Sales Forecasting Examples For Each Method of Forecasting Sales

Published on Thursday, October 8, 2020

Accurate sales forecasting can lead to better supply chain management, steady cash flow, increased sales growth, and higher total sales. When you create accurate sales forecasts, your sales reps can start to look at ways to ensure they meet, or even exceed, those sales projections. 

By setting expectations through sales forecasting, sales teams can set attainable sales goals. They can manage their time better. They won’t be stressed out and harried during busy times and scrambling to close deals meet quotas when business slows.

Accountants, CFOs and sales managers define sales forecasting as the process of predicting future sales for a given time period. Businesses use multiple methods to accurately forecast sales. 

As with any type of business plan, the data you use to generate your forecasts affects its accuracy. Having solid, real-time sales data aggregated from a variety of up-to-date sources leads to more reliable forecasting and better business insights. 

How To Have Accurate Sales Forecasts

Creating accurate sales forecasts relies on the accuracy of the data and the reliability of the sales forecasting method you choose. Follow these tips to create more accurate sales forecasts. 

  • Choose the best forecasting method for your business and stick with it – Consistency leads to accuracy in sales forecasting. After all, you can’t spot trends if you keep changing your forecasting methods.

    Not every method is best for every business. For instance, new businesses lacking historical data may benefit from intuitive forecasting, which uses factors such as seasonality, market research, current market share, and planned strategic marketing efforts to predict future sales.

    Multivariable sales forecasting is a more sophisticated form of analysis that incorporates multiple sales forecasting methods. It may yield more accurate results, but often requires sophisticated software to create comprehensive reports.

    Switching from one method of forecasting to another without a good reason, or bouncing back and forth between methods, offers no basis for comparison to track trends over time. 
  • Refine your methods when necessary – On the other hand, don’t be afraid to refine sales forecasting methods over time. A startup may not have historical data to perform certain types of sales forecasting, but as time goes on, incorporating data on past sales into the spreadsheets can yield more accurate results.

    Similarly, as you gain new insights about your sales pipeline and conversion rates, you may wish to revisit your sales forecasting model to incorporate this valuable data.
  • Be sure you’re considering all factors that could affect your business – Historical sales data only goes so far when it comes to predicting future sales. To generate a more accurate prediction, include factors such as seasonality, new product launches, sales, market trends, and even new sales reps, who may show different results than former members of your sales team. 

    In addition, keep an open mind about changes you can’t foresee, including economic conditions and new competitors. These can blindside you, skewing your sales forecasts. It may not be possible to account for these factors, so keeping estimates conservative may compensate for these contingencies.
  • Keep forecasting simple – While you want to consider all the factors that affect actual sales, be careful not to make your analysis too complicated. If you stick with the same forecasting techniques for each time period and identify the factors that really move the needle on your revenue, you can create more accurate financial forecasts.

    Additionally, you may not need to dive too deep into the sale of specific products. Save that data for demand planning, which you will use to manage inventory. Most often, a sales plan should focus specifically on the length of your sales cycle, conversions, and revenue from sales.
  • Track discrepancies and exceptions – You’ll make the biggest impact by focusing your efforts on aberrations in your reports. In other words, look for unexpectedly low numbers and seek to make adjustments to put your forecast in line with your sales goals. If you see high numbers compared to past performance, be sure you aren’t being overly optimistic.
  • Make sure you’re using accurate sales data to start – Whether you’re relying on data from monthly sales or from last year, eliminate inaccuracies in past data by ensuring you are pulling it from the best sources.

    Can you rely on numbers from your sales team or should you be feeding data straight from your SalesForce CRM platform into your forecasting software? Forecasting tools that can pull real-time data can help create more accurate reports.

    Take care to verify data especially as it relates to factors like probability of closing or the length of an average sales cycle. If you are relying on your own sales team to provide this information, they may be offering nothing more than educated guesses.
  • Use sales forecasting software rather than Excel spreadsheets to eliminate the possibility of human error – Many inaccuracies in sales forecasting stem from typos, transposed digits, or entering the wrong data in the wrong field. Sales forecasting tools automate the process by pulling real-time and historical data directly from your CRM, CMS, accounting software, and point-of-sale systems. 

Examples of Sales Forecasting

Now that we’ve looked at some of the ways to create more accurate sales forecasts, let’s explore a few examples of sales forecasting used by medium to small businesses and enterprise organizations, alike. 

Sales Forecasting Examples

Intuitive Forecasting Example

Let’s look at a start-up online apparel retailer as one example. Without historical sales data, you can create an intuitive forecast relying on: 

  • Seasonality
  • Market trends
  • Social media trends
  • Educated guesses from the sales and marketing team
  • Monthly sales (from whatever months you’ve been in business)

From this data, the sales team can predict future sales for the next month, quarter or year. 

For instance, if the first two months moved an average of $100,000 worth of apparel, they might predict $1,200,000 in sales over the next year. 

Historical Forecasting Example

Let’s look at that same company three years down the line. Now, they can create a more accurate financial forecast based on actual sales and historical data. They would simply look at the last year of sales and predict similar results for the future. 

For instance, if they sold $1,200,000 last year, they can predict quarterly sales to total $300,000.

Historical forecasting fails to take factors like seasonality, which would affect a clothing retailer, into account. But the forecast can be useful to help set sales goals or look for growth opportunities. If you aren’t happy with last year’s sales numbers, how can you change your sales process or marketing strategies to shorten your sales cycle and improve the probably of closing?

Length of Sales Cycle Forecasting Example

Perhaps the company wants to track the success of their sales process after using a company of Facebook advertising, inbound marketing, and influencer marketing. They want to track leads and how quickly those leads move down the funnel, combined with their probability of closing. 

This will yield a more accurate forecast than the above methods. Use historical data to determine the length of the sales cycle for each type of customer. Separate each prospective sale based on the length of its average sales cycle. 

Using this data, you can find the probability of closing for each type of deal, as well as the dollar amount for each sale. 

As a simple example, let’s say leads from Facebook ads take an average of one month to close, with an average sale of $100. The probability of closing, based on historical data, after a Facebook user has visited the site equals 50%. If you’ve generated 2,000 visitors from Facebook ads, you’d get 1,000 sales at $100 each in the next thirty days, equaling $100,000 in sales for the one-month period.

Opportunity Stage Forecasting Example

Let’s take the length of sales cycle forecasting one step further to look at opportunity stage forecasting, which can provide more accurate predictions based on where your prospects sit in the sales cycle. 

Again to use the example of Facebook advertising for lead generation, you’ve determined the probability of closing to be: 

  • 50% if they click the ad
  • 75% if they put something in their cart
  • 90% if you offer a coupon after cart abandonment

With these figures in mind, and each sale worth $100, prospects who have been offered a coupon after purchasing represent $90 in your forecast, those who have shopped represent $75, and Facebook ad clicks would equal $50 in your sales forecast. 

Multivariable Forecasting Example

A multivariable forecasting analysis may yield more accurate results than any of the above methods, because it combines multiple sales forecasting models to account for a variety of factors that affects a company’s sales growth. 

An ecommerce clothing retailer might use a combination of the above factors, but add in seasonality to account for a spike in sales during the holidays, back-to-school season, and the beginning of summer. 

They may also analyze clothing market trends and what’s popular on social media, increasing sales predictions if they sell many of the season’s hottest styles. 

Sales Forecasting Methods 

Understanding the various sales forecasting methods can help you choose the best one for your business. 

Intuitive Forecasting –The most basic of sales forecasting methods, this relies on educated guesses from sales people to predict future sales for a given time period. 

Historical Forecasting – Companies with historical data to review may use historical forecasting to create objective forecasts and aid in creating a realistic sales plan. 

Opportunity Stage Forecasting – Companies with accurate data about their sales pipeline, typically tracked through a CRM like Salesforce, can create accurate sales forecasting by looking at where each lead sits and its value based on its probability of closing.  

Length of Sales Cycle Forecasting – Similar to opportunity stage forecasting, length of sales cycle forecasting relies on accurate data regarding your sales pipeline. Rather than looking at the probability of closing, it evaluates the time a lead should take to close. 

Multivariable forecasting – Multivariable forecasting combines the other techniques to create a sales forecast that explores the business from all angles to predict future sales and sales growth with accuracy.  

Sales Forecasting Templates

If you’re completing your sales forecast calculations in Microsoft Excel, you can find an extensive list of sales forecasting templates online. These templates can help you get the job done quickly using a standardized format. 

Automate Sales Forecasting with revVana

However, many enterprise-level organizations find a better way to create accurate sales forecasts with revVana Plan, a sales booking management, business planning, and forecasting software that integrates with your CRM to eliminate inaccuracies. See how revVana can help you create more accurate sales forecasts today.

How to close the gaps between sales pipeline and revenue forecasts — free webinar recording: