ModerateUpdated Oct 18, 2026
A solar call center dials the leads a model says will convert
A propensity model trained on past customers sorts leads green, yellow and red so setters stop dialing the bottom deciles first.
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Inputs
- Lead fields you have
- Past win/loss export summary
- Current calls per appointment baseline
Help a solar call center manager design a test of predictive lead scoring.
Use only the inputs. Do not invent lift numbers.
Fields: {{fields}}
History: {{history}}
Baseline: {{baseline}}
Write: how to bucket leads, how to keep cadence equal across buckets, what to measure weekly, and when to call the test.Example output
An illustration of the shape of the answer. Not a reported result.
Bucket by score decile: top 2 green, next 2 yellow, rest red. Same 6-touch cadence for all. Measure connected calls per set appointment by bucket weekly. Call the test after each bucket has at least 50 appointments.
Needs one owner and a connection to the CRM or the recorder.
The problem
Rules-based scoring used a few static data points. A lead's priority never changed, and setters needed 180 connected calls per scheduled appointment.
How AI solves it
Train a lead score on existing customers plus third-party consumer data.
Bucket leads into green, yellow and red.
Keep the same call cadence and measure appointment rate by bucket.
Refresh scores as new customers and lead behavior come in.
Tools that do this
- Faraday
Consumer data and propensity models for lead scoring.
Workflow
Step 1
Export won and lost leads
Step 2
Agree on a baseline (calls per appointment)
Step 3
Run a bucketed test before changing cadence