User Segmentation for Understanding 28 Million MAU, TUES

Toss developed TUES (Toss User Engagement Segment) to analyze its 28 million monthly active users from a platform-wide perspective. It groups users by their service-use patterns, enabling Toss to understand user motivations, design segment-specific strategies, and explain changes in company-wide metrics. TUES V2 improves on the original by capturing usage depth, multi-service behavior, and engagement with individual service categories.

Platform-Wide User Segmentation

• Service-specific segments such as “users of Service A” are not mutually exclusive or collectively exhaustive because users may use multiple services.
• TUES groups users with similar patterns across Toss’s entire service ecosystem.
• It helps identify:
  • Which services users primarily use
  • How engaged they are with the app
  • Which user groups may be suitable for particular growth or marketing strategies

How TUES V1 Worked

• Toss calculated each user’s service-use rate per app open.
  • For example, a user who opened the app 60 times and used Toss Pay during 20 of those sessions had a 33% usage rate.
• Users with similar service-usage distributions were grouped using K-Means clustering.
• The raw clusters were interpreted and renamed to make them more useful for product and strategy teams.
• V1 included:
  • Highly engaged users: Users who regularly use several services
  • Service-oriented users: Users primarily focused on Toss Bank, Toss Securities, inquiry services, benefits, transfers, or other services
  • Simple visitors: Users who open the app but rarely use its services

How Toss Uses TUES

• Transition strategy: Teams can plan how to move users from simple visits to service-oriented engagement and eventually to highly engaged usage.
• Product growth: Product teams can quickly identify which user segments use their service most and combine that insight with transition strategies.
• Behavior analysis: TUES reveals when users change segments, begin churning, or return after inactivity.
• Top-line metric analysis: When MAU changes, Toss can identify which user segments moved and which services likely caused the change.
• Targeted marketing: Marketers use TUES segments for campaigns such as push notifications. The segments are also available in Toss’s internal marketing tool, TUBA.

Limitations of TUES V1

After roughly two years of use, Toss identified several weaknesses:

• V1 measured only the probability of using a service during an app open, not the number of times it was used.
• Users who engaged with a service once and users who used it ten times could appear equivalent.
• It could not show engagement with secondary service categories.
• K-Means is a hard-clustering method, so each user belonged to only one segment despite often using multiple services.
• New major services, including Toss Shopping, App in Toss, and Toss Pay, were grouped into a generic “ETC” category.

TUES V2 Improvements

• Usage-depth measurement: V2 uses the number of service interactions per app open as a feature, capturing the intensity of engagement.
• Soft clustering: Instead of assigning each user to one segment, V2 calculates each user’s degree of association with multiple segments and selectively uses those results.
• Three-layer structure: Users are described through:
  1. Overall app engagement
  2. Primary service orientation
  3. Engagement with each individual service category
• The layers are built sequentially, making it clearer why a user belongs to a segment and what action may be appropriate next.

New Strategic Capabilities in V2

• Teams can identify which service-category engagement should increase first to move users from a semi-engaged segment to a highly engaged one.
• Individual service teams, or silos, can quantitatively connect actions that increase service engagement with company-wide segment and performance changes.
• Products can more clearly compare the engagement profiles of users who do and do not use their services.
• Cross-activation strategies now have a more precise starting point based on service-level engagement.

Future Development

Toss plans to combine TUES with additional analytical frameworks to:

• Create faster and more detailed transition strategies using concepts such as service similarity.
• Build strategic user maps based on user profiles and service-use patterns.
• Quantify segment-specific value by combining TUES with frameworks such as MTVi.

TUES demonstrates how platform-level segmentation can make a growing MAU base easier to understand and act upon. By combining overall engagement, primary service use, and service-level depth, TUES helps Toss develop more targeted growth strategies and connect individual product actions to broader company outcomes.