Client Contact Clustering & Segmentation
Feb
2025
Ongoing
Problem / Purpose
Client-facing contacts varied widely in behavior, influence, and satisfaction, but there was no structured way to segment or understand them. This limited the company’s ability to target referrals, gather testimonials, or identify service issues.
Solution
Self-initiated an end-to-end contact clustering project. Built an aggregated dataset from raw Salesforce data including job role, tenure, communication volume and quality, case metrics (e.g., reopen rate, time to close), sentiment analysis, and NPS history. Ran clustering models in DataRobot and identified 6 distinct contact groups, with strong alignment to NPS segments and referral behavior. Delivered interpretability insights and guidance for future data collection (e.g., contact influence and goals).
Key Achievements / Impact
Enabled Account Managers to prioritize promoter-type contacts for referrals/testimonials and recognize patterns in dissatisfied contact types. Provided foundational logic for future segmentation and service strategy improvements.
Key Technologies / Tools Used
DBT, Dagster, DataRobot, Salesforce, Snowflake, SQL, ML Models (Clustering), ML Models, Data Product Design, Sentiment Analysis, NPS Analytics
Role
Data Scientist (self-initiated)
ProService Hawaii