Amplero adds machine learning to help pinpoint and leverage influential customers

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.

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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.

Of course, as with all of these technologies, it is easy to cross the line from personalized to “creepy” once you start detailing and leveraging behavioral data. Amplero is clear about its objectives in this area of marketing.

“At the end of the day, the enterprises using Amplero are doing nothing more than encouraging, and in many cases incentivizing, their most socially connected customers to do things like ‘refer a friend,'” Fleckenstein said. “Referral programs have been around for decades.”

While traditional influencer marketing techniques tend to focus on celebrities and “internet famous” individuals, Amplero’s approach and machine learning capability help to identify those regular consumers who also happen to be influential to the brand using the solution. Is this a better, more natural approach than employing the services of more traditional types of influencers? Fleckenstein thinks a combination of both methods works well.

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“We certainly don’t see it as an either/or type of decision,” Fleckenstein said. “There are many brands out there who have benefitted from having well-known influencers and celebrities endorse their brand. As we enter 2017, we see a strong continuation of the trend to re-allocate non-addressable marketing spend (television, print, OOH, etc.) to addressable marketing (email, search, call center, etc.). If this trend continues, I think we’ll see more and more dollars shifting from celebrity influencer strategies to social influencer ones.”

Amplero’s new machine learning-driven Influencer Optimization capability is available to Amplero Intelligence Platform customers from today.