Sales & Revenue Intelligence
Probabilistic revenue forecasting, not pipeline fiction
Score deal quality, validate close dates, build bottoms-up probabilistic forecasts, and protect margins with discount guardrails — replacing CRM gut-feel with structured decision science.
Challenges We Solve
Common pain points that structured decision models eliminate.
Pipeline Opacity
Pipeline data tells you what reps entered, not what's real. Score deal quality, validate close dates against historical patterns, and expose pipeline risk objectively.
Discount Leakage
Every deal gets a 'special' discount. Model the cumulative revenue impact of discount patterns, enforce guardrails, and protect margins at scale.
Forecast Inaccuracy
Quarterly forecasts swing wildly from commit to close. Build probabilistic forecasts from pipeline stage, deal velocity, and historical conversion data.
Churn Blind Spots
Renewals slip through the cracks until it's too late. Score renewal risk continuously and trigger intervention playbooks before customers defect.
Use Cases
How teams use DecisionLedger to make better decisions.
Runs the deal quality scoring model across the entire pipeline weekly, flagging deals with inflated close dates, missing champion signals, or stalled velocity — replacing forecast calls with data.
Forecast accuracy improved from 60% to 88% with probabilistic scoring
Uses the discount curve model to analyze cumulative margin impact of discount patterns by segment, rep, and deal size — setting guardrails that protect margins without blocking deals.
Recovered 3.2% margin by enforcing data-driven discount guardrails
Deploys the renewal risk model to score every customer's churn probability 90 days before renewal, triggering intervention playbooks for high-risk accounts.
Gross retention improved from 88% to 94% with proactive intervention
Measurable Impact
Based on platform benchmarks across early adopters.
Forecast Accuracy
+/-25% variance
+/-8% accuracy
Discount Control
Ad-hoc approvals
Model-backed guardrails
Renewal Risk
Discovered at renewal
90-day early warning
Pipeline Hygiene
Rep self-reported stages
Velocity-validated scoring
Connects With
Featured Models
Pre-built decision models ready to run with your data.
Close Date Realism
Compares stated close dates against historical stage-velocity patterns to flag unrealistic timelines. Outputs expected close date, slip probability, and days of bias per rep and stage.
Deal Quality Scoring
Multi-signal deal quality score combining ICP fit, intent signals, engagement depth, mutual plan indicators, and stakeholder mapping. Outputs composite score with driver decomposition using weighted MCDA scoring.
Pipeline Sla Forecaster
Predicts the likelihood of delayed or failed refreshes based on job history, dependencies, and runtime patterns. Recommends preemptive actions.
Renewal Risk Score
Assesses likelihood of churn by cohort, segment, and account health signals. Combines usage trends, support ticket patterns, stakeholder changes, and contract terms into a weighted renewal risk score with per-account recommendations.
Revenue Probability Model
Revenue Probability Model -- Monte Carlo simulation engine that models SaaS revenue trajectories. Takes go-to-market assumptions (ACV, close rate, pipeline, churn, sales cycle, ramp time) and runs 10,000 stochastic simulations to produce probability distributions for hitting revenue targets ($1M, $5M, $10M, $20M ARR). Outputs confidence intervals, time-to-target distributions, and sensitivity analysis on which levers move the needle most.
Stage Conversion
Calculates stage-to-stage conversion probabilities segmented by rep, source, segment, and deal size. Identifies where pipeline leaks occur and which segments convert best using Bayesian probability estimation.
Win Propensity
Probability of win given deal attributes including stage, source, segment, rep, deal size, engagement, and competitive presence. Uses logistic regression or ML classification with SHAP-based driver explanations. Falls back to heuristic scoring when training data is unavailable.
Win Rate Discount Curve
Maps the relationship between discount depth and win rate to identify diminishing returns and optimal discount guardrails by segment, deal size, and competitive situation.
How It Works
Three steps to structured, auditable decisions.
Score & Qualify Pipeline
Automatically score every deal on quality, win propensity, and close date realism. Surface the pipeline that actually matters.
Forecast & Protect
Build bottoms-up probabilistic revenue forecasts, detect discount patterns, and model pricing scenarios across segments.
Retain & Expand
Score renewal risk, identify expansion opportunities, and track customer health signals to protect and grow recurring revenue.
Your CRO commits a number every quarter based on what reps say they'll close. How many times has that number been right?
CRM pipeline reports
Opportunity stages that reflect rep optimism, not statistical close probability
Clari / BoostUp forecasting
AI-signal tools that predict revenue but can't run risk models or discount analysis
Spreadsheet discount approvals
Ad-hoc margin erosion with no pattern analysis or guardrail enforcement
Manual churn tracking
Renewal risk discovered at renewal time instead of 90 days in advance