RevOps Case Study 3: Forecasting Model Rebuild
Industry:
B2B SaaS – Mid-market sales motion with a mix of inbound and outbound pipeline. Deal sizes ranged from $25K to $100K ACV, with a ~60-day average sales cycle and a multi-stakeholder buying process.
Problem:
The company’s sales forecast consistently missed targets by 15–20%, creating downstream issues in hiring, budgeting, and investor reporting. Forecasting inputs varied by rep and lacked standardization across stages and pipeline categories.
Solution:
We rebuilt the forecasting model from the ground up, introducing standardized pipeline stages, forecast categories, and weighting rules. The model combined historical conversion rates, rep-entered confidence levels, and real-time CRM data to produce a dynamic forecast aligned with executive reporting needs.
Execution:
- Audited current opportunity stages and definitions in Salesforce
- Interviewed sales leadership and finance to align on forecasting needs
- Defined new pipeline stages (e.g., Commit, Best Case, Pipeline) with clear criteria
- Applied historical win rates to each stage to auto-weight the forecast
- Built a forecasting dashboard in Looker Studio with filters by team, owner, and forecast category
- Rolled out weekly forecast review cadences and trained reps on proper stage usage
Results:
- Forecast accuracy improved from 78% to 93% within two quarters
- Reduced forecast volatility by 60%
- Increased leadership trust in CRM data
- Enabled Finance to align hiring and budget plans to realistic revenue projections
Dashboard:

Key Takeaways & Learnings:
- Forecasting isn’t just a sales ops issue—it requires alignment with finance, CS, and leadership
- A forecast model must balance qualitative rep input with historical, data-driven probabilities
- Training reps on stage definitions is just as critical as building the model itself
- Reps adopt the process faster when they see the forecast used in executive reporting