Operational Sales Optimization with Machine Learning
We turn your historical CRM data into a predictive engine that increases win-rate by identifying the companies you have the highest win rate with.
How it works
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Upload your CRM data
Historical deals in. Signals out.
You securely share your CRM history:
Won and lost deals
Seller assignments
We only need structured deal data, no complex BI setup required.
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We analyze win patterns
Identify where you actually win.
We apply statistical modeling and machine learning to uncover:
Which company profiles convert at the highest rate
Which seller performs best with which company type
Which factors truly predict a closed deal
This reveals your highest-probability segments — not assumptions, but measurable patterns.
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We score new companies
Predict win probability per seller.
For every new lead:
We calculate its similarity to high-win company profiles
We estimate win probability per seller
We rank the best seller–company match
Instead of random distribution, each lead is routed to the highest-probability closer.
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The system improves continuously
More data → better allocation.
As new deals close:
The model retrains
Prediction accuracy increases
Seller strengths become clearer
Allocation becomes sharper
Your win-rate optimization compounds over time.
WHY IT WORKS
Focused on leverage – In B2B, improving win-rate drives revenue faster than increasing lead volume.
Data-backed allocation – Leads are assigned based on probability, not hierarchy or intuition.
Immediate impact – Even small improvements in win-rate translate directly to revenue growth.
Self-improving model – The system gets smarter as your CRM data grows.
What you get
A clear map of your highest-converting company profiles
Seller-specific performance insights
Win probability scoring for every new lead
Ranked lead allocation recommendations
Continuous model refinement
Measurable win-rate improvement over time
