Amplero adds ML to pinpoint & leverage influential customers

January 19, 2017

PUBLISHED BY Stewart Rogers

SOURCE VentureBeat

AI and machine learning (ML) are set to pervade the marketing technology universe throughout 2017. With billions of marketing touchpoints to learn from, marketing technology is a natural home for ML capabilities.

And today, Amplero has announced its Influencer Optimization capability, powered by machine learning and offered as part of its Intelligence Platform. The new addition makes it possible to not only discover your most influential customers but also to understand the actions they are taking and how to optimize your connections with these valuable advocates.

So how does it work, and why is machine learning particularly suited to this optimization and identification process?

“When we engage with a new B2C enterprise, the first thing that we do is leverage all of the rich contextual data (persona data, marketing data, point-of-sale data, product/app usage data, etc.) to build a historical, longitudinal view of each customer,” Matt Fleckenstein, chief product officer at Amplero, told me. “In doing so, you get a deep view into changes to a user’s state, such as how a given user’s product/app usage patterns are changing and evolving over time.”

That level of data and detail is fuel to machine learning’s fire.

“Because customers’ worlds are increasingly connected, there are natural social networks that develop based upon who they play online games with, the people they call or text, or those whom they collaborate with to create or edit a document,” Fleckenstein said. “We can now leverage the power of machine learning to identify and influence these relationships to move beyond 1:1 marketing.”

The company has backed up claims about how well its new capability works by teaming up with researchers from the Columbia Business School and HEC Paris. After studying data from nearly 6,000 mobile customers, it found that the ripple effect of personalized marketing campaigns on non-targeted consumers within the targeted consumer’s network caused a 28 percent lift.

Read the full article on VentureBeat here.