Compare pipeline forecast accuracy by company across multiple months in HubSpot

HubSpot doesn’t provide tools to track and compare pipeline forecast accuracy over time at the company level. The platform lacks historical forecast preservation and variance analysis capabilities needed for meaningful accuracy tracking.

Here’s how to build comprehensive forecast accuracy tracking that preserves historical predictions and automatically calculates variance trends to identify forecasting strengths and weaknesses across companies and time periods.

Build comprehensive accuracy tracking using Coefficient

Coefficient enables comprehensive forecast accuracy tracking through Snapshots and automated variance calculations. You can preserve point-in-time forecasts from HubSpot and automatically compare them against actual outcomes to track accuracy trends over multiple months.

How to make it work

Step 1. Create historical forecast baselines with Snapshots.

Use the Snapshots feature to capture monthly forecast predictions by company and pipeline, preserving point-in-time forecasts. Set up automated snapshots on the last day of each month to create historical baselines. This gives you the historical data needed for accuracy comparisons that HubSpot cannot preserve.

Step 2. Track actual revenue outcomes with separate imports.

Import closed-won deal data with company associations to track actual revenue outcomes. Filter for deals with “Closed Won” status and include actual close dates and revenue amounts. Set up scheduled refreshes to automatically update actual results as deals close.

Step 3. Build variance analysis formulas.

Create formulas comparing forecasted vs actual revenue with percentage accuracy calculations. For example: =ABS(Actual_Revenue – Forecasted_Revenue) / Forecasted_Revenue. Build these calculations for each company and pipeline combination to track accuracy at a granular level.

Step 4. Create month-over-month accuracy trending.

Build formulas that track accuracy trends over 3, 6, or 12-month periods. Use functions like AVERAGE and TREND to identify improving or declining forecast performance. Create charts that visualize accuracy trends by company to spot patterns and seasonal variations.

Step 5. Build company comparison scorecards.

Aggregate accuracy metrics across companies to identify forecasting strengths and weaknesses. Create summary tables showing average accuracy by company, best and worst performing months, and consistency metrics. Use conditional formatting to highlight top and bottom performers.

Step 6. Set up automated monthly accuracy updates.

Configure scheduled refreshes to automatically update accuracy calculations monthly. Add Slack and Email Alerts to notify stakeholders when monthly accuracy reports are updated or when significant accuracy changes occur.

Track forecasting performance with data-driven insights

This provides the multi-month pipeline forecast accuracy tracking that HubSpot cannot deliver, enabling data-driven improvements to your forecasting processes. Start tracking your forecast accuracy today.