Potential pre-assignment inputs
Useful fields depend on what is consistently populated.
- lead and source
- property and measurement context
- project and estimate type
- market and timing
- assigned salesperson
- won and lost result

Evaluate Roofr lead, estimate, property, source, representative, and outcome history for machine-learning-driven roofing assignment.
Start with historical appointments, realistic eligibility rules, and outcomes that have had time to mature.
A measurement or proposal platform can standardize the project record, but it does not automatically determine which eligible salesperson is best matched to the homeowner, source, project, and market. Historical outcome modeling can test that fit.
The integration maps CRM-specific objects and stages into a stable appointment-level record: prospect, source, appointment, assigned representative, property or opportunity context, and mature outcome.
Client-specific mappings remain explicit because pipelines, custom fields, and operating definitions differ even among companies using the same platform.
Useful fields depend on what is consistently populated.
CRM automation and business rules remain authoritative.
| Measurement | Why it matters | How to use it |
|---|---|---|
| Closed appointment win rate | Confirms that the Roofr integration supports a reliable assignment decision. | Compare appointments that have had enough time to reach an outcome |
| Revenue per appointment | Confirms that the Roofr integration supports a reliable assignment decision. | Compare similar periods, lead sources, and assignment approaches |
| Recommendation alignment | Confirms that the Roofr integration supports a reliable assignment decision. | Compare appointments assigned to recommended representatives |
| Probability calibration | Confirms that the Roofr integration supports a reliable assignment decision. | Review prediction accuracy alongside actual sales and revenue |
| Assignment coverage | Confirms that the Roofr integration supports a reliable assignment decision. | Check for changes in lead mix and team performance before changing your approach |
Isotope Labs organizes your appointments, representative assignments, and outcomes into a history we can evaluate, using information available before each assignment.
We test predictions against historical outcomes kept separate from model training to identify reliable matches between opportunities and sales representatives.
We evaluate historical assignment scenarios that reflect representative eligibility, territories, availability, and workload limits.
See which recommendations your team uses and how those appointments perform, including close rate and revenue per appointment as outcomes become available.
A measurement or proposal platform can standardize the project record, but it does not automatically determine which eligible salesperson is best matched to the homeowner, source, project, and market. Historical outcome modeling can test that fit.
No. Roofr remains the system of record. Lithium Six adds a client-specific ranking layer for eligible representatives.
Only fields available before assignment should be considered. Outcome-revealing fields such as a final stage, sale date, or contract value must be excluded from prediction inputs.
The first step is a field and outcome mapping, followed by appointment-level quality checks and a held-out historical model evaluation.
Combine CallRail campaign, call, form, source, and qualification context with CRM appointments and outcomes to improve agent matching.
CRM guideEvaluate AccuLynx lead, job, appointment, source, salesperson, property, and outcome data for roofing-specific assignment optimization.
Strategy guideReview the fields, definitions, and history needed for a credible evaluation.
Share a representative CRM export or connect your data. Isotope Labs will evaluate data readiness, representative-performance variation, and the potential value of model-guided assignment before a live rollout.