In the race to deliver more personalized experiences, a single customer view (SCV) has become a strategic imperative.

But despite its potential to unlock better targeting and stronger loyalty, many brands struggle to bring this vision to life.

From fragmented data sources to unclear ownership, the path to a single customer view can feel like trying to navigate in a forest without tracks or a compass.

But it doesn’t have to be.

With the right framework, it’s possible to break down the complexity and make meaningful progress, starting with small wins and building toward long-term transformation.

Tryzens Global has developed a proven five-step framework that simplifies the journey to a single customer view, which we have implemented for clients ranging from publishing to global retail brands.

Here’s how it works.

Step 1: data collection

You can’t understand what you can’t see.

Most brands sit on a wealth of data across internal and external systems, like CRM platforms, ESPs, POS systems, websites, apps.

Too often, though, that data lives in silos. The first step is to audit what’s already there. What tools are in use? Who has access? What’s the quality of the data? Where are the gaps?

Even for brands who believe they’ve already started this journey, it’s worth revisiting the basics.

One retailer recently claimed to have a SCV, but couldn’t define what was actually inside it. So Tryzens advised a “speed and impact” approach: focus on what can be accessed quickly and will deliver measurable value fast.

Usually, this starts with first-party data from engaged customers and understanding how that data is being used (or underused) in campaigns. 

Ultimately, this phase isn’t about perfection but momentum.  

The goal is to get a usable data sample to start identifying how it connects as well as where it doesn’t.

Step 2: data integration

Once you have all your data in one place, the next challenge is making sense of it.

Different systems describe the same customer in different ways: it could be a “user” or an “account.” A real single customer view requires unification, mapping those identifiers together into one record that updates in real time as customers engage across channels.

This often hinges on creating a unique identifier. In the early stages, that might be anonymous, but as the customer engages (signing up for email, making a purchase, downloading an app), the brand can evolve the profile from “unknown” to “known,” layering in richer behavioral, transactional, and preference data. 

This layered identity model is critical to future segmentation, personalization, and predictive analytics.

Key here is collaboration across teams: understanding how data is currently structured, what identifiers exist, and what needs to be created to connect the dots.

Step 3: customer segmentation

With clean, unified data, brands can start to group customers based on shared characteristics, behaviors, or values.

But not all segments are created equal.

The focus should be on defining audiences that align to specific business goals. For example, a retailer looking to boost cross-sell in a new category should first identify existing customers who’ve shown purchase affinity for related categories.

It starts with your largest, highest-value audience. That “big slice of the cake” becomes the test case.

From there, segments can be refined further based on device type, channel, location, engagement recency, and other variables.

Behavioral data from tools like GA4 helps add depth, revealing where customers enter the journey, which channels they engage through, and how frequently they return.

Combining this with order history and campaign data helps marketers turn segments into actionable cohorts.

Segmentation, then, becomes a strategic tool not just for personalization, but for decision-making across marketing, product, and commercial teams.

Step 4: data-driven predictions

Once segmentation is in place, the next step is using the data to predict outcomes.

What’s the likelihood this customer will purchase again? What should we recommend next? Which customers are at risk of churn?

Predictive models don’t need to be overly complex to deliver value. In many cases, brands can start with simple rules-based automation: frequency thresholds, last-click analysis, or RFM models.

The key is to define clear, measurable outcomes and use existing data to steer toward them, always anchoring predictions in business value.

That might mean prioritizing a VIP retention segment, identifying ideal customers for a loyalty program, or triggering lifecycle emails based on time since the last purchase.

You can watch how AI can be used to experiment in conversion rate optimization, helping to define hypotheses, selecting the most relevant metrics to measure success, and identifying risks before launch.

This stage bridges the analytical with the tactical, helping businesses act on insight instead of just observing it.

Step 5: analytics

The final step in the SCV framework is putting insights into action and tracking their performance.

Here, brands deploy unified and segmented data across channels: CRM, email, paid media, onsite personalization, in-store experiences.

Customer data platforms (CDP) play a key role in centralizing activation and streamlining real-time decisioning.

Just as critical is the feedback loop.

Are these segments performing as expected? Are campaign ROIs improving? Where are drop-offs happening?

Analytics at this stage need to tie directly back to business KPIs and customer outcomes.

Crucially, this isn’t a one-and-done exercise. Activation requires continuous iteration: testing, learning, and evolving as customer behavior, tools, and market conditions change.

Key takeaway

Achieving a single customer view isn’t about chasing the perfect data stack or implementing a new platform.

It’s about clarity of purpose, cross-functional alignment, and a clear roadmap from where you are now to where you want to be.

Start small. Prioritize speed and impact. Build on what you know. And remember that the north star is a better experience for every customer, every time.

For deeper insight into our 5-step strategy for single customer view, listen to our podcast episode, led by our Solutions Consultant, Dimitar Alourdas, and CX expert Aimée Hart.