Interesting Facts I Bet You Never Knew About Regression Analysis Sales Forecasting

Before you jump into this article, there is one thing you should know. Regression analysis sales forecasting is a granular process that is extremely dense mathematically, which is why most companies don’t typically use this sales forecasting method unless they need a comprehensive analysis of sales. 

It requires in-depth data analytics but can offer daily accurate forecasts. This accurate forecasting tool can be used for predictive analysis to conduct proper forecasts.

Regression Analysis Sales Forecasting Explained

Regression analysis is a form of statistical modeling. It is a statistical process for predicting the quantified relationships between a dependent and at least one independent variable. The two most common forms of regression analysis are linear regression and nonlinear regression.

Typically sales forecasting methods use length of sales cycles, sales pipeline, opportunity stages, historical data, scenario planning and or intuition to predict sales. All of these methods rely on company data and with a proper Customer Relationship Management software, can be easily implemented.

Regression analysis is a sales forecasting method that uses complicated mathematical equations to estimate future revenue. It is not a widely used method of sales forecasting since it is very complicated.

However, with a great CRM software to help you with your calculations and some time at your disposal, using regression analysis to forecast sales might just be the revenue forecasting method for you. Small businesses can use Excel for this forecasting method. however, it can get complicated as more data becomes involved. oo

Regression analysis is a method of forecasting that uses regression analysis to predict your future sales. This forecasting method looks at the variables in your business and helps you find what areas are affecting your sales.

This forecasting method looks at both dependent and independent variables. Being able to find out what is affecting your sales can allow you to greatly improve your business.

Who Should Use Regression Analysis Sales Forecasting?

Regression analysis is a good method of sales forecasting, but it certainly isn’t for everyone. Since regression analysis needs baseline data points, it should not be used by brand new companies with no sales.

This sales forecasting method should only be used by companies that have been around for at least a couple of months and have some closed sales because it uses monthly sales numbers and sales data sets to determine a regression equation. 

Even in the right company, regression analysis shouldn’t be used by just any untrained employee as a method for forecasting sales.

Any user of regression analysis sales forecasting needs to collect and input a certain amount of accurate and time-relevant data for a given time period.

 The user then needs to be able to stay on top of the heavy data need since the data must be updated in order to maintain accuracy.

A company that uses regression analysis sales forecasting also needs to be able to understand the results of a regression analysis since this forecasting method can get fairly complex. This can take some time but is well worth it as using regression analysis is one of the most accurate ways of sales forecasting.

When applied properly, simple linear regression models can help companies predict sales two years into the future. Typically, companies will use multiple linear regressions to determine forecasts.

Most sales managers have a rudimentary understanding of statistical analysis which is why they don’t typically use this method to forecast sales. There are statistical softwares that can be bought to assist with this forecasting method but it is an unnecessary investment if the company already has a proper CRM software that it uses for sales forecasting.

Why Use Regression Analysis Sales Forecasting?

Regression analysis is a very accurate, thorough, and data-based sales forecasting method that uses complex mathematical equations to predict future sales. This forecasting method relies heavily on available data which is why it’s important to have clean data when using this forecasting method. It looks at how one or more independent variables impact the dependent variables. 

Since regression analysis can look at and analyze multiple variables at a time, it is not only very accurate but also very time efficient.

Regression analysis is precise because allows you to find out what variables are impacting your sales at any point in time. This can allow you to find issues and fix them. If reacted to correctly, the ability to find problems within your sales can be very beneficial to your company and its ability to forecast sales.

Regression analysis is objective because it does not use subjective opinions. It uses objective numbers related to your business that you collect to conduct predictive analysis about the business. Since regression analysis always uses hard data, the results of the analysis will be objective.

This means that as long as the data you collect is accurate and time-relevant, you will receive a very accurate forecast of your sales.

Implementing Regression Analysis Sales Forecasting

Now that we have gone over sales forecasting and regression analysis, let’s get into the steps that will allow you to implement regression analysis into your company. Keep in my that this is a quantitative method for forecasting which means that it relies heavily on sales numbers and data. This means that the more accurate the sales numbers are, the more meaningful information can be extrapolated from this forecasting method.

Collecting Data

Collecting data is the first step on the road for you to implement regression analysis into your company. The type of data you collect depends on what you are trying to analyze. However. it will normally include the amount and or number of deals closed and some other possibly influencing factors like the number of times you reached out to potential customers or how long you interacted with customers. You will want to store this data on a spreadsheet or in a CRM software.

Generating a Regression Analysis

The next step to implementing regression analysis is to actually generate one. You will want to plug in the data you collected into your regression analysis equation.

Again, this equation will depend on what variable you are looking to test because depending on the variable it will determine whether or not it is statistically significant. 

In order to have an effective regression analysis, you will want to use good software capable of handling this statistical model. Excel can be a good tool for regression analysis. However, since there is a lot of manual input involved the data, can be compromised.

The purpose of this forecasting method is to use the forecasting data sets to see if there is a normal distribution and linear relationship between the dependent and independent variables.

This software may be able to provide you with an equation to plug your data into in order to test for a certain variable.

Moving Forward

Great job! You have just completed all of the steps necessary to complete your first regression analysis. However, it is now time to look ahead to the steps you will need to take in order to improve your new regression forecast.

Once you have completed your regression forecast, you should analyze the results. Being able to identify explanatory variables and generating a regression line is essential for this forecasting method.  

As previously stated, this is a bit complicated but with time, most people can learn how to analyze a regression analysis.

After you have analyzed the results of the analysis, you can decide if there is anything you need to change in your sales process. Next, you will want to begin adding and changing the information in the regression analysis.

Doing this will keep your analysis accurate since they are only good for a limited amount of time. In order to keep your analysis time-relevant, you will want to consider readjusting the variables in your equation whenever needed.

Common Problems and Solutions with Regression Analysis

Although regression analysis is a very accurate way to forecast sales, just like all other sales forecasting methods, it certainly comes with its share of positives and negatives. One of the main downsides when it comes to regression analysis is its complexity.

Regression analysis is one of the most complicated methods of sales forecasting out there. It takes some time to learn to use regression analysis and then understand the results that come from it, which is not always possible for everyone.

The other downside to regression analysis is the data needed. Regression analysis requires a lot of accurate data to plug in and produce forecasts. It can take a considerable amount of time to both collect and store the data.

Even though regression analysis has a couple of negatives, in its complexity and data need, they can certainly be overcome. Both the complexity of regression forecasting and the data need that comes with it can be taken care of by using a simple, user-friendly CRM software.

The use of a CRM software can help track and store the complicated data involved with regression forecasting. This can both simplify the complicated process of regression analysis and help with the stringent data load.

Regression analysis forecasting is a forecasting method that should not be used on its own because the data might be too complex for the sales team to interpret. 

It is a good way to analyze trends and make predictions. However, without the appropriate statistical knowledge, the results of the forecasting method can be difficult to interpret.

Forecasting accuracy for this sales forecasting method is dependent on the accuracy of the dependent and independent variables. 


Regression analysis sales forecasting is an effective analysis tool that can be used for sales forecasting. However, this forecasting method may be too complex for day to day operations. Sales forecasting techniques are meant to use sales data to develop and execute business strategies and predict sales. Traditionally, companies use rollup forecasting to predict sales if they want to generate complex and intricate sales forecasts then they use other methods like AI forecasting, or multivariable sales forecasting to forecast revenue.

This method is used by data analysts that are part of a company’s sales team and have an in-depth knowledge of the company’s business activities. Regression analysis sales forecasting can be a great business strategy and an integral part of a business if companies have the proper tools to implement it. 

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