How machine learning can influence the customer journey

Kristina: What does the future hold for journey mapping? What do you see replacing the tools that enable it?

Glenn Pingul, VP of Scientific Marketing Strategies at Amplero: A customer journey map tells the story of a customer’s experience interacting with a company, starting with the initial contact and continuing through all interactions. It’s well suited to B2B marketing, but when you look at what enterprise B2C marketers are trying to achieve with marketing personalization, you’re talking about an entirely different degree of scale. The reality is that in a hyper-connected world, you can’t possibly develop enough journey maps to predict the infinite number of ways consumers interact with your brand, and then personalize and optimize every customer interaction as behavior dynamically changes. This is where machine learning comes in.

Kristina: What will this next wave of marketing technology investment mean for the marketer?

Glenn: Essentially the marketer won’t have to manually turn all of the dials trying to decide which offers should be executed to whom, when, and how. Instead marketers will rely on machine learning to discover the conditions and contexts for optimal targeting. This will mean that the marketer is no longer limited by the number of experiences that can be explored or the number of decisions that can be made or the number of individual customers that can be treated. Marketing personalization and optimization will easily scale to many millions of customers, and marketers will gain much better insight into what drives behavior and response to impact performance.

Kristina: How will the process of running customer marketing campaigns change and who will do what exactly in this new world?

Glenn: With machine learning, the entire process of marketers manually developing and managing targeting rules goes away. Marketers no longer have to perform the heavy analysis upfront to define which segments are appropriate for which offers. Likewise they don’t have to manually design each test and hypothesize which attributes impact performance. And they don’t have to rely on business intelligence teams to measure the performance of each campaign.

Marketers still guide the strategy and set the guard rails for a specific campaign in term of offer eligibility, for example. Then the machine tests thousands of variants of marketing messages, and automatically determines what works and what doesn’t to drive continuous optimization.

This allows marketing teams to focus on what they really enjoy which is the creative part of developing strategy, message, incentive, etc. rather than on the operational tasks of creating targeting lists and determining what works.

Kristina: What results can marketers expect with a machine learning approach to marketing?

Glenn: Marketers can expect to increase customer engagement across the entire lifecycle by getting to true one-to-one personalization at scale. With Amplero, where machine learning is really at the heart of B2C marketing automation, our enterprise clients are achieving great results when it comes to increasing customer lifetime value – gaining 3-5% incremental growth in customer revenue and experiencing 5x retention lift, for example. What drives these results is the ability to achieve new scale and precision when it comes to targeting, and the ability to optimize each and every customer interaction.