Data-Driven ML Predictions.
A metrics-driven prediction engine that achieves ML-like predictive power without deploying ML models in production.
Sales Teams, Replace Anecdotal Advice with Data
Lead Conversion Prediction
Predict conversion probability using historical data and behavioral patterns from similar leads.
Opportunity Scoring & Recommendations
Score opportunities using MEDDPICC, Fit, and Engagement scores based on 5,000+ historical deals.
Sales Operations, Scale Data-Driven Guidance
Pipeline Intelligence & Risk Identification
Identify at-risk opportunities early by detecting qualification gaps and competitive threats.
Resource Allocation & Strategic Planning
Understand what marketing assets and activities drive success to optimize resource allocation.
Technical Teams, Deploy Without ML Complexity
No ML Infrastructure Required
All calculations run in Python/Apex within Salesforce with no ML libraries or GPU needed.
LLM-Powered Explanation
LLM explains pre-computed results naturally while data stays within your Salesforce environment.
Explore ML Recommendation Features
Discover how our data-driven prediction engine helps you make smarter sales decisions.
Powerful Capabilities
ML-Validated Scoring
Advanced machine learning models analyze multiple data points to calculate accurate conversion probability scores for each lead.
Next Step Recommendations
Get data-driven next step recommendations unique to each opportunity based on similar past deals, customer profile, deal characteristics, and engagement patterns.
Native Salesforce Security
All calculations run entirely within your Salesforce environment using Python/Apex. No external ML infrastructure or data transmission required.
Explainable Predictions
Formula-based approach vs. black box ML models. Understand WHY predictions are made, with LLM-powered explanations of pre-computed results.
Frequently Asked Questions
We trained 6 industry-standard ML models (XGBoost achieved 0.933 AUC, 87.8% accuracy) on 5,000 historical opportunities to identify the most predictive features. We then extracted those insights into simple formulas that achieve 91.3% agreement with ML predictions. All calculations happen in Python/Apex within Salesforce - no ML infrastructure needed.
The system is backed by rigorous analysis of 5,000 opportunities across 109 fields with less than 2% missing data. We used 10 Jupyter notebooks covering EDA, win/loss analysis, feature importance, segmentation, sales velocity, pricing patterns, activity impact, and loss reasons. Six ML models identified consensus feature rankings, which were then translated into formulas.
For sales reps: Get data-driven guidance instead of anecdotal advice, especially valuable for new reps and territory realignments. For sales leadership: Combine experience with data during pipeline reviews to scale guidance and improve win rates. For sales ops/marketing: See what assets and activities actually drive success, providing justification for strategic investments.
Opportunities are scored using MEDDPICC (50% weight) for sales qualification quality, Fit Score (25% weight) for ICP alignment, and Engagement Score (25% weight) for buyer intent signals. This weighting was validated through ML analysis showing 0.89 r² correlation with win/loss outcomes. MEDDPICC ranked #4 in our 6-model consensus feature ranking.
Yes, the system uses Monte Carlo simulation (industry standard for modeling uncertainty) to run what-if analysis. For example, see how completing Budget Confirmation impacts win probability with 80% confidence intervals. The system runs 1,000 iterations to provide statistically rigorous probability distributions.
All calculations run natively in Salesforce using Python/Apex. The LLM explains pre-computed results, ensuring no data leaves your secure environment. Recommendations appear as fields on Lead and Opportunity records and can be used in reports, dashboards, and automation flows. No external integrations or ML infrastructure required.
Why Choose ML Recommendation Engine
Backed by 5,000+ Historical Data
Rigorous data science analysis across 10 Jupyter notebooks and 6 industry-standard ML models (XGBoost, Logistic Regression, Random Forest, SVM, KNN, Decision Tree) validates every formula and recommendation.
91.3% Prediction Accuracy
Our formulas achieve 91.3% directional agreement with ML predictions (0.89 r-squared, 4.2% MAE) without requiring ML infrastructure - just formulas that run natively in Salesforce.
Three-Pillar Scoring Framework
MEDDPICC (50%), Fit Score (25%), and Engagement Score (25%) combine to create comprehensive opportunity assessment, validated through ML feature importance consensus.
Transform Sales with Data-Driven Predictions
See how GPTfy's ML Recommendation Engine provides ML-like accuracy without ML complexity. We'll demonstrate how 5,000+ historical opportunities power your sales predictions in just 30 minutes.