TL;DR
Aggregate averages can hide meaningful subgroups. Trajectory segmentation models distinct paths over time
(rising, stable, declining) and supports timing-sensitive decisions about who to help and when.
Problem
- Who is trending in a risky direction even if overall metrics look fine?
- When does the trend become meaningful enough to intervene?
- What actions should differ by segment?
Data
- Repeated measures (time series / longitudinal survey / behavioral signals)
- Optional covariates for interpretation (baseline, demographics, context)
Method
- Fit a trajectory model to capture patterns over time.
- Derive interpretable trajectory classes (segments).
- Compare downstream outcomes and recommend actions by segment.
Key takeaways
Make the time dimension explicit
Don’t optimize the average. Interventions should target the rising trajectory segment.
Timing matters. A late intervention can be less effective than an earlier, lighter one.
Design experiments by segment. Test different strategies within each trajectory class.
How this maps to product work
- Segment-specific nudges / messaging
- Resource allocation: prioritize human review for highest-risk trajectories
- Measurement: report metrics by segment + progression between segments
NDA-safe interview deck
- 1 slide: trajectory curves + segment sizes
- 1 slide: outcome differences by segment
- 1 slide: decision + next experiment