Sales Forecasting
Sales forecasting is the data-driven prediction of future revenue — for SaaS, that means projecting new bookings, renewals, expansion, and churn across a specific period. Accurate forecasts drive hiring, budgeting, and investor confidence. Inaccurate ones cause cash crunches and missed targets.
A SaaS company forecasts Q4: (1) Weighted pipeline of $2M in new business (using historical stage probabilities), (2) $1.5M in renewals due (at 92% renewal rate = $1.38M), (3) $300K in expansion pipeline (at 60% close rate = $180K), (4) Expected churn of $100K. Total forecast: $2M + $1.38M + $180K - $100K = $3.46M.
Here's the dirty secret: most sales forecasts are fiction dressed up as data. Reps commit deals based on vibes, managers roll up numbers they know are inflated, and everyone acts surprised when actuals come in 30% below forecast.
The fix isn't better guessing — it's removing guessing entirely. Historical win rates by stage. Actual days-in-stage patterns. Commit vs. close analysis by rep. When you forecast based on what actually happened, not what reps hope will happen, accuracy goes from 60% to 85%+.
The RevOps role: build the forecast model from CRM data, enforce data hygiene (close dates, amounts, stages), and run the forecast review process. When RevOps owns forecasting, the number becomes a tool for decisions rather than a fiction for board decks.
Define ItOther Definitions
“Sales forecasting is the process of estimating future sales revenue over a specific time period based on historical performance, current pipeline, market conditions, and sales team input.”
“Sales forecasting is the process of predicting future sales performance. It enables businesses to make informed decisions about resource allocation, budgeting, hiring, and growth strategy.”
“Modern sales forecasting combines CRM pipeline data, historical win rates, rep performance patterns, and AI-driven signals to predict which deals will close and when — replacing gut feel with data-driven predictions.”
Sales forecasting predicts future revenue using a combination of pipeline data, historical patterns, and market inputs. Gartner emphasizes the multi-input approach: past performance, current pipeline, and market conditions. Salesforce focuses on the decision-making application: budgeting, hiring, resource allocation. Clari adds the modern angle: AI and historical patterns replacing intuition.
For B2B SaaS, forecasting must account for recurring revenue components: (1) New business — net new logos and bookings; (2) Renewals — existing contracts coming due; (3) Expansion — upsells, cross-sells, and seat additions; (4) Churn/contraction — expected losses and downgrades.
Common forecasting methods include: Weighted pipeline (deal value × stage probability), Historical trending (past performance projections), Rep-level bottoms-up (individual commits rolled up), and AI/ML models (pattern recognition on historical deal behavior).
MistakesCommon Mistakes
Using static stage probabilities instead of actual historical win rates
Poor CRM hygiene — wrong close dates, stale stages, inaccurate amounts
Not segmenting forecasts (new vs renewal vs expansion behave differently)
Over-relying on rep confidence without behavioral data to validate
Ignoring churn and contraction in the forecast model
Forecast accuracy below 80%?
We build forecast models from your actual win rate data — so the number you commit is the number you hit.
Experience across
