The Marketer’s Playbook for Braze Segmentation

Just technically, orchestrating Braze customer segmentation can be hard; however, the real test lies in strategizing. 

One of the reasons, as Roger Martin puts it, is that segmentation is not deterministic, it’s probabilistic. “…it is a bad idea to treat customers in your segment as if you own their purchase decision. And it is an equally bad idea for you to discourage customers outside your defined segment from buying from you.” Now, that’s very accurately put. Yet it is equally true that segmented contact lists increase revenue by a staggering 760%.  

Segmentation may be a probabilistic science—but with a dash of common sense and confidence, it becomes powerful.

In today’s Braze segmentation guide, our Braze expert at Mavlers breaks down how to make segmentation work for you.

Types of segments in Braze

Parameters for segmentation

1. High-value inactive users

2. Multi-channel users

3. High-intent cart abandoners

4. Frequent buyers (VIPs)

5. Users likely to churn

6. Location-based targeting

7. Users with specific custom attributes

Wrapping up

Types of segments in Braze

Before digging into Braze segmentation strategy, let’s refresh. 

Quickly, here are the different types of segments available for doing Braze customer segmentation:

• Behavioral: Based on actions users take or don’t take.
• Demographic: Based on user profile attributes.  
• Technographic: Based on device, app, browser, OS, etc. 
• Engagement-based: Based on user interaction.
• Predictive: To forecast user behavior.  

For predictive segmentation, you’d use Braze’s Predictive Suite. 

Parameters for segmentation

For advanced customer segmentation in Braze, we’ll be relying on the following key parameters: 

• Custom events: Track user-specific interactions (e.g., added_to_cart, watched_video) and use them as segment filters.
  
• Custom attributes: Profile-level data you can set and use for segmentation (e.g., subscription_type = premium).
  
• Time-based filters: You can segment based on when an event occurred (e.g., “Made a purchase within the last 14 days”).
  
• Nested logic: Use nested group logic to combine multiple conditions with precision.
  
• Engagement filters: Include or exclude users based on their interaction with previous campaigns (e.g., ignoring a push notification).

Now, let’s turn our attention to a few important segments in order to learn more about Braze segmentation. 

1. High-value inactive users

Goal: Target those users who were once active and high spenders, but haven’t engaged recently.

Segment Filters:

• Total Purchase Amount > $500
• Last App Session > 30 days ago
• Email Subscribed = true

You should set each condition in Braze Segmentation and choose the filter option that matches with conditions:

1. Total purchase amount > 500

Filter option to choose: Custom attributes

Select attribute: total_purchase_amount

Condition: greater than 500

2. Last App Session > 30 days ago 

Filter option to choose: Sessions

Use: Last used App

Condition: more than 30 days ago 

This type of segmentation is typically used in win-back or retention campaigns, where the goal is to re-engage valuable users. 

To effectively re-engage high-value lapsed users in Braze, combine behavioral (inactivity), transactional (high purchase history), and eligibility (email subscribed) filters. This kind of highly-targeted segmentation enables personalized win-back campaigns aimed at users most likely to return and convert.

2. Multi-channel users

Goal: Target those users who are engaged on both email and in-app messages.

Segment Filters:

• Last Email Open < 7 days ago
  
• Last In-App Message Viewed < 7 days ago

We’re identifying a segment of users who are actively engaging with both email and in-app channels within the past 7 days. These users are highly reachable and responsive across multiple touchpoints, making them ideal for cross-channel campaigns, upsell opportunities, or feature announcements. 

Their dual-channel engagement signals high intent or interest, so now is the time to deepen the relationship or drive action.

3. High-intent cart abandoners

Goal: Users who added items to the cart but didn’t purchase within 24 hours. (Or more, depending on the user)

Segment Filters:

• Custom Event: Added to Cart
  
• Not Performed Event: Purchase in the last 24 hours

This segment captures high-intent users who showed purchase interest by adding items to their cart but didn’t complete the transaction within a defined time frame (e.g., 24 hours). 

These users are at a critical decision point and are ideal candidates for abandonment campaigns. It’s a classic opportunity to reduce drop-off and increase conversion.

4. Frequent buyers (VIPs)

Goal: Create a VIP customer group based on frequent purchases.

Segment Filters:

• Purchase Count > 10
  
• Last Purchase Date < 30 days ago

This segment identifies your most loyal and recently active customers. Users who have made more than 10 purchases and bought something within the last 30 days. These VIP users are highly engaged and valuable, making them perfect for exclusive offers, early access campaigns, loyalty rewards, or brand advocacy initiatives.

Recognizing and nurturing this group helps boost retention, increase lifetime value, and foster deeper brand loyalty.

5. Users likely to churn

Goal: Targeting users whose engagement is dropping steadily.

Segment Filters:

• Last App Session between 21–30 days ago
  
• Custom Attribute: Churn Risk Score > 70

This segment identifies users who are in the early stages of churn. They haven’t used the app in 21 to 30 days and have a high churn risk score (above 70). These users are slipping away but not entirely lost, making them ideal for proactive re-engagement efforts such as personalized offers, reminders, or helpful nudges to bring them back before they fully disengage.

Intervening at this stage can reduce churn and preserve customer value.

6. Location-based targeting 

Goal: Target users in New York who attended a previous event.

Segment Filters:

• Custom Attribute: City = New York
  
• Custom Event: Attended Event at least once

This segment highlights users located in New York who have previously attended an event. They’re geographically relevant and have shown past engagement, making them strong candidates for localized event promotions, exclusive invites, or personalized follow-ups. Their prior attendance signals interest, increasing the likelihood of future participation.

7. Users with specific custom attributes

Goal: Target users based on subscription plan and interest.

Segment Filters:

• Custom Attribute: Plan Type = Premium
  
• Custom Attribute: Interest = Fitness

This segment targets users who are on a Premium plan and have shown a specific interest in fitness. These users are both high-value and highly targeted, making them ideal for personalized content, upsell opportunities, or fitness-focused feature promotions tailored to their preferences and plan level.

Braze segmentation best practices:

– Start broad, then refine: Begin with larger, more general segments and progressively narrow them down as you gain insights into user behavior and campaign performance.

– Utilize “Exclude” filters effectively: Prevent over-messaging or irrelevant communication by actively excluding users who have already converted, are inactive, or have received a similar message recently.

– Segment for the entire customer lifecycle: Create distinct segments for users at different stages of their journey (e.g., new users, active users, lapsed users, high-value users) to tailor messaging accordingly.

– Consider recency, frequency, and monetary (RFM) values: Segment users based on when they last interacted, how often they interact, and how much they spend to identify your most valuable customers and those at risk of churn.

– Regularly review and update segments: User behavior and business objectives evolve, so periodically analyze segment performance, clean up outdated segments, and create new ones to ensure continued relevance and effectiveness.

Wrapping up!

Braze segmentation isn’t just about slicing and dicing data. It’s really about understanding user behavior, anticipating needs, and acting strategically. Whether you’re identifying churn risks, nurturing loyal customers, or targeting users based on interests and geography, well-defined segments help you meet the moment with relevance.

The real power lies in combining filters thoughtfully, testing what resonates, and continuously optimizing.  

If you need help with advanced customer segmentation in Braze, book a call with one of our Braze experts.