Sales Forecasting Methods Explained

A sales forecasting method describes the way a company predicts future sales. Depending on the method chosen, the company will use different types of data to estimate sales for the coming week, month, quarter or year. 

Companies rely on various sales forecasting techniques to set sales goals and predict their cash flow and profitability for coming months or financial quarters. The type of sales forecasting method you use in your business may depend on:

• The quality and quantity of sales data you have at your disposal 
• How well sales data is organized and in what format (such as an Excel sheet or more sophisticated sales forecasting software) 
• How good your sales team is at predicting sales figures
• The length of your average sales cycle

An accurate forecasting model enables your company to set better sales goals, bring in new business, shorten the sales cycle length, and make smarter hiring and budgetary decisions. Each of the different sales forecasting methods have various benefits and drawbacks depending on your business model, the data points you have access to, and even how long your company has been around. 

Let’s look at some of the different sales forecasting methods to help you choose the technique most suited for your company to use when forecasting sales. 

List of Sales Forecasting Methods

Understanding the different methods to forecast sales can help you choose the one that’s best for your company. 

Length of Sales Cycle Forecasting

Companies can use length of sales cycle forecasting to accurately predict when future sales will close. Using this method of forecasting, sales managers divide sales into various buckets depending on how and where prospecting occurred. 

For instance, you can attribute different sales cycles to cold email leads, referral clients, leads obtained in person, leads obtained through Google Adwords, social media advertising or other digital marketing methods, or leads obtained from trade shows. 

You can then use historical data to predict the probability of closing future sales within a specific time period.

Pros: 

• Objective sales forecasting based on past sales data and typical conversion rates
• Creates an accurate forecast based on different types of lead generation

Cons:

• Relies on sales reps tracking leads accurately
• Sales cycles may vary depending on the type of customer, regardless of where the lead came from
• Typos and reporting errors can skew data

Length of Sales Cycle forecasting works best for organizations where the marketing and sales teams are in lock-step and the sales process rarely varies. Since past performance indicates future sales, careful tracking of historical data over a specific period of time leads to a more accurate sales forecast using this method. 

This method may not work if seasonality plays a large role in your sales process or sales reps do not carefully track the sales pipeline.  

Opportunity Stage Forecasting

Similar to Length of Sales Cycle Forecasting, Opportunity Stage forecasting also evaluates the sales pipeline to create an accurate sales forecast for the future. To accurately predict sales success using this method, you must know the various stages of the sales pipeline and be able to predict conversion rates at the different stages using historical sales data. 

The major difference between the two types of sales forecasting is that forecasting based on the length of the sales cycle takes into account a given period of time during which a deal is likely to close. Opportunity stage forecasting looks at the position of each lead in the sales funnel, regardless of the timeframe. 

Each opportunity stage has a specific probability of closing, based on past conversion rates, which can be used as a weighting for calculating the sales forecast. 

If your sales team intimately understands the various opportunity stages, this method may provide more accurate sales data.  

Pros

• Accurate sales forecasting based on past rep performance at each stage of the sales cycle
• Objective forecasting based on historical sales data

Cons

• Relies on accurate reporting of past rep performance
• Affected by seasonality 

This method works best for companies intimately familiar with opportunity stages and a wealth of accurate historical data to draw from to make accurate sales predictions. 

Historical Forecasting

Historical forecasting uses historical sales data to predict total sales during time frames that can vary from next month to next year. One of several quantitative methods of predicting sales growth, historical forecasting does not necessarily look at the quality of leads, market research, or changes in the sales process. 

However, as a simple way to predict sales volume over a specific time period, historical forecasting can work to drive intelligent business decisions for many companies. 

Pros: 

• Quantitative method provides objective sales forecasts
• Factors in seasonality to forecast sales

Cons:

• May not work for new products
• Past rep performance may not indicate future win rates
• Historical forecasting works well for companies with accurate sales data to draw from
• A simple typo in a spreadsheet can skew data

Historical forecasting works well for organizations leaning toward quantitative methods of sales forecasting. If your company has accurate historical sales data over a lengthy time period, historical forecasting can yield accurate results if no other factors in your sales process or lead funnel have changed. 

Lead-Driven Forecasting

Combining some of the elements of sales cycle forecasting, opportunity stage forecasting, and historical sales forecasting, lead-driven forecasting also relies on the input of your sales team. In lead-driven forecasting, you will assign a value to each lead based on its probability of closing. 

Factors affecting predictions include sales in the previous time period, conversion rates based on the source of the lead, and the average sales price based on the source of the lead. 

Pros

• Considers multiple factors in evaluating lead quality
• Relies on past historical data for a quantitative analysis

Cons

• May be overly optimistic based on sales team predictions
• Does not factor in seasonality
• Reliant on accurate historical sales data

Lead-driven forecasting may be effective for organizations with accurate historical sales data looking for ways to evaluate short terms leads and potentially make changes in the sales process or marketing strategies to improve results. The sales and marketing teams must work together to accurately identify the source of each lead and the predicted time to closing. 

Intuitive Forecasting

Intuitive forecasting relies on the analysis of your sales reps to predict their win rate over a specific time period. Rather than relying on historical data, this method leans on your sales reps’ predictions about their probability of closing within a specific time frame. 

Pros

• Provides a snapshot of total sales and future growth without excessive number-crunching
• Can be effective if you have no historical sales data 
• Good for new products
• May factor in seasonality and the sales process since you’re relying on the knowledge of your sales team

Cons

• May lead to overly optimistic predictions
• May not be as accurate as quantitative methods

Intuitive forecasting may be your only effective means of a sales forecast if your company is in the start-up stage or a period of rapid growth with new products. If you have no historical sales data or haven’t tracked your sales pipeline, the intuitive sales forecasting method is still better than nothing at all. 

Multivariable Sales Forecasting 

Multivariable forecasting combines many of the sales forecasting methods above to provide a comprehensive analysis of your predicted sales growth over any time period. 

Multivariable sales forecasting looks at your market share, seasonality of your sales, your sales pipeline, past rep performance, and historical sales data to create an accurate sales forecast.  

Multivariable sales forecasting gives you the insight to change your sales and marketing strategies to achieve your sales goals. Whether you want to predict annual sales, gauge your company’s growth rate, or just look ahead by a few months, multivariable sales forecasting takes into account all figures to give you the data you desire. 

Pros

• Evaluates multiple factors for more accurate forecasting
• Helps you gauge a moving average of sales since it’s considering multiple variables affecting sales
• Good for long-term and short-term forecasting 

Cons

• Multivariable forecasting in spreadsheets can still fall victim to human error
• May require sophisticated artificial intelligence software to reveal useful analytics and actionable insights
• Most complex of the sales forecasting methods

Multivariable forecasting works well for businesses in late growth stages who are ready to take their sales forecasts to the next level. They might require the technology to support this sophisticated process, along with extensive and accurate historical sales data to generate accurate sales forecasts. 

revVana Automates Sales Revenue Forecasting

Whatever method you choose to forecast sales, revVana can help you automate the process for more accurate sales revenue forecasting. RevVana helps you instantly forecast revenue directly from your sales volume and sales growth, because it integrates with the sales data from your Salesforce Customer Relationship Management (CRM) platform. 

There’s no need to worry about transposed digits, typos, or other human errors in your historical sales data when you use the revVana platform. Set up the software with your choice of sales revenue forecasting method – or multiple methods – for more accurate sales revenue forecasting. 

Step away from the spreadsheet and let revVana provide a more accurate revenue forecast that can help you make more intelligent business decisions and scale your company in the way you’ve always envisioned. 

Schedule your free revVana demo here.