A Simple Guide on Multivariable Analysis Forecasting

What is Multivariable Analysis Forecasting

Multivariable analysis forecasting is perhaps the most complicated sales forecasting method because it takes into account various sales processes and integrates them into a single forecasting method. 

Even though multivariable forecasting is complex, it doesn’t necessarily mean that it is the most accurate forecasting method. Typically, companies rely on rollup, sales pipeline, or intuitive data to forecast sales. Some companies even implement AI technology or regression analysis into their forecasting techniques to have more intricate analyses.

A solid CRM software can be a great multivariable analysis forecasting tool for a sales team to use for its forecasting model.

This forecasting technique uses predictive analysis and business-related factors such as the length of the sales cycle, opportunity stage forecasting, historical forecasting, and your rep win rates.

All of this means that this method is much more data-intensive and relies heavily on companies to produce accurate data to input into their forecasting software.

Newly established companies and small businesses actually have a slight initial advantage as they largely have clean data that can be used for multivariable forecasting, however, companies that are launching new products or have product changes can also gather clean data for this forecasting method. 

Older companies will often have to obtain clean data from their sales pipeline by starting from a new fiscal year to get accurate results because their historical data might be tainted which can alter future sales forecasts. 

Who Should Use Multivariable Analysis Forecasting?

Although multivariable analysis forecasting is a very accurate method of sales forecasting, it certainly should not be used by everyone. For starters, a company’s sales pipeline that relies heavily on this forecasting method can have a hard time keeping up with the intricacies of this forecasting method.

This forecasting tool has many fluctuating variables that all need to be entered into a forecasting software, making the workload fairly heavy for a sales forecasting method. If all the moving components of this forecasting method aren’t strategically implemented, multivariable sales forecasting can negatively impact a company’s sales strategy and business model. 

That data also needs to be accurate as incorrect data will not aid a company in any way and will only produce inaccurate forecasts. Multivariable analysis forecasting should only be used by those who can keep up with the stringent workload for time relevant and accurate data. Multivariable analysis forecasting is also expensive. This means companies with limited capital to spend on sales forecasting should steer clear of multivariable analysis forecasting.

Companies that use this forecasting method need to be able to determine the seasonality of their product. They need to set KPIs and determine metrics in which data can be extrapolated to use in this forecasting method. 

Typically, one variable that most companies use in this forecasting method is historical sales data. Individual rep performance from the past can paint an accurate picture for said rep’s future performance. 

Another variable that is often used is lead generation from a company’s BDR and marketing team. Companies can determine lead source and value in addition to customer conversion rates based on data from the BDR and marketing teams. 

Companies using scenarios to plan for unforeseeable economic circumstance might also find this forecasting method challenge since it is data driven.

Since this is an expensive forecasting tool both time-wise and financially, most companies shy away from this forecasting method. 

When Should My Company Start Using Multivariable Analysis Forecasting?

Just like most other forms of sales forecasting, multivariable analysis forecasting has baseline requirements to be used.

In order to be implemented correctly, multivariable analysis forecast users must know their company’s sales activities. They must be able to analyze sales cycles, win rates for each step of their sales cycle, and the win rates of their reps.

A company’s sales manager should also be aware of their deal type and market share to be able to accurately use this forecasting method. 

The best way to determine market share is to conduct test market analysis forecasting where a company has product demos in test markets to see if their product can survive in the current market conditions and potential market changes. 

All of these must then be entered into the forecasting software. The timeline of when usage becomes viable will vary from company to company, but a good baseline date is at least a year.

Also, as stated before, companies should only use this method of forecasting if they are in good financial standing and have a decent budget as this is an expensive form of sales forecasting.

Why Multivariable Analysis Forecasting?

Multivariable analysis forecasting is a very thorough sales forecasting method that weighs your overall business factors in order to come up with a potential value of future revenue.

This forecasting method looks at the length of your sales cycle, the size of each potential deal, the stage of your sales cycle that each potential deal is in, the win rate at each deal size, the win rate at each stage of your sales cycle, and the win rate of each of your reps in order to come up with a very accurate sales forecast.

Multivariable analysis forecasting is very data reliant. It relies on clear cut business data input by you, leaving no room for opinions. This makes it one of the most objective ways to forecast your future revenue.

This also means that the forecasts should be pretty accurate as long as you input your business data accurately into your sales forecasting software.

Multivariable analysis forecasting uses multiple business data points to forecast sales. As previously stated, multivariable analysis forecasting looks at the length of your sales cycle, the win rate of each opportunity type, and your reps’ win rates in order to produce sales forecasts.

This provides another layer of security when it comes to ensuring that your multivariable analysis forecasts are accurate.

Implementing Multivariable Analysis Forecasting

Now that we’ve gone over sales forecasting and multivariable analysis forecasting, let’s get into the steps that will allow you to implement multivariable analysis forecasting into your company.

Collecting Your Data

Collecting your data is the first step on the path to implementing multivariable analysis forecasting. Estimating the growth rate of a company and the potential value of future opportunities are all key components of this forecasting method.

At a minimum, a company will want to gather the length of its sales cycle, the size of each potential deal, the stage of your sales cycle that each potential deal is in, the win rate at each deal size, the win rate at each stage of your sales cycle, and the win rate of each of its sales reps. Sales teams typically keep this data on a good CRM software.

Generating a Multivariable Analysis Forecast

The next step on the road to multivariable analysis forecasting is to actually generate a forecast using your sales pipeline. Companies that use sales forecasting methods typically have all of their data stored into some kind of forecasting tool which is why this is the simplest step yet.

Once a company has all of its data checked for potential issues and timely relevance, they can input or modify the data in its CRM software.

Moving Forward

Congratulations! You have completed all of the steps necessary to complete your first multivariable analysis forecast, but now it is time to look ahead to the future and the steps you will need to take in order to improve your new forecast.

Once you have completed your initial forecast, you will want to begin adding and changing the information in it depending on the economic conditions.

In order to do this, you will need to have your reps keep track of their potential deals, similar leads, and lead value as well as monitor some things yourself like the win rate at each deal size and at each step in your sales cycle. 

Additionally, keeping track of the market and competition is also essential for this forecasting method. 

The ability to use analytical tools like CRM software or Excel spreadsheets to run multiple regression can be helpful for this sales forecasting method. Doing this will keep your forecast accurate since most forecasts are only good for approximately 2 to 6 months.

Example of Multivariable Analysis Forecasting

Pretend you have three separate reps, each one working a separate single account.

Your first rep just gave a demo to a potential customer, your second rep just offered a proposal to a different company, and your third rep is currently negotiating with another potential customer.

Accounting for your first rep’s win rate for the demo stage and the expected medium-sized deal of $20,000, the rep has roughly a 25% chance of closing the deal. This gives you a forecasted amount of $5,000.

Accounting for your second rep’s win rate for the proposal stage and the expected smaller sized deal of $8,000, the rep has a 65% chance of closing the deal. This gives you a forecasted amount of $5,200.

Taking a look at your third rep’s win rate for the negotiating stage and the expected larger sized deal of $50,000, the rep has an 80% chance of closing the deal. This gives you a forecasted amount of $40,000.

Over the course of this quarter, you would get a combined sales forecast of $50,200.

Common Problems and Solutions with Multivariable Analysis Forecasting

Even though multivariable analysis forecasts are arguably the best way to forecast sales, just like other alternative sales forecasting methods, they certainly do not come without their faults.

One of the biggest pros that comes with multivariable analysis forecasting can also be its biggest possible problem, and I am of course referring to its large reliance on data.

The constant need for precise and relevant data associated with multivariable analysis forecasting can be a plus for very organized data-driven individuals, but for normal individuals without a CRM software, the task of updating the data can be the downfall of their pursuits of multivariable analysis forecasting.

The other main issue concerning multivariable analysis forecasting is that all sales reps need to regularly track and clean data.

Your sales reps will need to track their win rates and the stage of each of their possible future deals. With a larger team of reps, this can be difficult to do.

But of course, any problems that come with multivariable analysis forecasting can be overcome.

Both of the main issues involved with multivariable analysis forecasting, being data reliance and the need for reps to track and clean data, can be overcome with having your reps refresh data in a timely manner and using a simple, user-friendly CRM software.

Having your reps refresh data when necessary will keep your forecast relevant and prevent it from becoming useless and outdated.

A sales forecasting CRM can then help you manage your relations, store your data, and predict your sales, making your selling that much simpler.

Diving Deep into CRMs

A customer relationship management software, or CRM for short, can help you manage your relationships with customers and new interactions with potential customers while also saving all of the necessary data involved with revenue forecasting and taking the organizational workload off of your shoulders in the process.

Sales forecasting CRM extensions can also forecast your sales for you, taking another possible stressor off your back.


If a company has the money and resources, multivariable forecasting is a great sales forecasting strategy. It can give accurate forecasting results and help improve a company’s sales process. If done properly, multivariable forecasting can optimize resource management and thus produce more accurate sales forecasts.

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