Revenue Intelligence
Practical writing on pipeline forecasting, churn signal timing, expansion scoring, and the data infrastructure that makes B2B SaaS revenue predictable. Written by practitioners, not a content team.
Why CRM Data Alone Can't Predict Churn — And What's Missing
CRM activity logs tell you what reps did, not what customers experienced. Here's the signal layer most RevOps teams are missing.
Why Your Forecast Should Be a Range, Not a Number
P10/P50/P90 forecasting isn't hedging — it's how you have an honest conversation with your board about uncertainty.
Product Usage Signals That Actually Predict Expansion Revenue
Feature adoption velocity, API call frequency, seat utilization — the product signals that correlate with upsell, based on analysis across 40+ B2B SaaS cohorts.
The RevOps Stack Integration Guide: CRM, PLG, and Billing in One Model
How to connect Salesforce, Mixpanel, and Stripe without a data engineering team and what you can model once you do.
Rep Forecast Accuracy as a Coaching Lever
When you can measure which reps consistently over- or under-forecast, the coaching conversation changes from opinion to data.
How Early Is Early Enough? Churn Signal Timing in B2B SaaS
The 60-90 day pre-renewal window is the last viable intervention point. Here's what the signal data shows about when churn is actually decided.
Why Gradient Boosting Works Better Than Linear Models for Revenue Forecasting
Revenue outcomes aren't linear. Here's why ensemble models handle the non-linearities in deal progression better than regression.
The CS-to-RevOps Handoff: How to Stop Missing Expansion Signals
CS teams see the product engagement data. RevOps teams manage the renewal calendar. They're rarely talking about the same accounts at the same time.
ARR Forecasting for the CFO Review: What Finance Actually Needs
CFOs don't want a confident number. They want a defensible range and the logic behind it. Here's how to frame a forecast they'll trust.
HubSpot vs. Salesforce: Does Your CRM Choice Affect Forecast Accuracy?
The CRM matters less than how it's being used. But there are systematic differences in data completeness between platforms that affect model quality.
Forecasting Across Segments: When One Model Isn't Enough
SMB and enterprise customers churn for different reasons and expand at different rates. A single forecast model that ignores segment boundaries will be wrong more often.
Billing Signals Most RevOps Teams Ignore Before Churn
Seat count reductions, invoice dispute history, and payment delays are all visible in Stripe weeks before a churn conversation starts. Most teams aren't looking.