Increase close rates
Improve conversion by matching each lead to the agent with the highest expected outcome based on prior performance.
Assign every prospect to the agent most likely to win.
Lithium Six by Isotope Labs is an adaptive assignment intelligence engine for high-stakes sales teams. It matches each inbound lead, call, or inquiry to the agent most likely to convert it.
Every leadmatched to the agent most likely to convert
Roster-widecandidate agent outcomes compared for each assignment
<200 msreal-time recommendation returned to dispatch
Pairing modelLead x agent
Lithium Six predicts the expected outcome of each lead-agent pairing rather than scoring the lead alone.
Signal coverage480+
Prospect, project, channel, market, property, and agent prior-performance signals help the system recognize higher-probability assignment opportunities.
Activation layerCRM-native
Your team sees recommendations inside the assignment and dispatch workflows they already use.
Lithium Six Assignment Engine
Adaptive lead assignment for high-stakes sales teams
Current assignment estimate31.2%
Recommended assignment39.0%
Expected profit / appt.$352
Assignment confidence53%
Turn lead flow into controlled advantage.
Once assignment quality is measurable, every inbound opportunity can be assigned with more precision while your existing CRM workflow stays intact.
1
Lift without more leads
Match each lead to the agent most likely to convert, improving yield from the demand already being purchased.
2
Existing workflows stay intact
Recommendations surface inside the CRM and dispatching flow your team already uses.
3
Prior performance becomes measurable
See where each agent has historically converted best by lead profile, project type, channel, and urgency.
4
Closed outcomes improve assignment
As new results come in, recommendations stay aligned with real selling performance.
Lead intake
New leads arrive from paid search, phone, forms, referrals, and partner sources.
Enrich and score
Prospect, project, market, and agent attributes are evaluated together.
Recommend agent
The highest expected close probability is returned to CRM dispatch.
Learn from outcomes
Closed won, lost, and downstream economics refresh future recommendations.
Every assignment has thousands of signals.
The decision is not one lead score. Demographic, household, property, channel, urgency, and project signals are evaluated together to surface assignment strengths.
Combinatorial profile space
348B
possible lead profiles before the right agent is chosen.
CRM, property data, marketing channels, and demographic sources can expose hundreds of useful signals. Even a modest subset creates a profile space too large for manual assignment rules.
348Bprofile combinations from 15 dimensions
x
6 (sample)available agents in the assignment pool
=
2.1Tpossible assignment paths scored
Household income8 groups
Education level6 groups
Age range7 groups
Home value8 groups
Household size6 groups
Marital profile4 groups
Residence tenure5 groups
Dwelling type5 groups
Occupation mix10 groups
Commute profile6 groups
Neighborhood income8 groups
Market density5 groups
Lead channel5 groups
Project type6 groups
Urgency3 levels
Marcuspremium + urgent + high home value
Eliphone + service plan + repeat profile
Noahfinancing + web form + mid-income
Carloscommercial + referral + high urgency
Adamlong-tenure + referral + trust profile
Julianpromo + younger homeowner + standard job
Machine learning built for assignment decisions.
Signals become real-time recommendations through models trained on CRM history, lead context, prior agent outcomes, and closed-loop conversion data.
Machine learning architecture
Your data becomes agent-specific win predictions before assignment.Feature space480+
Pair scoresRoster-wide
Refresh cadenceWeekly
What Lithium Six learns
The system learns which combinations change the expected outcome.1
Lead-agent interaction patterns
Identify where each agent has historically converted best across specific mixes of household profile, home value, channel, urgency, job type, and tenure.
2
Assignment value
Score each incoming appointment against every available agent to estimate the value of the highest-probability assignment.
3
Confidence-calibrated recommendations
See when the top agent meaningfully separates from the next-best option.
4
Closed-loop learning
Won, lost, revenue, and margin outcomes keep assignment guidance aligned with changing markets and agent performance.
Turn prediction into better assignment.
Use the predictor to see how changing lead attributes changes the recommended agent, expected close probability, and confidence level.
Premium homeowner
Marcushighest expected close probability
Financing-sensitive project
Noahhighest prior conversion pattern for financing conversations
Established referral
Adamhigher prior conversion pattern for long-cycle referrals
Prospect profile explorer
Change the lead profile to see which agent should get the opportunity.Candidate-agent predictive assignment
Compare the expected outcome for each available agent.
Predicted best agent
Marcus
Wins because high HHI, graduate education, premium install, and paid-search urgency reinforce the same profile.
High
Confidence based on separation from the next-best agent.
The recommendation reflects the full lead profile, not a single segment label.
Controlled test. Measurable lift.
But does better assignment change real outcomes? Yes. In a completed controlled test, optimized assignment produced higher conversion and more profit.
Over a 3-month double-blind testing period, optimized assignment produced an 18.7% close-rate lift and $230K incremental revenue per sales agent.
The test was structured to isolate assignment quality: assignment decisions were blinded so the measured lift came from assignment performance, not behavior change.
+4.6 ptsabsolute close-rate improvement
+18.7%relative conversion lift
$230Kincremental revenue per agent over 3 months
99%confidence versus baseline
Control vs experiment
Optimized assignment converted more prospects from the same appointment flow.
Control
24.8%
Experiment
29.4%
Baseline
24.8%
Optimized
29.4%
+4.6 ptsabsolute close-rate shift
99%statistical confidence
Two-tailed T-test: The pre/post shift is statistically significant, confirming the improvement is unlikely to be explained by normal sales-rep variation.
Want to understand what this could mean for your lead flow?
Review your current assignment process, available data, and likely launch path with Isotope Labs.
Schedule a Strategy Session
Where lift comes from.
While the models are complex, their insights are often very intuitive. Changes in lead-agent combinations create measurable opportunity changes.
Predicted close probability by lead profile
Compare expected close rates across candidate agents for different lead types.Why the recommendation changes
The historical signals behind the predicted outcomeCollect
Use the lead, project, channel, region, urgency, and conversation context you already capture.
Enrich
Add demographic, household, geography, and property context where available.
Combine
Evaluate how signals work together instead of relying on one-variable assignment rules.
Score
Predict the expected outcome for each lead-agent pairing and learn from closed outcomes.
See how this would connect to your CRM and dispatch workflow.
Walk through data access, shadow scoring, calibration, and activation options.
Discuss Implementation
Lift concentrates in specific pairings.
Optimization does not treat every agent or lead type the same. The largest gains appear where prior outcomes show clear assignment advantage.
Per-agent incremental lift
Expected gain when each agent receives appointments aligned to prior conversion patternsPrior-performance assignment map
See which agent has the highest expected outcome for each lead profileFits inside your CRM flow.
After fit is established, Lithium Six connects to existing sales systems, ingests outcome data, and returns optimized recommendations where dispatch decisions are already made.
Implementation path
Launch through controlled testing, shadow scoring, or direct CRM activation.Connect
Link CRM, intake, dispatch, and outcome data through API, webhook, export, or warehouse access.
Ingest
Bring together appointment history, agent availability, dispositions, revenue, and close outcomes.
Build
Train, validate, and calibrate models that treat the agent as part of the prediction.
Deploy
Surface recommended agents, scores, and confidence signals in your existing dispatch workflow.
What makes Lithium Six different
Patent-pending assignment intelligence built for operational sales teamsAgent performance intelligence
Score each agent against the lead, project, channel, urgency, and demographic profile using prior outcomes.
Combinatorial scoring
Evaluate attribute combinations instead of relying on one-variable segments or simple lead scoring.
Ongoing calibration
Refresh recommendations as new outcomes and operating patterns appear.
CRM-native activation
Put recommendations where dispatchers already work, reducing adoption friction.
Operational impact after launch.
Once recommendations are live, the operating model improves conversion, reduces wasted spend, and keeps dispatch aligned with real constraints.
Reduce wasted lead spend
Extract more value from existing channels before expanding acquisition budgets or adding headcount.
Use agent performance history
Recognize the lead types, geographies, project types, and situations where each agent has historically performed best.
Replace passive rotation
Move beyond equal distribution when equal distribution is not equal opportunity.
Improve speed-to-lead
Make assignment decisions quickly while respecting availability, territory, and operating rules.
Learn from outcomes
Use every won, lost, revenue, and margin result to improve future assignment decisions.
Where assignment intelligence matters.
Lithium Six is strongest where demand is expensive, response time matters, and the person handling the opportunity can change the probability of winning.
Model the business impact.
Translate assignment lift into economics using your appointment volume, team capacity, margin, and close rate.
Account assumptions
Business inputsAppointment capacity: 6 agents x 3 appointments/day x 22 selling days = 396 appointments per month.
18.7%
Predicted operating outcome
Annualized impact estimateIncremental wins220
Incremental revenue$5.51M
Incremental profit$2.31M
Optimized close rate29.4%
Want help pressure-testing these assumptions?
Bring your appointment volume, close rates, and CRM constraints to a private strategy session.
Review Your Scenario
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