Case study 02
User Segmentation & Personalization
Behavior → Self-service audiences
Building a scalable segmentation system that turned behavioral data into self-service audience creation for product, marketing, and business teams.
Integrated / Final validation in progress
01Context
Product, marketing, growth, and business teams needed a consistent way to understand users. Audience groups were built manually and in isolation — inconsistent and hard to scale.
02Challenge
Signals were fragmented across sources, needed multiple time horizons, and had to stay reliable at scale — while remaining usable by non-technical teams.
03My role
Designed and implemented the core user segmentation model, combining behavioral, activity, engagement, monetization, retention, listing, and category signals into a unified user-level structure.
Built multiple time-based aggregation layers, integrated the analytical output with TimescaleDB, and supported Data Hub integration, front-end behavior testing, and performance optimization.
04Behavioral foundation
Seven behavioral dimensions, one user-level model.
05Time-aware analytics
A user can look very different today, over a week, or over a quarter.
Different time windows helped distinguish recent behavior from longer-term patterns.
Calendar views
Rolling windows
Viewing: 30D · conceptual
06Analytical / system flow
From raw signals to business use.
Conceptual representation
01 · Behavioral signals
Fragmented marketplace behavior
02 · User-level model
Unified user analytical model
03 · Time & aggregation
Calendar and rolling views
04 · Serving layer
Snowflake / dbt foundation, TimescaleDB serving
05 · Internal Data Hub
Self-service segmentation interface
06 · Business use
Product · Marketing · Growth · Business
07Self-service layer
The project didn't stop at the model.
Segmentation was integrated into the internal Data Hub, so Product, Marketing, and Business teams can build their own audiences.
From
“Manual segment requests to the data team”
To
“Business users creating governed dynamic segments through the internal Data Hub”
- Define behavioral conditions
- Choose dynamic timeframes
- Combine rules
- Create targeting audiences
Segment builder
Illustrative · not the internal UI
Click a rule to see how conditions compose into an audience.
08Scalability & reliability
Recurring use, with data quality designed in.
Built for recurring use through scalable aggregation, performance optimization, freshness checks, and downstream readiness validation.
09Approach
From raw behavior to business usability.
01
Understand the user signals
02
Design the behavioral model
03
Add time-aware analytics
04
Build for scale and reliability
05
Integrate into the serving layer
06
Make segmentation self-service
10Impact
Covered by the segmentation foundation
Enabled non-technical teams to create and manage dynamic audiences
Combined behavioral, engagement, monetization, retention, and category signals
Supported recurring product, marketing, growth, and strategic use cases
11What this demonstrates
Strong segmentation is not just about assigning users to categories.
It requires combining behavior, time, reliable analytical modeling, scalable delivery, and business usability into one reusable system.
Tools & capabilities