Technology & SaaS
SaaS metrics, product-led growth, and unit economics at decision speed
Track SaaS metrics that matter, model cohort economics, detect product usage dormancy, score expansion propensity, and run pricing experiments, all in one governed platform with real-time data from Stripe, Mixpanel, and your product telemetry.
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SaaS Models
Real-time
Metrics
Cohort
Economics
Challenges We Solve
Industry-specific pain points that structured decision models eliminate.
Metrics Fragmentation
ARR, NRR, LTV/CAC, and payback period live in 6 different tools. Unify SaaS metrics into a single governed source of truth with real-time calculation and cohort segmentation.
Silent Churn Signals
Product usage drops weeks before cancellation, but nobody connects the dots. Detect dormancy patterns, score expansion readiness, and trigger intervention before churn materializes.
Pricing Guesswork
Pricing changes are gut-feel decisions with unquantified revenue impact. Model packaging experiments, analyze willingness-to-pay by segment, and simulate revenue outcomes before launch.
Tech Debt vs Feature Velocity
Engineering wants to refactor; product wants features. Quantify tech debt cost, model velocity impact, and make build-vs-buy decisions with structured analysis instead of opinion.
Use Cases
Industry-specific scenarios powered by DecisionLedger.
Reviews the SaaS metrics dashboard weekly with cohort LTV/CAC payback analysis, identifying that the Q1 enterprise cohort has 18-month payback vs 11-month target, triggering GTM strategy adjustment.
Shifted enterprise GTM to higher-intent channels, reducing payback to 13 months within 2 quarters
Uses feature value attribution to identify that 3 features drive 72% of expansion revenue, while 8 features have <5% adoption. Prioritizes roadmap based on revenue impact, not feature requests.
Increased expansion revenue 24% by focusing engineering on high-impact features
Runs the roadmap commitment credibility model to score delivery reliability by team and initiative size. Identifies that initiatives >3 months consistently slip 40%, driving shift to smaller batches.
On-time delivery improved from 55% to 82% with smaller initiative sizing
Measurable Impact
Based on platform benchmarks across early adopters.
Metrics Latency
Weekly spreadsheet updates
Real-time from billing system
Churn Prediction
Discovered at renewal
Usage dormancy detection 60 days early
Pricing Decisions
Gut-feel with no modeling
Monte Carlo revenue simulation
Roadmap Credibility
55% on-time delivery
Commitment scoring with calibration
Built for SaaS Leaders
Real-time SaaS metrics, cohort economics, and product-led growth intelligence for data-driven technology companies.
SaaS Metrics Engine
Real-time ARR, MRR, NRR, GRR, LTV, CAC, and payback period calculated from subscription data with cohort segmentation and trend analysis.
Product Usage Intelligence
Feature adoption tracking, dormancy detection, and engagement scoring that predicts expansion and churn from product telemetry data.
Pricing Lab
Packaging experiment simulation, willingness-to-pay modeling, and revenue impact forecasting before you change a single price point.
Connects With
Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics
GitHub
GitLab
Linear
Jira
Segment
Mixpanel
Amplitude
PostHog
Stripe
Snowflake
Databricks
SlackGitHubGitLabLinearJiraSegmentMixpanelAmplitudePostHogStripeSnowflakeDatabricksSlackFeatured Models
Pre-built decision models ready to run with your data.
Cohort Ltv Cac Payback
Builds cohorts by acquisition channel and segment, models gross margin LTV, CAC, payback period, and retention curves so growth decisions do not silently destroy cash.
SaaS Metrics Dashboard
Computes 22 SaaS KPIs including ARR/MRR, net and gross revenue retention, LTV/CAC ratio, payback period, Magic Number, Rule of 40, Burn Multiple, Quick Ratio, and a composite efficiency score with health assessment benchmarks.
Expansion Propensity
Predicts upsell/cross-sell readiness with recommended next product.
Net Revenue Retention Decomposition
Splits NRR drift into expansion, contraction, and churn components.
Product Usage Dormancy
Detects feature or seat dormancy patterns predictive of contraction.
Feature Value Attribution
Decomposes revenue and retention impact across product features.
Pricing Experiment Readiness
Scores whether a pricing change has experimental design and rollback path.
Roadmap Commitment Credibility
Scores whether roadmap commitments are realistic based on historical delivery data and capacity.
How It Works
Three steps to structured, auditable decisions.
Connect Product & Revenue Data
Pull subscription data from Stripe/Chargebee, product telemetry from Mixpanel/Amplitude, and engineering metrics from Datadog. Map once, refresh continuously.
Model & Experiment
Run cohort economics, score expansion readiness, detect usage dormancy, and simulate pricing scenarios with Monte Carlo confidence bands.
Decide & Track Outcomes
Route decisions through approval workflows, track roadmap commitment credibility, and calibrate model accuracy against actual outcomes.
Your board wants NRR, LTV/CAC payback, and Rule of 40, but the numbers come from 4 different tools, take 3 days to compile, and never quite match.
ChartMogul / Baremetrics
Subscription analytics without product usage intelligence or decision models
Spreadsheet SaaS models
Static cohort analysis that's stale by the time it's shared with the board
Product analytics tools alone
Usage data without revenue correlation or expansion/churn prediction
Pendo / Gainsight
Customer health scores without structured decision science or Monte Carlo simulation