From Data to Action: How to Create a 360-Degree View of Customers
Oct 22, 2025
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Post written by
Hannes Bünger
Photo by: Robynne O
Data is the foundation of everything
Data-driven customer communication doesn't start with the channels, campaigns, or algorithms—it starts with the data. Without the right data in place, it's impossible to build relevant customer experiences. That's why a Customer 360—a comprehensive view of the customer—is the backbone of modern Customer Engagement.
However, Customer 360 isn't just about gathering customer data. To truly personalize, we also need product data, transaction data, behavior data, and sometimes external data sources. For example, if products lack descriptive attributes (color, style, use), creating relevant recommendations becomes impossible. If we can't link a customer's purchase to the product's features, we can't predict the next buy or offer the right accessories.
More than just customer data
Many organizations think of “customer data” when discussing Customer 360—i.e., email addresses, phone numbers, consents, loyalty IDs, and behaviors across channels. But equally important is the product data that enables personalization:
Retail: If every product is just a “jacket” in the system, we can't personalize. However, if the product has attributes like color, material, style, use, and season, we can recommend other similar products or complementary accessories.
Travel industry: If a booking is just a “trip,” we can't personalize the communication. But if the trip is broken into components (flights, hotel, transfers), we can build messages based on the next needs—e.g., seat booking, car rental, or hotel room upgrade.
Insurance: If a customer just has “an insurance” in the system, personalization is tough. Instead, if we have product attributes (car model, housing type, destination), we can create relevant cross-selling opportunities and tailored offers.
Product data is key to customer profiling and personalization.
In short: Customer 360 = Customer + Product + Context
CDP vs Data Lake – different roles in a Customer 360
When companies build a Customer 360, the big question is often: should we collect data in a CDP or a Data Lake? The answer is almost always “both”—but with different roles.
Option 1: CDP as the hub for Customer 360
How it works: All data needed to create a unified customer profile (customer data, product data, behavioral data) is fed directly into the CDP. Here, identity management, profile unification, and activation in channels take place.
When it fits: For organizations wanting quick time-to-value, where marketing and CRM can work directly in the platform without reliance on IT or data science. Typical examples are retail, e-commerce, and subscription services.
Strength: Fast implementation, close connection between data and activation.
Limitation: Can struggle to handle very large or complex data volumes. Also, this solution can become expensive, as most CDPs charge per event—which rapidly increases costs. For this option to work, a special type of CDP is often needed, such as so-called data integrators. These have more advanced functionality for data management—including data preparation, cleansing, and enrichment—which makes them better equipped to handle large volumes.
Option 2: Data Lake as a base, CDP for activation
How it works: All raw data (customer, product, transaction, web, sensor data, etc.) is collected in a central Data Lake. Data cleaning, refinement, modeling, and often AI/ML processing occur here. The CDP is connected as a “user platform” for marketing and CRM, with access to finished profiles and product structures.
When it fits: For organizations with complex operations, large volumes, or many different data sources. Typical examples include telecom, insurance, travel industry, and banking.
Strength: Flexibility, potential for advanced analysis, and use of more data sources.
Limitation: Longer time to value, requires strong IT and data expertise.
Option 3: Hybrid solution
How it works: Some data is managed directly in the CDP (e.g., web events, campaign response) while large data volumes (e.g., transactions, product master data) are stored in a Data Lake. CDP and Data Lake are synced to provide a complete Customer 360.
When it fits: For companies that want to combine quick activation with scalability. Works especially well for organizations already with a modern data platform but wish to give marketing easier access to data.
Strength: Balance between time-to-value and long-term scalability.
Limitation: Requires clear interfaces and governance to avoid complexity.
Data model – the foundation for how data is used
Before exploring how to collect, unify, and activate data, we need to pause at a critical building block: the data model. It determines how data is organized and used, and the choice of model greatly influences practical possibilities.
A frequently overlooked but vital question in Customer Engagement is which data model is used. A data model serves as a framework or schema that dictates how data should be organized, stored, and processed in the system. It also guides how different data types relate to one another and determines what possibilities you have to use the data in your communication.
In practice, there are mainly two models that CDPs use—and they function in entirely different ways.
There are two types of data models that CDPs use, and they work very differently.
Relational data model
In a relational database, information is organized in tables linked together through defined relationships. A customer can have multiple bookings, a booking can include several products, and each product can have its own attributes.
Strength: Manages complex business logics where many different entities need to be connected.
Example industries: Airlines, hotel chains, telecom, and insurance—where relations between customers, transactions, products, and services are complex.
Challenge: Requires more work to define tables and relations, which often means longer implementation time.
Event-based model
An event-based data model stores everything as events: “customer logged in,” “customer bought a product,” “customer clicked on an email.” These events can then be linked to a customer profile and trigger personalized communication.
Strength: Very flexible and fast for building real-time-based flows and triggers. Perfect for scaling large data volumes.
Example industries: Retail and e-commerce, where transactions are many and the customer journey consists of a series of recurring behaviors.
Challenge: Every data point must be defined as events or attributes, making the model less suited to more complex business relationships.
When does which model fit?
Relational data model: When the business is complex and requires handling of multiple interconnected entities. Example: hotels where a booking can include several rooms, different guests, and multiple payment methods.
Event-based model: When the business relies on high volumes of relatively simple interactions, like in retail where customers often buy many products in quick transactions.
In short: the choice of data model affects how far you can go with personalization and automation. A model that doesn't match the business logic will limit both efficiency and scalability.
How to collect, unify, and activate data
Building a Customer 360 involves three steps that need to work in concert:
Collect data – from all relevant sources: CRM, web, e-commerce, apps, customer service, product databases, and external sources.
Unify data – merge different identities (loyalty ID, email, device ID, phone number) into a common customer profile. Several options exist: unification can occur in the CDP, a Master Data Management system, or a Data Lake. However, the further you try to solve unification downstream, the greater the complexity and the risk that the data can't be used effectively in activation.
Activate data – make profiles usable in practice: in email, SMS, push, social media, advertising, and on the website. This is where personalization, trigger logic, and omnichannel orchestration occur.
If any of these three steps fails, Customer 360 becomes either incomplete (data isn't collected), fragmented (identities aren't unified), or unusable (it can't be activated).
Common mistakes and how to avoid them
Many companies fail with Customer 360 due to some recurring reasons:
They focus too narrowly on customer data – but forget product data and context. Without these, true personalization is impossible.
They try to collect “everything” directly – leading to overly complex models that become unworkable. Better to start with data linked to prioritized use cases.
They choose the wrong architecture – for example, they try to do everything in a CDP even though the business logic requires a Data Lake.
They don't unify profiles correctly – leading to duplicates and incorrect customer pictures.
They collect data but don't activate – the CDP or data solution remains just a data warehouse without business value.
Being aware of these pitfalls and working methodically helps you avoid costly mistakes and realize the value of a Customer 360 faster.
In conclusion
A Customer 360 is the foundation for all modern customer communication—but it requires more than just customer data. Product data and context must be in place for personalization to become a reality.
CDP and Data Lake play different roles in this work. The CDP is the tool to empower marketing and CRM to act quickly and personally. The Data Lake is the foundation for managing complexity, large volumes, and advanced analysis. Often the best solution is a combination—where the Data Lake provides refined data to the CDP, and the CDP makes it useful in real activation.
The key is always to start from the business: what type of data do we need to create relevant customer experiences, and which system is best equipped to handle that data?