Use appointment-level history
Separate routine dispatch from quotes or consultations where choosing a salesperson could change the outcome.
Evaluate performance-based assignment for Jobber sales workflows.
Use qualified quote and estimate history to test whether assigning the right eligible salesperson can improve high-value Jobber opportunities.
CRM guideKeep Jobber as the system of record
Every Jobber account uses requests, quotes, jobs, and users differently. The complimentary fit review maps the client’s real workflow before any model is proposed.
In brief
Evaluate Jobber client, request, quote, job, source, property, and assigned-user data for performance-based sales assignment.
Turn Jobber history into an assignment decision
Jobber is used across many field-service businesses, including operations where most work is transactional and others where quotes represent meaningful considered sales. Assignment optimization should focus only on the latter.
A useful implementation identifies the pre-sale event, assigned person, customer and property context, mature outcome, and value fields that are reliable in the client’s Jobber data.
Compare eligible agents
Evaluate eligible users against the specific client, property, service, source, and timing context.
Measure the result
Track whether recommendations are used and reconcile them with later approved quotes or won jobs.
Build the decision around usable evidence.
Useful Jobber data
The initial mapping focuses on fields available before assignment and reliable outcome data after the appointment.
- Clients and properties
- Requests and scheduled visits
- Quotes and quote status
- Assigned users or salespeople
- Service category and source
- Approved value and outcome timing
What Lithium Six adds
The CRM continues to manage the workflow. Lithium Six adds a client-specific prediction for the appointment-agent pairing.
- Appointment-level normalized history
- Leakage-safe feature selection
- Eligible-user ranking
- Constrained policy backtesting
- Adoption and outcome measurement
A practical Jobber data-readiness review
| Data area | What we look for | Why it matters |
|---|---|---|
| Decision point | Qualified estimate or consultation | Avoids modeling routine dispatch as sales |
| Outcome | Approved quote or mapped won state | Must reflect the business’s actual process |
| Value | Quoted or approved value used carefully | Post-outcome values are reporting, not prediction inputs |
| User | Stable Jobber user identity | Supports history and live eligibility |
| Coverage | Enough repeated outcomes by context | Determines whether personalization is feasible |
From Jobber data to live guidance
Confirm the workflow
Identify which Jobber request, visit, or quote event represents a qualified sales appointment and which status reliably represents a won or lost outcome.
Map the data
Normalize Jobber contacts, appointments or deals, assigned users, outcomes, and pre-assignment context.
Test predictive accuracy
Compare candidate approaches and backtest assignment policies under realistic operating constraints.
Choose the delivery path
Integration and delivery options are assessed during the fit review; no live in-product workflow should be assumed until the client’s Jobber configuration is validated.
See what your own appointment history supports.
Isotope Labs provides a complimentary CRM integration and historical evaluation before recommending a live rollout. You receive the evidence, limitations, operating requirements, and a clear next step.
Common questions
Does Lithium Six replace Jobber?
No. Jobber remains the operating CRM. Lithium Six provides an additional recommendation at the point where an eligible sales agent is selected.
Can Isotope Labs evaluate Jobber data before launch?
Yes. The initial data integration and historical fit review are complimentary. They are used to assess data quality, predictive signal, operating constraints, and estimated opportunity.
Does every CRM field become a model feature?
No. Fields are screened for availability at assignment time, quality, leakage risk, stability, and predictive contribution. Outcome-revealing fields are excluded.
What happens if the data is incomplete?
The fit review identifies missing fields and unreliable outcomes. Isotope Labs can recommend a narrower model, a data-cleanup period, or a no-go decision when the evidence is not strong enough.
Continue your evaluation
Data guide
Is your CRM data ready for assignment modeling?
Continue with practical guidance, evaluation criteria, and next steps.
Assignment guideMachine learning lead assignment built around the agent-prospect fit.
Continue with practical guidance, evaluation criteria, and next steps.
Comparison guidePredictive assignment does not replace CRM routing rules. It makes them smarter.
Continue with practical guidance, evaluation criteria, and next steps.
Complimentary fit review
See whether your Jobber history supports better assignment.
Request a complimentary data-readiness and historical opportunity review. We will map the practical integration path and explain what your own data can support.
- CRM data-readiness review
- Historical model evaluation
- Constrained assignment backtest
- Plain-language opportunity review