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Implementing Linear Regression Analysis in Sales Intelligence

When it comes to sales intelligence techniques, linear regression analysis is among the most popular. This prediction method is used across a wide variety of industries - it is primarily the art of comparing variables. But, how can this method help to improve your sales forecasting for the years ahead?

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Linear Regression Analysis 101 

Linear regression analysis, at its base, relies on variable comparison. Specifically, it is the technique of comparing one set of variables with another - one independent, one dependent. Crucially, it's the dependent variables in your sales data that you want to predict.

It is a case of looking closely at the variables that appear to generate or spur specific actions. For example, a company may find its sales spike during summer or fall. Or, they may discover that when competitor prices increase, interest seems to climb. While it may be easy to assume what knock-on effects are, regressive analysis can prove vital.

The dependent variable in the analysis is what you want to track or predict. The independent variable, meanwhile, is that which you have suspicions about. Ultimately, the heart of the operation is the dependent variable - and the independent variable is what you want to investigate.

Regression analysis allows us to see whether or not the independent variable does impact the dependent. In many cases, it does - either way, this is where software and forecasting tools can help to answer the question.

Regression Analysis and Forecasting Sales

Due to its relative simplicity, regression analysis is ideally suited for short sales forecasting. To forecast sales using such a model, one must use historical data. The dependent data is the concrete figure, whereas the independent variable is the relative unknown.

To analyze sales based on precedent in this way, we need to track two data sets. For example, you may choose physical sales per month as a dependent variable. The independent variable in this equation may be free trial sign-ups (for a service operation).

Using dedicated software, we can theoretically track how sales are impacted by free trial adoption. This means that users can discover how many users, for example, progress to 'full service'. It could help to predict, over seasons to come, how effective free trials are in driving lead onboarding.

Forecasting in this manner is often used to simplify complex queries and concerns. Usually, it's not possible to accurately estimate why sales or interest may drop across a year. Therefore, by using a mode of averages and precedence, sales intelligence can help to predict how things may go.

What's more, linear regression analysis can also help ascertain which areas of operation may need support. For example, if analysis suggests free trials are steadily dropping away from take-up, this is an area to target. This analysis style can help predict the future and fine-tune daily operations.

This forecasting style can help us understand complex, context-reliant sales progression. Sometimes, understanding customer behavior needs a little extra support.

Rain and Sales: An Example

To better understand regression analysis' place in sales forecasting, let's consider this fantastic example professed by Amy Gallo at HBR.

Gallo suggests that to assume both sets of variables for regression analysis, we need to gather absolute data. That is, all we have available in either category. Gallo's example revolves around rain - where sales figures are dependent variables, and rainfall is independent. We cannot control the weather. Thus, it is independent in this model.

You may discover through the rain as independent and sales as a dependent is that the latter increases with heavier precipitation. For example, a regression analysis may show us that five inches of rain in a given period increases umbrellas' sales. It may also show us that sales crash when rainfall is lighter.

The essential facet of this analysis is that we have one singular forecasting line. This analysis is flexible, too - as you can switch and modify the variables as you see fit. Arguably, we may not have the precise answers as to why sales spike when the weather's bad. However, the fact remains we now know this is the case, and this gives us a platform to build upon.

Ultimately, regression analysis provides us with concrete data beyond pure guesswork.

The Tools on Offer

Forecasting through linear regression analysis is perfectly achievable through various tools and software. In fact, it's entirely possible to use Microsoft Excel to plot out linear graphs and run the equations you demand. However, it's highly recommended you seek out a professional, dedicated solution for the best results.

For example, HubSpot Sales Hub is fantastic at automating sales figure collation. It's also tuneable to offer insight into employee performance, client interactions, and more. This, ultimately, means less time spent actively pooling and gathering categories for variables.

InsightSquared, too, remains a popular choice for many regressive analyzers. This particular system may not regressively analyze on its own, but it has a curious methodology. The service revolves around artificial intelligence, which can be used to derive data needed for analysis.

Crucially, to undertake regression analysis successfully, there needs to be efficient, accurate data pooling. Beyond this, a simple equation or graph setup will be enough to crunch the numbers. Of course, how companies exactly manage this will vary from case to case.


With sales growing ever more complex in the modern age, it stands to reason why companies clamor for control. Regression analysis - on a linear basis - can help us find a starting point to why customers behave as they do. However, there is always further work to do - and we can't rely on rudimentary guesswork.

Efficient, workable sales intelligence has never been more important. It's time to pull the data together. With real-time startup funding details available through Fundz, you're already halfway there.




Topic: Sales Intelligence