Client Survival Analysis & Break-Even Modeling
Feb
2025
Ongoing
Problem / Purpose
The company lacked visibility into when and why clients terminate, how long they remain profitable, and how break-even timing differs across client types. This limited the ability to strategically allocate resources and optimize service models.
Solution
Self-initiated a survival analysis project to model client term timelines and break-even points using Kaplan-Meier survival curves. Segmented analysis by service offering, client size, and termination reason (e.g., out of business vs. switching providers). Modeled breakeven for administrative fees versus sales acquisition and servicing costs. Ran Python survival models from a self-service Snowflake data object using dbt and Dagster. Outputs informed retention strategies and product focus.
Key Achievements / Impact
Revealed client lifecycle patterns and profitability dynamics across key segments. Enabled leadership to better prioritize client types for retention, product tailoring, and resource allocation.
Key Technologies / Tools Used
Python, Survival Analysis, Data Analysis, DBT, Dagster, Python (Pandas), Snowflake, Data Modeling, Segmentation Analysis, Python (lifelines)
Role
Data Scientist (self-initiated)
ProService Hawaii