During the first week of every quarter, sales leaders huddle together to make a sales forecast for the next three months. The problem is that 80% of business will end up missing their forecast by greater than 10%. That means most of us will miss. To improve our forecasts, it is important to reflect on how and why we missed. A price and volume variance analysis provides a clean framework to do this. It is great for providing detail around our forecast accuracy.
Price and volume variance are typically used as a cost accounting technique to manage operations and plan for the future. This approach can also be used for sales forecasts. The factors to consider are: Average Sale Price (ASP), the number of deals decided in the quarter (win or loss), and the win rate. The goal is to break down a sales forecast by each of these parts to better understand what caused us to miss.
- Price Variance
- The difference in forecasted sales and actual sales due to a change in ASP.
Price Variance = (Actual ASP - Forecast ASP) x Actual Wins
- Volume Variance
- The difference in forecasted sales and actual sales due to having more or less won deals (win) than expected. Wins are a function of both the total number of deals (won or lost) and the win rate.
Volume Variance = (Actual Wins - Forecast Wins) x Forecast ASP Deal Variance = (Actual Deals - Forecast Deals) x Forecast Win Rate x Forecast ASP Win Rate Variance = (Actual Win Rate - Forecast Win Rate) x Actual Deals x Forecast ASP
Below is an example of how to do these calculations.
|Deals||Win Rate||Average Price||Total Value|
With the above inputs the calculations are as follows:
|Calculation||Result||% of Fcst|
|Total Variance||$495,000 – $600,000||-$105,000||-17.5%|
|Price Variance||($9,000 – $10,000) x 55 Wins||-$55,000||-9.2%|
|Volume Variance||(55 Wins – 60 Wins) x $10,000||-$50,000||-8.3%|
|Deal Variance||(110 Deals x 60%) – (100 Deals x 60%) x $10,000||+$60,000|
|Win Rate Variance||(50% – 60%) x 110 Deals x $10,000||-$110,000|
So, what is this telling us? 9.2% of our sales forecast missed because of a lower than expected ASP. Another 8.3% of our sales forecast missed due to not winning enough deals. Specifically, we had more deals decided in the pipeline and that should have produced a positive variance. However, the win rate was 10% lower than expected and this negative impact totally reversed the benefit of the additional deals over expectation.
When we miss our forecast due to price variance, it is usually because our sales reps are discounting deals at the end of the quarter to get buyers to close. This is typical behavior from many sales teams and can be avoided by proactively considering the price. Price variance is also a result of our sales and marketing teams qualifying smaller deals. This can also be avoided by specifying the types of deals to qualify.
When we miss due to volume it is either because our team did not qualify enough deals, or we just didn’t win them. If it is because we didn’t qualify enough deals we can solve it by increasing our marketing activity, or outbound prospecting. If it is a win rate issue it might due to the quality of the qualified deals, sales turnover and training, or a market shift.
Price and Volume Variance analysis is a great methodology to evaluate your forecasts. Now that you have a grasp on how you can use it, you are well on your way to improving your forecasting process. At ORM Technologies, we specialize in sales and marketing analytics to include sales forecasting. If you have questions about this blog post or would like help with your sales forecast please let us know at email@example.com.
For more information on sales forecasting, our white paper on The Importance of Sales Forecasting goes in-depth on refining your methodology and improving its accuracy.