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.
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