Segmentation strategies: when to use events vs. attributes

Segmentation is at the core of every effective lifecycle marketing program. The more precisely you can group customers, the more relevant your messaging becomes and the higher your engagement and revenue. But when it comes to powering those segments, teams often get stuck deciding between events and attributes. Both describe customer data, but they serve different purposes, and using the wrong one can make your lifecycle efforts harder to scale.

Understanding events vs. attributes

Attributes (sometimes called properties or traits) describe the state of a user. They are persistent values stored on a profile, such as first name, subscription status, lifetime spend, loyalty tier, or last purchased product category. Attributes are ideal for long-term segmentation and are often used in audience filters like “VIP customers with LTV > $500” or “active subscribers who haven’t logged in within 30 days.”

Events, on the other hand, capture actions taken by a user. These are behavioral data points such as:

• Placed Order

• Viewed Product

• Signed Up 

• Abandoned Cart

• Clicked Email 

Events are time-stamped and can trigger automated flows or be used for behavior-based, dynamic segmentation, like “users who viewed a product but didn’t purchase in 48 hours.”

Put simply, attributes are one to one, and events are one to many.

Platform differences matter

Each lifecycle platform defines and stores these data types differently.

In Klaviyo, profile properties act as attributes, while metric events represent behavioral data. Both can be updated or sent through the API. Attributes typically update through integrations or direct API calls, and events can be tracked through the API, SDKs, or platform connections like Shopify or Segment.

Iterable distinguishes between user fields (attributes) and event data fields (events). Both can filter audiences, but only events can trigger journeys.

In Braze, user attributes and events coexist with custom attributes, allowing nested data structures. This flexibility is powerful but can add schema complexity.

Customer.io separates people attributes from event data. It’s flexible for tracking granular actions but requires thoughtful schema planning to maintain consistency.

Understanding these differences helps keep data consistent across systems and prevents headaches during migrations.

How data modeling affects teams

Lifecycle marketing teams rely on attributes for defining audiences and events for triggering behavior-based messaging. When event tracking is incomplete or attributes are outdated, segmentation breaks. Consistent data definitions between marketing and engineering are critical to avoid broken flows or duplicate triggers.

Data engineering teams face the trade-off between volume and accuracy. Events generate higher data volume and require schema management, while attributes need controlled update logic to prevent overwriting. Engineers must define what data lives where, how it syncs, and how often it updates to keep both marketers and systems aligned.

End users (your customers) feel the results. Good segmentation improves experience and timing; poor data hygiene does the opposite. A “welcome” message firing twice or a loyalty email missing eligible customers can damage trust and performance.

When to use events vs. attributes

As a rule of thumb:

• Use events to define when something happens.

• Use attributes to define who the user is or what state they’re in.

• Mixing the two leads to confusing data structures and unreliable automations.

Advanced segmentation strategies

Combine both for precision. Many brands layer event filters on top of attribute-based segments, such as “Customers in the Gold loyalty tier (attribute) who abandoned cart in the last 24 hours (event).” This pairing brings behavioral relevance to high-value segments.

Use derived attributes. Some teams calculate metrics like average order frequency or days since last login from event data, then store them as attributes for easier campaign targeting.

Map event properties carefully. Events often include nested properties such as product name or category. These properties can power dynamic content or product recommendations directly within emails.

Invest in a unified data schema. A consistent, documented data schema ensures your ESP, CDP, and analytics platforms speak the same language. Frameworks like Segment’s schema guide and Amplitude’s taxonomy best practices are great references for building this foundation.

Putting it all together

Use attributes to define who your users are and events to define what they do. Keep your schema consistent, and audit it regularly to prevent drift between marketing logic and data reality. By aligning your data models early, lifecycle teams can move faster, engineers can avoid rework, and customers get more relevant experiences.

If you’re optimizing your ESP data or planning a migration, Scalero’s migration services can help ensure your segmentation scales with your business.