale and efficiency. On the other hand, modern marketers are using machine learning to focus on growth by taking a holistic view of the customer and reimaging marketing to deliver against evolved expectations. I also spent time with David Baekholm, senior VP of growth marketing at HomeAway, and he shared similar ideas. In our conversation, he discussed how machine learning and a focus on mobile customers and long-term performance over short-term ROI helped the company increase revenue by over 115 percent year over year.