Sure our forecast measures our sales performance, but how do we measure our forecast performance?

Must-Have KPIs to Track to Ensure Forecast Accuracy.

Without the right metrics and procedures in place, we have no insight into how well we actually forecast. Moreso, when it fails, we don't know how to make it better.

So, in order to make your most accurate sales forecasts ever, make sure you have these forecasting accuracy metrics and KPIs in place...

  1. Annual forecast vs. quarterly results
  2. Forecast by region and/or product group
  3. Pipeline coverage
  4. Forecast category and movements
  5. Opportunity age
  6. Risk score

We gathered a panel of some of the best revenue and sales operations leaders, consisting of:

  • Stephen Daniels, Head of Revenue Operations, Branch
  • Victoria Moss, VP, Revenue Operations, Greenhouse
  • Anu Krishnakumar, VP, Global Sales Operations, Smartbear

The group discussed how exactly they measure the accuracy of their sales forecasts. Here’s what they had to say. You can also watch the full recording on the essential sales forecasting accuracy KPIs  at any time, here.

1. Annual forecast vs. quarterly results

Victoria Moss, VP, Revenue Operations at Greenhouse recommends that at the end of each quarter you re-examine your annual revenue or sales forecast through the lens of the quarterly results.

Check and see if your yearly revenue or sales goals are still attainable, or if you will surpass them. Regardless of the outcome, this will either prevent an end-of-the-year scramble to make goal, or give support, onboarding, and/or success teams a chance to prepare for additional users.

2. Forecast by region and/or product group

For greater forecast accuracy, it is important to adjust your sales forecast KPIs to fit the needs of your situation. Two examples are within geographic regions or for different size companies.

Stephen Daniels, Head of Revenue Operations at Branch breaks his forecasts up by NA, EMEA, and APAC. Each of those regions has different working cultures and therefore deals have different expectations. Applying the same sales cycle to all of them will only result in a less accurate sales or revenue forecast. To find the benchmark, he recommends applying a historical win rate.

Tori echoes the sentiment, recommending a best practice of breaking up your forecast by the company size. Such as the following:

Small business

30-day sales cycle. Forecast on deal volume and trends, calculated by opportunities x win rate x average account value.

Mid-Market

Deal-by-deal forecast with a “gut-check” based on quota capacity.

Enterprise

Deal-by-deal forecasting with weekly MEDDPICC-driven inspections by both directors and managers.

3. Pipeline coverage

"Pipeline coverage is table stakes,” says Anu Krishnakumar, VP, Global Sales Operations at Smartbear. Every company should be looking to have between three to 5 times their forecast covered in their pipeline. Stephen adds that he calculates pipeline for different geographic regions based on the historical amount needed to meet expectations.

4. Forecast category and movement

Forecast categories: included deals, excluded deals, and why opportunities fall within them are very important. If sales reps and managers are doing a proper job of forecasting, you should be able to gather good information about what will close, and what’s at risk.

Tori likes to look at what was called “likely” on day one of month one of each quarter, compared to day one of month two. She looks for deals that have been downgraded, and which ones have been added, and why.

5. Opportunity age

Also consider the age of an opportunity in these forecasts. If one starts to draw out for longer than the average sales cycle, risk on it should be gradually increased. As extended deals are statistically less likely to close as time goes on.

6. Risk score

Stephen also loves to use BoostUp’s risk score to judge the accuracy of his forecast. The AI monitors deal activity, including buyer and seller engagement through all sales channels to determine if there is meaningful communication occurring and if the deal is being progressed. If the forecast includes a lot of high-risk deals, it’s time to take action.

To watch the full recording on Forecast Accuracy Metrics & KPIs, click here.

FAQ - Forecast Accuracy KPI

What is forecast accuracy, and why is it essential for demand planning?

Forecast accuracy measures how closely your predictions align with actual outcomes, playing a pivotal role in demand planning. Its benefits include:

  • Improved Resource Allocation: Helps businesses optimize inventory and minimize waste.
  • Better Financial Outcomes: Ensures accurate budgeting and revenue forecasting.
  • Enhanced Customer Satisfaction: Reduces stockouts and ensures timely product delivery.

 

What are the main challenges in achieving accurate demand forecasts?

Achieving accurate demand forecasts is often hindered by market volatility, poor data quality, and inefficient processes that fail to integrate across departments and systems effectively.

How can organizations measure sales forecast accuracy effectively?

Measuring sales forecast accuracy involves:

  • Using Standard Metrics: MAPE (Mean Absolute Percentage Error) and WMAPE (Weighted MAPE) are commonly applied formulas.
  • Implementing Forecast Accuracy Dashboards: Real-time analytics provide visibility into prediction accuracy.
  • Comparing Forecast vs. Actual Sales: Analyzing deviations helps refine forecasting models.

Why is it important to monitor revenue operations KPIs alongside forecasting metrics?

Revenue operations KPIs are critical for aligning forecasts with financial objectives, identifying performance gaps, and optimizing resource allocation. BoostUp.ai supports these efforts by integrating data across departments, providing detailed pipeline insights, and offering dynamic tools for tracking variances and improving ROI.

How can demand planning KPIs improve forecast accuracy?

Demand planning KPIs enhance forecast accuracy by aligning inventory management with revenue objectives and identifying trends in error metrics, enabling organizations to refine their strategies effectively.

What are key KPIs for sales forecast accuracy?

Key KPIs include:

  • MAPE and WMAPE: For measuring percentage error in forecasts.
  • Forecast Bias: Evaluates whether predictions systematically over or under-estimate.
  • Revenue Growth vs. Forecast: Monitors the relationship between forecasted and actual revenue growth.

What is a good forecast accuracy percentage for demand planning?

A good forecast accuracy percentage depends on the industry but often ranges:

  • Above 85% for stable markets with predictable demand.
  • 70%-80% in volatile or high-variability industries.
  • Custom Benchmarks: Based on specific business needs and historical performance.

How can sales forecasting tools help to achieve better forecasting accuracy and assist demand planning?

Tools like BoostUp.ai significantly enhance sales forecasting accuracy and demand planning through several key features:

  1. Data Integration: BoostUp.ai seamlessly consolidates data from systems such as Salesforce, HubSpot, and Marketo, ensuring high-quality, consistent inputs that reduce forecasting errors.
  2. AI-Powered Predictions: Advanced analytics analyze historical and real-time data to deliver accurate, actionable forecasts.
  3. Real-Time Adjustments: BoostUp’s interactive dashboards provide instant insights, enabling teams to adjust forecasts dynamically.
  4. Team Collaboration: By centralizing data and insights, BoostUp enhances collaboration across sales, marketing, and finance, aligning demand planning strategies.

These capabilities drive precise forecasting, enabling businesses to anticipate fluctuations and respond proactively.