Sales forecasting quantitative methods use past sales periods to predict future revenue and sales growth rates and help predict the team’s future performance. Compared to qualitative sales forecasts, the quantitative method provides unbiased data and helps make better decisions about company direction. Almost anyone can quickly implement a quantitative sales forecast in CRM by building dashboards and reports. Oh, and It’s also the most cost-effective forecasting model.
This article will highlight exclusively quantitative forecasting techniques to get a more in-depth overview of the method. I will underline qualitative sales forecasting techniques in another post, but let me first give you a brief overview.
Qualitative method vs. quantitative forecasting technique
The sales forecasting methods are divided into qualitative and quantitative. Qualitative sales forecast techniques use expert opinions to make projections. In contrast, the quantitative sales forecasting method uses actual data from past sales periods to predict future revenue and sales growth rates and help predict the team’s future performance. The main difference between these two types is that in a qualitative approach, you might be wrong even though you are an expert. At the same time, there is no room for errors in a quantitative sales forecast technique because the data used to make projections is factual.
The company can improve the accuracy of sales forecasts by including both qualitative and quantitative techniques in its sales forecasting methodology. Of course, I would suggest starting with a reliable method that will provide insights into sales performance. A sales forecast technique should be easy to implement, cost-effective, and give the sales team accurate sales data. As a result, managers can make better decisions based on facts rather than emotions or feelings.
What is the quantitative sales forecasting method?
Before we jump into the topic, I want to give you an overview of what quantitative sales forecasting means. Quantitative sales forecasting is the sales forecasting method that uses numerical data instead of information based on personal opinions. The sales forecast can be “quantified” by using sales metrics, such as the number of sales calls made or sales pipeline activity. But we will get back to this.
What are quantitative sales forecasts used for?
Quantitative sales forecasts are often used in business intelligence to provide an objective overview of sales performance. Compared to qualitative sales forecasting, the quantitative method provides unbiased data and helps make better decisions about company direction.
The sales manager can quickly implement quantitative sales forecasting in CRM by building dashboards and reports. These tools track early-stage sales like:
- number of phone calls made;
- scheduled meetings;
- created accounts and contacts (leads).
Where are quantitative methods used?
Quantitative forecasts are often used for short-term prediction because they take into account current trends. Unfortunately, they don’t project long-term changes or future events that could influence companies’ sales, for example, new competitors entering the market or increase/decrease of production costs.
Where are quantitative sales forecasts not suitable?
Companies that implement quantitative methods to forecast sales often overuse the technique. Quantitative forecasting models are not suitable for long-term sales planning. As I mentioned before, the method provides an accurate picture of current sales performance. Still, it doesn’t predict long-term changes or events.
Historical data
Quantitative forecasting uses historical sales data, and it analyses past sales periods and applies them to the future. As a result, it can’t react to the current market situation. As sales managers, we need to understand that sales forecasting is never an exact science. Many things can affect sales performance. Sales forecasts will always be a projection, not a strict prediction of future sales numbers.
Don’t track creative processes with quantitative forecasting
Another area where the company should not use the quantitative method is to track c-level managers and business developers who create processes from scratch. In other words, the technique is not suitable for sales managers who are building their sales processes from the ground up. It can be helpful to track sales representatives’ activity after they have built a solid foundation for the sales process. Still, it’s not suitable for newly created business processes because the method requires past performance data.
Before you begin with quantitative methods of forecasting for your sales team
If you are over-excited about converting your whole sales process into numbers, let me tell you that you have to do your homework first. Before you build your first report, you need to collect accurate data about the previous period’s sales revenue. You may want to use sales management software or sales analytics tools to track the data. You can’t build accurate sales forecasts without using correct historical sales performance numbers, so make sure you follow your sales activities accurately.
What sales metrics should you track to build correct sales, forecasting models?
Now that we have covered when and where the quantitative method is not suitable let’s look at which sales activities can be used to create accurate forecasts.
There are specific vital numbers every manager needs to measure to forecast future sales:
- A number of new clients acquired in the period under review (you may want to split them by account or territory).
- Average contract value per client (to see if your revenue went up or down compared to the previous period).
- Sales quota attainment (how many sales quotas were met in the given time frame).
Quantitative forecasting examples
Now it’s time to dig deeper into details. In this section, I will cover four quantitative forecasting methods for accurate forecasting of sales. These are casual methods used almost in every company which wants to predict sales accurately.
Sales forecasting is never an exact science, but these four methods can help you build a solid foundation for your sales forecasts.
Linear extension
A sales manager can use a linear extension to predict sales numbers based on the previous period’s data but also add “x” percent of increase or decrease to account for changes in market conditions and new deals coming in.
Linear extension example
For example, if your sales increased by 15% compared with the previous month, you would make projections as follows:
Previous sales month: $100,000
+15%: 15% of 100 is 15. So forecasted sales in the following period would be 115,000.
If there was a decrease by 20%, you should make projections as follows:
Previous sales month: $100,000
-20%: 80% of 100 is 80. So forecasted sales in the following period would be 80,000.
Where to use linear extension
The sales forecasting method is not suitable for sales managers building their sales processes from the ground up because it requires past performance data. However, once you have a sound system in place that tracks your sales activities accurately, this approach can be helpful to predict future sales numbers by using historical growth rates of revenue and potential changes based on market conditions or other factors.
Short-term benefit
Another reason why the linear extension is the preferred sales forecasting method for short-term (less than three months) projections is that it’s not very sensitive to seasonality and trends, which can distort your forecasts in some instances. This brings us to another important point: if you are building sales models based on historical data, make sure you use a correct time frame so as not to cause any distortions when making future predictions such as yearly or quarterly revenue numbers. Keep seasonal trends in mind because they impact performance metrics like new clients acquired, number of deals closed, etc… Suppose you don’t adjust those figures accordingly. In that case, there will be no objective basis for projecting sales revenues further down the road.
Linear extension bottom line
A linear extension is one of the simplest methods used when making forecasts for short-term periods (up to three months). It’s also more accessible than the regression model, which will help make predictions longer-term while keeping projections more accurate. The linear approach allows companies to quickly adjust projections without using complex statistical models with many variables coming into play over extended sales periods.
Linear regression model
The linear regression model is the preferred sales forecasting method for longer-term (up to 12 months) projections because it’s not very sensitive to seasonality and trends, which can distort your forecasts in some instances. This brings us to another important point: if you are building sales models based on historical data, make sure you use a correct time frame so as not to cause any distortions when making future predictions such as yearly or quarterly revenue numbers. Keep seasonal trends in mind because they impact performance metrics like new clients acquired, number of deals closed, etc… Suppose you don’t adjust those figures accordingly. In that case, there will be no factual basis for projecting sales revenues further down the road.
Where to use linear regression model
First of all – use it when you need to produce forecasts for a more extended period (12 months), and/or you have to consider the seasonal index. Due to the fact, the linear regression model is an evolution of linear extension model, it is more complex and requires a lot of sales data.
Linear regression model bottom line
The linear regression sales forecasting method helps companies predict sales numbers over more extended periods (up to 12 months) by using past performance metrics like the number of new customers acquired; total deals closed, average deal value, etc… However, suppose you don’t have sales data spanning more extended sales periods. In that case, you can use a linear extension model to make sales forecasts for short-term (up to three months) sales numbers.
Run Rate
Run rate is not a method but rather a type of report. It is elementary to calculate, and the run rate extrapolates the current number to a more extended period. For example, if your sales for the last week accounted for $10,000, your monthly run rate will be shown as $40,000. You are extrapolating sales for the period in which sales data is not available. The run rate can be helpful when making short-term projections (less than three months). It’s also an excellent quick way to get a general idea of sales performance over extended sales periods by using more complex quantitative forecasting methods like regression models or neural networks.
Moving average
Moving Average (MA) method: this sales forecasting model uses the average sales performance of the previous periods to project future sales numbers. For example, we used MA30 and had three data points for our period sales of $100, $110, and $120. The sales forecast would be calculated as follows:
$130 = ($100 + $110 + $120) / three periods minus the average sales performance for these periods (in our case it was 100+110/two=115).
MA method is not always accurate because it can’t predict if sales will go up or down in the coming months. However, the MA model has its place when you need to build a quick sales projection based on historical data without too many calculations involved. It’s very simple! Remember that this model doesn’t work well with new business processes with no historic numbers to base your projections on. Also, keep in mind that MA30 works better for sales forecasting than MA60.
Which quantitative sales method is best?
I love this question, but the answer is not simple. It depends on sales cycle length, sales forecasting horizon, and budget. Also, it depends on how accurate your sales revenue data, sales rep activity collected data quality, and overall sales teams readiness to use sales tracking tools.
If you don’t have sales data for a more extended period, go with a linear extension model and increase sales rep activity and/or sales forecasting accuracy by using more complex quantitative methods like regression models or neural networks afterward.
Are qualitative forecasting methods any better than quantitative forecasting methods?
The short answer is NO. They are not better, neither are they worse. They are different. Qualitative sales forecasting methods like consumer surveys, focus groups, or brainstorming sessions (with the sales team) are suitable for understanding why customers buy your product and what’s important to them. These sales data-gathering techniques help companies understand consumers’ behaviors, but they can’t be used for quantitative sales forecasting.
Both methods are great, but remember that they work best when combined. Quantitative methods use past data, while qualitative sales forecasting methods help you understand driving sales numbers and why.
Can you forecast month’s sales with quantitative forecasting?
In short – not precisely. While quantitative methods are great for the small sales department, they require past data. The future data they provide is somewhat indicative and does not represent 100% accuracy. Even sales forecasting with regression models requires having a good base of historical sales data.
Some companies might not have this information. In those cases, you can use other sales forecasting methods like “what-if analysis,” allowing you to make assumptions about factors that influence sales (like a number of sales reps, marketing budget). But even by using what-if analysis, it is still hard to forecast the exact numbers because past sales performance does not guarantee future results.
Combine methods
So my advice is: don’t rely on quantitative methods only – combine them with qualitative research techniques like focus groups or consumer surveys where your customers share their expectations for the upcoming period. Also align forecasts across different departments like product team, customer success department, etc..
The best combination of sales forecasting methods is a mix of quantitative and qualitative sales forecasting techniques.
How do we implement quantitative forecasting if we are a small company?
Word quantitative forecasting is derived from quantity. This means that first of all, you need to collect previous sales data. Once you collect the data set, you will be able to understand your historical growth rate. Then, you will take that data and apply it to your near future sales forecast.
It would help if you started with the sales department early-stage sales trackings, such as tracking the number of phone calls, scheduled meetings, created accounts and contacts.
Once you have sales data, use it to understand your monthly sales compound rate growth and apply it to future months to create a more accurate sales forecast.
Other quantitative forecasting methods
While there are many different methods available, they can be divided into two groups:
- Time series models (like ARIMA or linear regression) – this is the simplest method that uses historical sales values to predict future sales performance;
- Bayesian statistics (regression with sales factors adjustment) – this method considers the impact of sales team structure, product price, and other significant sales drivers.
But that is a different story. My goal was not to deep dive into data analytics theory but instead, provide you with a brief overview of what’s available out there.
Data is everything
Quantitative forecasting’s important aspect is getting accurate data in your CRM. Remember, this is the very first thing you have to do. Once you are done with data, you can research academic literature on statistics, sales forecasting techniques, sales pipeline management, and sales data analysis.
Summary
So, quantitative sales forecasting is the method of creating a forecast by using numerical data. Businesses can use four main quantitative forecasting methods for accurate sales forecasts. Linear sales forecasting is the simplest and most commonly used method, but it does not necessarily provide accurate sales data. More complex quantitative sales forecasting methods like regression models or neural networks help increase sales forecast accuracy and reduce forecast errors. If you don’t have sales data for a more extended period, go with a linear extension model and increase sales rep activity and/or sales forecasting accuracy by using more complex quantitative methods like regression models or neural networks afterward.
I hope this article was helpful to you. If you liked it, please mind sharing it on your social media. Have a fantastic sales forecasting day/night, whatever it is now when you are reading this!
Jeff