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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
HubSpot’s reporting limitations make it nearly impossible to create comprehensive actual vs forecasted revenue comparisons at the company level across multiple pipelines. The platform lacks the tools to preserve historical forecasts and compare them against actual outcomes.
Here’s how to build sophisticated forecast variance tracking that combines live HubSpot data with advanced calculations to monitor forecast accuracy across all your revenue streams.
Build comprehensive variance tracking using Coefficient
Coefficient enables sophisticated forecast variance tracking by combining live HubSpot data with advanced spreadsheet calculations. You can preserve historical forecast predictions and automatically compare them against actual revenue outcomes with real-time variance monitoring.
How to make it work
Step 1. Import historical deal data with company associations.
Set up filtered imports to pull deal data from all pipelines with company associations. Include fields like deal amount, close date, pipeline, and deal stage. Use date filters to focus on your forecast periods and configure scheduled refreshes to keep data current.
Step 2. Create forecast models with weighted probabilities.
Build formulas that calculate forecasted revenue using weighted pipeline probabilities and historical close rates. For example: =Deal_Amount * Stage_Probability * Historical_Close_Rate. Apply these calculations across all companies and pipelines.
Step 3. Preserve forecast baselines with Snapshots.
Use the Snapshots feature to capture monthly or quarterly forecast predictions as historical baselines. Set up automated snapshots to preserve point-in-time forecasts before they get updated with new data. This creates the historical record you need for variance analysis.
Step 4. Track actual revenue with separate imports.
Create separate imports for closed-won deals to track actual revenue by company and pipeline. Filter for deals with “Closed Won” status and use the same company/pipeline dimensions as your forecast data for easy comparison.
Step 5. Build variance analysis formulas.
Create formulas that compare actual vs forecasted revenue with percentage accuracy metrics. For example: =(Actual_Revenue – Forecasted_Revenue) / Forecasted_Revenue. Use conditional formatting to highlight significant variances and set up automated alerts when variance exceeds defined thresholds.
Step 6. Configure automated variance alerts.
Set up Slack and Email Alerts to notify stakeholders when variance exceeds 15% deviation or other defined thresholds. Include variables in your alerts to show specific variance amounts and percentages.
Start tracking forecast accuracy across all pipelines
This approach provides the multi-pipeline company revenue variance reporting that HubSpot cannot deliver natively, with automated tracking and real-time monitoring built in. Get started with comprehensive forecast variance tracking today.
