Adam Greco of Hightouch discusses AI’s impact on CDPs, shifting from data collection to activation, and the rise of composable architectures for customer intelligence.
In an era increasingly defined by data, where a profound understanding of the customer has become the ultimate differentiator, Adam Greco stands as an unparalleled authority. As the esteemed Product Evangelist at Hightouch, Greco has not merely observed but actively shaped the entire trajectory of digital analytics, from its foundational focus on campaign performance to the sophisticated, holistic, and AI-driven approaches that define today’s landscape.
In this exclusive and insightful interview, Greco offers his invaluable perspectives on the profound, transformative power of Customer Data Platforms (CDPs), particularly as they integrate more deeply with artificial intelligence. He delves into the disruptive potential of AI Decisioning, a groundbreaking innovation that promises to revolutionize personalized customer engagement. Furthermore, Greco shares critical advice on the essential mindset shifts organizations must embrace to transition effectively from mere data collection to genuine data activation, ensuring that data translates directly into tangible business value. Join us as we explore the evolving frontier of customer intelligence with a true visionary, uncovering the strategies and technologies poised to redefine how businesses connect with their customers.
Full interview;
Having witnessed the evolution of digital analytics firsthand over two decades, how do you envision the role of customer data platforms (CDPs) evolving in the next five years, particularly with the increasing integration of AI?
When digital analytics started, it was primarily a mechanism for understanding how digital campaigns performed. When paid search, email, and other digital advertising supplanted traditional marketing channels, digital marketers used digital analytics to capture campaign codes, roll them up into marketing channels, and determine which channels they should invest more or less in by computing return on advertising spend (ROAS).
In subsequent years, digital analytics began tracking all website behavior, including content popularity, user path flows, conversion funnels, etc. Next, digital analytics allowed product teams at organizations to learn how users were using their digital properties (websites/apps) so they could improve conversion and experiences. Eventually, things like surveys and session replay were added to digital analytics.
Recently, organizations have realized that digital analytics often portrays an incomplete picture of customer behavior since it only contains customer behavior from websites and mobile apps, frequently missing critical data from stores, call centers, and other customer touchpoints. Organizations have increasingly adopted cloud data warehouses to centralize all customer data, including digital analytics data.
We’re seeing the same thing with Customer Data Platforms (CDPs). The first generation of CDPs collected just a subset of customer data (online interactions) so that marketers could build audiences and campaigns using that data. But the next generation uses a far more complete picture of customer data by operating from the data warehouse– that’s the big revolution we’ve been seeing as “Composable” CDPs like Hightouch gain traction by working from the data warehouse.
The AI wave will also pressure organizations to use complete and governed data from the data warehouse. AI needs the breadth of data that the warehouse has to work correctly, so the best marketing applications of AI will also be warehouse-native. Within CDPs, AI will begin to power all customer journeys and experiences. And we’re seeing new models for agentic AI-powered marketing that are emerging parallel to CDPs, like AI Decisioning.
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You often emphasize the importance of making data actionable. What are the most significant mindset shifts organizations must embrace to transition effectively from data collection to genuine data activation?
In my experience, most organizations don’t think of data as something that should generate ROI but view it as a cost of doing business. Organizations spend months or years collecting and modeling data, verifying its accuracy, and adding it to dashboards. But these are all preambles to using data to generate profit or cost savings.
Data should be used to answer business questions that will ignite changes in digital properties that result in incremental revenue or cost savings. If your organization doesn’t make or save more money each year than it spends on data tools and the supporting data team, you are not making data actionable and need to shift your mindset.
With Hightouch’s composable approach gaining traction, what advice would you offer enterprises currently relying on traditional, monolithic CDPs regarding transitioning to a more flexible model?
Enterprises using the older “packaged” CDP model will eventually lose to competitors adopting the new Composable CDP model. The legacy “packaged” CDP model requires organizations to choose what customer data to import into their CDP because data has to be duplicated and because of its exorbitant cost. As a result, they often build audiences, journeys, and experiences without using all customer data.
Conversely, Composable CDPs allow organizations to leverage existing data and data schemas in cloud data warehouses to build marketing campaigns, audiences, and journeys using all customer data. Over time, organizations using Composable CDPs will be able to react faster and more holistically, leading to better marketing campaigns and experiences at lower costs.
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Looking forward, how do you anticipate the collaboration between data and marketing teams needing to evolve to leverage the potential of composable, AI-driven CDPs fully?
Traditionally, marketing and data teams were separated. Data teams focused on all customer data; marketing data was a small portion of the data architecture. If marketers needed customer data to build audiences, they would often have to file a ticket with the data team and wait days or weeks to get an export of names or emails they could use for marketing purposes.
However, as the world digitized, a larger portion of customer data came from marketing tools, specifically digital analytics tools. At the same time, the pace of marketing accelerated so that marketers demanded the ability to build their own customer audiences, campaigns, and journeys. This acceleration has pushed marketing and data teams to increase collaboration. Composable CDPs, which sit at the intersection of the marketing and data teams, have been instrumental in bridging the gap between marketing and data teams.
Composable CDPs allow both teams to get the best of both worlds – data teams can build comprehensive data warehouses as a single source of truth, and marketing teams have a self-service user interface to build audiences, campaigns, and journeys using the same dataset. The increased collaboration between data and marketing teams that Composable CDPs facilitate leads to better customer experiences and increased customer growth.
At Hightouch, what strategies do you employ to educate customers who may be new to concepts such as reverse ETL or warehouse-native architectures?
The shift from legacy “packaged” CDPs to newer Composable CDPs like Hightouch has taken the industry by storm. Hightouch helped coin the terms “Reverse ETL” and “Composable CDP” and, as a result, bore the brunt of educating the market on the technology. Creating a new category is both exciting and challenging.
To help educate the industry and potential customers about the next wave of CDP, Hightouch spent countless hours writing educational blog posts and videos. But most of the education Hightouch provides occurs in 1:1 meetings with organizations. Hightouch employees spend countless hours talking to organizations frustrated with their current CDP and showing them how Hightouch’s Composable CDP could help. These sessions often involve diving deep into their existing tech stack and mocking up potentially new ones.
Lastly, our founders and I spend a lot of time traveling the globe and presenting at industry events to educate data and marketing teams about the shift to cloud data warehouses and composable architectures.
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Based on your extensive experience with Adobe Analytics and Hightouch, what technical best practices do you recommend to ensure clean and reliable data flows into activation platforms?
Having written a book on Adobe Analytics, I can attest to its power. Many organizations use Adobe Analytics to optimize their digital properties. But nowadays, organizations should think twice about making Adobe Analytics (or any digital analytics product) the primary collection vehicle for user and event data. Suppose you want the best data in Adobe Analytics (or other digital analytics products). In that case, I recommend sending data from websites/apps to the warehouse and then using Hightouch to route the data from the warehouse to the digital analytics tool. I recommend this for several reasons:
- Tagging Lock-in – If your website has digital analytics vendor tags/SDKs.app, it makes it very difficult to switch vendors in the future. However, if you send data from the warehouse to the digital analytics vendor, you can easily route user and event data to a different tool in hours.
- Data Enrichment – You can only send data to digital analytics tools like Adobe Analytics, which is known at the time of the website or mobile app events. However, suppose you send data to Adobe Analytics (or other digital analytics products) from the warehouse (using Hightouch). In that case, you can enrich user and event data with any known data in the warehouse. For example, you could send the current user’s lifetime value from the warehouse to the digital analytics tool at the time of any event occurrence.
- Page Speed – If you collect data using digital analytics tools directly on website/app pages, you may also need to use SDKs for other SaaS products—the more tags, the slower pages load, which can impact conversion. I prefer to collect user and event data once, send it to the warehouse, and then hydrate all SaaS applications from the warehouse. This approach can remove clutter and improve speed.
- Data Consistency – If you send data directly to digital analytics products and then to the warehouse, the data in digital analytics products may be processed differently and not match the data in the warehouse. Sending data to a digital analytics product first can result in different metrics in digital analytics and BI dashboards, which pull data from the warehouse. If you send data to the warehouse and then the digital analytics tool, you have a higher likelihood of the data being the same since both have the same source.
Hightouch has introduced AI Decisioning. Could you elaborate on how AI decisioning fundamentally changes the landscape for marketers utilizing warehouse-native CDPs?
Hightouch’s AI Decisioning product is a game-changer regarding 1:1 customer personalization. AI Decisioning allows marketers to leverage warehouse data and AI reinforcement learning to send different messages to each customer. Instead of messaging customers based on static calendars and homogenous audiences, AI Decisioning uses rapid experimentation to test different messages, copy, and creative assets, ultimately building audiences of one.
AI Decisioning fundamentally changes the balance of work for marketers. They have to focus less on the “levers” to pull– choosing which segments to send which content, when– and instead operate in an outcome-oriented fashion. Marketers set goals for AI Decisioning to accomplish, then use the insights the system offers them to create more relevant content variants and set up new AI Decisioning agents for new campaign types.
However, AI Decisioning requires accurate customer data to improve accuracy, typically in the warehouse. Since Hightouch has access to all customer data in the warehouse and has integrations with hundreds of messaging tools, AI Decisioning was a natural fit for Hightouch.
Also Read: What Does Modern Customer Experience Look Like in 2025?
Reflecting on your career journey in digital analytics, what has been the most significant learning or pivotal moment that has shaped your perspective on the power of data?
The most pivotal moment in my digital analytics career was the day I realized that face-to-face interactions were going away. Early in my career, when websites and mobile apps arrived, I realized how we talk and listen to customers would change forever. In the formative years of my career, those who dressed the best, spoke the best, and sold the best were the ones who succeeded. Your ability to converse, support, or convince someone face-to-face to buy your product was critical. But as customer interactions moved online, it became clear that the organizations that could leverage data to understand and learn about their customers were the ones that would win in the long run. This realization of a new customer interaction model drew me to the digital analytics field.
Today, we can see that the brands that know the most about their customers are winning—Amazon, Apple, Facebook, Netflix, Google, etc. The more companies know about you, the more likely customers are to continue using their products and services. Data has become the new way to listen to and speak to customers.
Beyond the world of data, what are some of your passions or interests that fuel your insights and approaches in your professional life?
Beyond data, one of my passions is product evangelism. For many years, I have had an official or unofficial role of “product evangelist,” even though very few understand this role. Few product evangelists exist worldwide, but I feel it is one of the most fulfilling roles. Over the years, I have tried to mentor others and help them become product evangelists. Product evangelism combines writing, public speaking, customer interviews, product insights, and marketing. I think many more organizations should invest in product evangelism, and I hope to find ways to make that happen in the future.
