Personalization at Scale: How to Power AI with a Solid Data Strategy

While marketers have long pursued greater personalization to engage customers, the rise of generative AI is revolutionizing the ability to deliver tailored, data-driven experiences at scale. This shift is raising customer expectations to new heights. In fact, more than three-quarters of consumers are more likely to purchase from brands that personalize their interactions, meaning businesses that fail to meet these demands risk falling behind.

But as marketers rush to embrace AI’s possibilities, many overlook the most crucial first step: building a solid data foundation.

AI without a data strategy is like trying to drive a car without fuel — it simply won’t go far. Without clean, structured data, even the most sophisticated AI systems cannot deliver on their promise. A well-constructed data strategy rooted in unified data and teams empowers marketers to fully leverage AI’s potential.

In AI-driven marketing, the same powerful base models are driving insights across industries. The real competitive advantage lies in your data, your understanding of customer behavior, and your ability to contextualize insights within your brand’s unique business framework. This foundation is the key to unlocking the promise of true personalization.

Let’s dive into some key first steps that will help you optimize your data framework to unlock AI’s full capabilities and drive real business results.

Change your team’s mindset around data

Traditionally, data was owned by specialized teams, with marketers having to request access to audience insights needed for campaigns. This siloed approach slowed execution and disconnected marketing teams from the data required for personalized customer experiences. With the rise of AI and the demand for greater campaign velocity, it’s no longer enough to rely solely on data experts.

A unified data strategy enables marketing and data teams to collaborate seamlessly. Data teams should understand the marketing use cases, while marketing teams need direct access to data to build real-time customer journeys and audience segments.

Imagine marketers wanting to target customers who showed interest in a specific product during an email campaign. If they have to wait for data teams to provide those insights, the window of opportunity could pass. But with direct access to unified data, marketers can pull that information quickly and create timely campaigns that resonate with customers.

So, how do marketers access that unified data? By adopting a cloud data warehouse paired with a composable architecture. The data warehouse allows teams to centralize customer data and work from a single source of truth. Then, a composable CDP on top of the data cloud allows marketers to self-serve unified customer data for audience and campaign building. Meanwhile, data teams focus on governance and optimization, allowing AI to perform at its best.

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Centralize your data for a unified view

Fragmented data stored across various tools — like CRM systems, email marketing platforms, and social media engagement tools — undermines AI’s ability to generate accurate insights. To unlock AI’s potential, marketers must combine all their customer data, whether it’s from loyalty programs, website interactions, or purchase histories, into a single, accessible source.

A critical step in centralizing data is identity resolution — the process of unifying customer profiles across multiple touchpoints. Imagine you’re running a campaign for a global retailer like Nike. Different departments — e-commerce, marketing, social media — have their own customer data, but none of it is linked. Without identity resolution, Nike might miss that the person browsing sneakers on their website is the same one interacting with their Instagram post, leading to fragmented customer experiences.

A unified data strategy isn’t just about centralizing data, though — it’s also about governance. Compliance with data privacy regulations, like GDPR and CCPA, must be baked into your strategy from the start. AI-driven marketing needs to prioritize transparency, customer consent, and the ethical use of data. Ensuring that your data is governed properly — secured, compliant, and trustworthy — builds customer trust and prevents costly breaches or violations.

With a cloud data warehouse serving as the backbone of your unified data strategy, marketers can consolidate and clean data across sources. This unified dataset empowers AI to deliver hyper-personalized experiences at scale, while the composable architecture ensures consistent access to this compliant data across all marketing tools.

Build flexibility with a composable architecture

As marketing and AI tools evolve, many businesses find themselves paying for multiple AI solutions embedded in each platform — whether it’s CRM, email, or social media. This fragmented approach drives up costs and complicates campaign management, as each tool operates in isolation. To simplify and streamline your AI-driven marketing efforts, a composable architecture centralizes your AI at the hub.

For instance, rather than using a separate AI tool for email marketing and another for social media targeting, marketers can use a cloud data warehouse paired with a composable CDP to house a single, preferred AI model. This centralized system processes customer data in real-time and pushes AI-powered insights to each platform as needed. Whether you’re launching an email campaign, running social ads, or personalizing a website experience, the AI model remains consistent across channels, delivering cohesive messaging and personalized experiences.

This approach simplifies campaign management, eliminates the need to pay for multiple AI solutions, and ensures that all marketing efforts are driven by a unified source of truth.

Empower experimentation and continuous learning

AI strategies thrive on constant iteration and learning. As new data flows in, you must continually test, optimize, and refine AI models to better home in on your target audience’s preferences. Whether you’re trying new audience segments, creative variations, or messaging strategies, creating a framework that supports rapid experimentation ensures AI is continually learning and improving.

However, AI doesn’t operate in a vacuum. To be truly effective, AI must work in harmony with human creativity and oversight. AI is not a replacement for human insight but rather a tool that enhances your team’s capabilities — giving them “superpowers” to work more efficiently and strategically. Introducing AI to your teams requires a focus on change management and enablement, ensuring that they feel empowered and equipped to use AI alongside their creativity.

For example, a retailer might test different promotions for various customer segments based on purchase history. Using a cloud data warehouse and composable architecture, data from these experiments feeds back into the system, allowing AI to analyze performance and inform how your team can adjust future campaigns. This real-time feedback loop ensures AI models don’t remain static; they evolve based on real-world outcomes, driving better performance over time.

Empowering your marketing team to experiment with AI-driven campaigns allows for quick adjustments and optimization, ensuring that your campaigns stay relevant and data-driven. With this approach, AI can continuously refine its insights, delivering increasingly personalized and effective campaigns.

Build a foundation of AI success

AI is transforming how businesses interact with customers, but without a unified data strategy, even the most advanced AI tools cannot reach their full potential. By shifting your team’s mindset, centralizing data, adopting a flexible composable architecture, and empowering experimentation, marketers can unlock the true power of AI. These steps enhance AI’s ability to deliver personalized, data-driven experiences and allow businesses to operate with greater agility, efficiency, and long-term success.

Success with AI is not just about adopting cutting-edge technology — it’s about building the right data foundation to ensure that technology drives meaningful outcomes.

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