Case study / Applied machine learning
ZIP-code lead prioritization
A regression system using public demographic data to help a home-services call center prioritize incoming leads.
Production context
9+ years in productionProduction context
Home servicesProduction context
Operational prioritizationThe challenge
The operating problem.
Granular outcome data was limited, but the operation still needed a defensible way to prioritize scarce call-center attention.
The engineering
What was built.
A Tobit-regression model combined American Community Survey signals with the available operating data and was integrated into the lead workflow.
Outcomes
What the system made possible.
- A practical ranking signal despite imperfect source data
- A model simple enough to understand and operate
- Long-running production use across changing business conditions
Have a related problem?
Talk directly with an engineer.
Bring the messy version. We will help identify the smallest credible path to production.