Use machine-learning-driven assignment to match pool and spa consultations with eligible consultants using project scope, property, financing, source, and market context.
Start with historical appointments, realistic eligibility rules, and outcomes that have had time to mature.
Organizations can improve assignment by ranking eligible representatives for each qualified opportunity using context available before the conversation, while preserving licensing, territory, capacity, and other operating rules. In this market, the relevant opportunities include design consultations for new pools, renovations, spas, and outdoor-living projects. The approach should be validated on mature outcomes before live use.
Pool projects vary sharply in design complexity, budget, lot conditions, permitting, financing, and homeowner expectations. The consultant who excels on a straightforward replacement may not be the best fit for a custom outdoor environment.
Lithium Six tests whether those differences create repeatable representative-opportunity fit on held-out history. It does not assume a demographic, source, project, or salesperson effect before the evidence supports it.
Use fields that are known before the sales conversation begins.
Rank only representatives who can realistically receive the appointment.
| Measurement | Why it matters | How to use it |
|---|---|---|
| Consultation-to-contract win rate | Measures whether assignment creates value across design consultations for new pools, renovations, spas, and outdoor-living projects. | Compare appointments that have had enough time to reach an outcome |
| Revenue per consultation | Measures whether assignment creates value across design consultations for new pools, renovations, spas, and outdoor-living projects. | Compare similar periods, lead sources, and assignment approaches |
| Assignment alignment | Measures whether assignment creates value across design consultations for new pools, renovations, spas, and outdoor-living projects. | Compare appointments assigned to recommended representatives |
| Deposit value | Measures whether assignment creates value across design consultations for new pools, renovations, spas, and outdoor-living projects. | Review prediction accuracy alongside actual sales and revenue |
| Cancellation rate | Measures whether assignment creates value across design consultations for new pools, renovations, spas, and outdoor-living projects. | 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.
Organizations can improve assignment by ranking eligible representatives for each qualified opportunity using context available before the conversation, while preserving licensing, territory, capacity, and other operating rules. In this market, the relevant opportunities include design consultations for new pools, renovations, spas, and outdoor-living projects. The approach should be validated on mature outcomes before live use.
No. The CRM remains the system of record. Lithium Six uses approved CRM data to evaluate and return a ranked list of eligible representatives.
The useful amount depends on appointment volume, team size, outcome quality, and opportunity variety. Isotope Labs begins with a complimentary data-readiness review.
Yes. Recommendations can be limited by territory, availability, skills, licensing, capacity, and other client-defined eligibility rules.
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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.
