Tony, to kick things off—can you please share a bit about your professional journey and what brought you to lead Amperity?
I’ve always wanted to work in customer data. My career has been dominated by two companies, 10 years at Salesforce and 10 years at Oracle. During these experiences, I started to see how powerful customer data was within the technology layer.
When I was approached about the opportunity at Amperity, I admit I was instantly intrigued as it has a huge success story. Amperity’s co-founders, Kabir Shahani and Derek Slager, had started Amperity in 2016, and I felt particularly drawn by the co-founders’ value proposition. Their efforts were anchored in the idea that without proper identity associated with customer data, other efforts would be less valuable. We continued to stay focused on solving that problem to unify customer data.
What typically takes 5–10 years to build, Amperity’s founders accomplished in just 2–3. I’ve always wanted to be around customer data, and when I looked at Amperity’s history, I started to be intrigued by the metrics. I kept asking myself what Amperity would look like inside an AI-native architecture—and I was excited by the possibilities.
What gap in the market were you aiming to fill with the Identity Resolution Agent, and how did that shape the product’s development?
Without identity, everything built on customer data—personalization, targeting, insights—is on shaky ground. Identity resolution is fundamental to the modern customer data priorities that organizations have. We often hear companies talking about unifying customer data, and they are sometimes less aware that identity resolution is foundational to that.
We have always had a real focus on solving customer identity across different platforms because of the challenge companies have with data readiness. So, while many companies want to deploy natural language AI tools like LLMs and agents, their mismatched and messy data is a real bottleneck.
Earlier this year we unveiled Amperity’s Identity Resolution Agent, which is the industry’s first AI agent purpose-built to tackle the messiest, most time-consuming part of customer data management. What makes this different is that we’re using AI to accelerate what used to take months of complex engineering work down to just hours.
The agent brings together several key capabilities that shaped its development. We’ve built AI-led data preparation that eliminates complex coding and tedious iteration. It offers multi-dimensional identity resolution, blending deterministic and probabilistic matching strategies so businesses can choose between high-confidence operational identities or broader marketing audiences based on their specific needs.
The results have been remarkable. One leading retailer discovered 3.5 million previously unreachable customer emails. The real opportunity here is that while everyone else rushes to layer on AI personalization, the smarter play is getting your customer data foundation right first.
You’ve said that “AI is only as good as the data that fuels it.” Why is that message more important than ever in today’s business landscape?
The more a company knows about you, the more it can give you guidance. That’s true whether you have a one-on-one relationship with a business owner or if your engagement with the company is analysed within a platform.
The challenge today is that customer data is exploding from every direction, your mobile app, website, loyalty program, customer service platform, each with their own way of capturing data and defining customer identity. So brands are struggling to collect it, store it and keep it clean and cohesive.
The reality most teams are finding themselves in is that they are suffering from fragmented customer profiles that make even the fanciest AI and personalisation investments miss the mark.
With our Identity Resolution Agent, we’re empowering data teams to create unified, trusted customer profiles in record time, giving brands the clarity and agility they need to turn AI investments into real business outcomes.
The new agent promises to turn data prep from a months-long process into something that happens in hours. How does it deliver on that promise?
The real opportunity lies in applying AI to the complex, tedious work of customer data stitching first. That’s exactly what we’ve done.
We’ve pointed AI directly at the foundational customer data problem. Our Identity Resolution Agent tackles the complex, time-consuming process of data stitching, tasks that once required months of complex engineering work can now be completed in hours.
This dramatic acceleration comes from eliminating the need for complex coding and tedious manual iteration. The AI handles data standardization and matching automatically, quickly connecting fragmented customer identities into accurate, unified profiles. We’ve also built a patented Sandbox environment that allows teams to test changes in isolation without disrupting production, so you can experiment and refine your approach without the usual risks and delays.
The agent integrates seamlessly with modern data architectures like Databricks and Snowflake, meaning teams can get up and running quickly without overhauling their existing infrastructure.
Can you talk about how combining deterministic and probabilistic matching creates better identity resolution for different business needs?
In deterministic matching, the matches are an exact match of the values, and the rules are simple and minimal. It’s predictable but ultimately insufficient for most use cases beyond operational ones like payment processing. It’s straightforward but results in a “garbage in, garbage out” problem when data doesn’t conform perfectly.
By combining this with probabilistic approaches, we can handle the messy reality of customer data. For instance, probabilistic matching can account for variations in how people enter their information, like typos or nicknames, using fuzzy matching algorithms.
At Amperity we’re focused on solving identity challenges across multiple systems, and this combination gives businesses flexibility: they can use high-confidence deterministic matching where accuracy is critical (like financial transactions) while leveraging probabilistic methods for broader marketing audiences where capturing more customers matters more than perfect precision.
The key is that different business needs require different approaches. A payments team needs 100% certainty, while a marketing team might prefer casting a wider net. By blending both methods, businesses can tune their identity resolution to match their specific requirements rather than being forced into a one-size-fits-all solution.
Even the most advanced AI can’t deliver meaningful experiences if it doesn’t understand who it’s speaking to. Personalization that works at scale and in real time, starts with trusted, unified customer data. Identity resolution isn’t just a technical fix; it’s what makes AI contextual, timely, and valuable.
One of the success stories came from New Look. What were your thoughts, Tony, when you saw those results in action?
It’s been great to see. We’re thrilled to be partnered with New Look, and the joint solution enables them to connect data across all customer touchpoints, online and in-store, to better identify high-value customers and activate insights in real time.
The results from the pilot indicated there would be good returns within the first quarter. This comes down to more efficient marketing campaigns, improved audience targeting, and increased cross-channel conversions.
New Look has been really quick to turn customer insights into action. We’re already seeing that customers with multiple identifiers spend almost twice as much and place twice as many orders. That’s a fantastic result and it demonstrates the business impact of great execution in combination with unified customer data.
As a leader, how do you personally approach strategy and decision-making during rapid innovation cycles?
Culturally, I’ve always been drawn to companies that balance performance with a strong learning mindset. I view it as an enabling culture. Are you enabling progress and growth? We are very much in the idea “I want to create a learning culture that creates an enabling culture.”
When it comes to using agents in the workplace, most companies are going to view it as a productivity play. I don’t necessarily view it this way, as they are systems that should have been automated anyway. Data and how agents get used have three elements: what does data mean contextually? Is it actionable? And lastly, data is perishable.
For companies just beginning to tackle fragmented customer data, what’s one key piece of advice you’d offer?
In order to enable growth and scale, you need to be able to engage with customers from a unified data foundation. Without unified data, personalization misses the mark as it lacks contextual influence.
I think the following considerations are important:
- Creating customer journeys that are guided but not forced
- Capturing multi-channel engagement data to understand customer preferences
- Using AI to analyze data contextually and make it actionable
- Focusing on understanding individual customer behaviors and schedules
- Providing personalized experiences that fit into the customer’s life, not disrupt it
The key is to use AI and data to create experiences that feel tailored and meaningful, where customers feel understood without feeling invaded. Companies should aim to narrow the focus of recommendations and interactions, making them more precise and relevant to each individual’s needs and preferences.
The ultimate goal is to create a personalized experience that becomes a competitive moat, making customers more likely to return and engage with the brand repeatedly.
Finally, what’s next for Amperity—and how do you see the Identity Resolution Agent shaping the future of AI-powered customer experiences?
In AI terms, it’s both exciting and uncertain. We’re unlocking more insights than ever from the data layer, but also entering a world where machines are asking machines questions.
Machines can now process more information than ever before. Over time, we’ll see domain-specific models—like those for customer data—emerge to deliver more precise and contextual insights. That’s where we see Amperity’s Identity Resolution Agent playing a key role—giving AI a trusted data foundation to work from, and helping brands make smarter, faster, more human decisions.
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Tony Owens, CEO of Amperity
Tony brings over 20 years of experience in executive roles at Salesforce and Oracle, where he scaled enterprise operations and global sales. Prior to Amperity, he was the president of worldwide field operations at LivePerson overseeing the company’s go-to-market strategy and teams. Before LivePerson, he had progressive responsibilities running Salesforce’s field operations in the Americas and was a member of the executive committee. Owens also previously served as group vice president at Oracle.
Tony serves on the board of directors for The Mandatum Foundation, SnapLogic, Pathlight, and CIPIO.ai. He attended BYU and lives in the Bay Area with his wife of 28 years and their four kids. Tony is a passionate supporter of helping veterans transition to the civilian workforce, Juvenile Diabetes Research Foundation (JDRF), as well as other charitable foundations. linkedin
About Amperity
Amperity’s Customer Data Cloud empowers brands to transform raw customer data into strategic business assets with unprecedented speed and accuracy. Through AI-powered identity resolution, customizable data models, and intelligent automation, Amperity helps technologists eliminate data bottlenecks and accelerate business impact. More than 400 leading brands worldwide, including Alaska Airlines, DICK’S Sporting Goods, BECU, Planet Fitness, and Wyndham Hotels & Resorts, rely on Amperity to drive customer insights and revenue growth. Founded in 2016, Amperity operates globally with offices in Seattle, New York City, London, and Melbourne. For more information, visit amperity.com or follow us on LinkedIn, X, Facebook and Instagram.
