How Amperity Uses Machine Learning To Unlock Data and Supercharge Marketing

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“Nobody was using machine learning to point at the underlying consumer data to help make sense of it and bring it together,” says Matthew Biboud-Lubeck of Amperity. “We put together cloud computing that was scalable with better economics alongside a machine learning algorithm that we were pointing at the data to help make sense of it. We realized that what we had was a pretty scalable solution to help brands get to that nirvana of a single view of the customer.”

Matthew Biboud-Lubeck, VP of Strategic Services at Amperity, discusses how their platform helps brands create a complete view of their customers in an interview on the B2B Growth podcast:

Helping Brands Create a Single View of Their Customers

We are a CDP (customer data platform) based in Seattle that is helping brands create a single view of their customers and to unlock personalized experiences from that data. If you look back to the founding of Amperity about three years ago our founders were canvassing the marketplace. What you saw was a marketplace using a lot of buzzwords but having a lot of trouble executing them. You heard about personalization, customer 360, and a 360 view of the customer. Marketers across major consumer brands were super frustrated.

They spent a fortune trying to cobble some view of their customer. They invested in technology to help them send better emails, to make their media more targeted, and to unveil better analytics. All of those tools that they have invested in talked about the notion of a single view of the customer because they fundamentally needed that to operate. The reality was that nobody was getting to the solution. We came in to say maybe there is a better way.

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Machine Learning Helps Brands Get To Nirvana

There were two things that changed in the marketplace that we capitalized on. First of all, it was that cloud computing got a lot cheaper. It used to be that if you were a big brand and got hundreds of millions of customer interactions, it’s just a lot of data. Part of the reason that no one was able to create an easy solution to putting that all together was because it was cost prohibitive.

The second really interesting evolution in the market is that machine learning has become much more mature. What we found was that everyone in the marketplace was using machine learning to make that last mile to the marketer a little bit better. It was used to decide which products to show a customer or to decide which offer to show a customer or to create a customer care solution that’s automated. You go online and type toward a solution and some bot talks back to you. Nobody was using machine learning to point at the underlying consumer data to help make sense of it and bring it together.

We put together cloud computing that was scalable with better economics alongside a machine learning algorithm that we were pointing at the data to help make sense of it. We realized that what we had was a pretty scalable solution to help brands get to that nirvana of a single view of the customer. That’s how we were born. What’s interesting is that the customer data platform space is a little bit confusing. You have a lot of companies that started as something else that rebranded as a CDP. We were purpose-built from the ground up as a customer data platform designed to bring all of a brands data, reconcile that data to create a notion of identity on it and then to unleash that data back to the brand anywhere that they want to use that data.

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