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The Most Popular Sales Forecast Methods (With Examples)

The Most Popular Sales Forecast Methods (With Examples)

Kushal Saini Kakkar
Kushal Saini Kakkar
January 5, 2022
5 min read
“The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present.” - Paul Saffo

Think of sales forecasting as your very own “sales-time-machine.” 

Sales time-machine

It allows you to peek into the future, or at least make assumptions on past (or current) events, and develop an estimated range of sales projections for the next six to twelve months.

Now that you know what’s coming, you can act confidently on your sales strategy, focus resources where they are needed, plan ahead for upcoming challenges, coordinate with other departments on how to best allocate resources, and take advantage of opportunities.

Let’s look at a few quantitative examples to understand the business value of sales forecast metrics better:

  • Let’s say you forecast a whopping 40% increase in your future sales revenue. This means you can finally invest in your new product development or fill certain in-house positions. However, if you predict that revenue figure to go down, it’s probably a good time to put hiring on pause.
  • Your sales team hit a quota of 75% conversion rate last quarter. From here, you only want them going up, or at least matching past sales. Now your forecasted sales revenue tells you that your salespeople are trending 30% below their sales quota for the next quarter. In that case, thank sales forecasting that it’s a prediction! You will still have time to course-correct, rather than facing this downfall at the end of the month or quarter.

What’s the best sales forecasting method? 

According to research by the Aberdeen Group, businesses that perform accurate sales forecasting are 7.3% more efficient in hitting their quota and accelerating their year-over-year revenue growth by 10%.

Accelerate your revenue growth with sales forecasting

So whether it’s manually with pen and paper or using software, there are many ways to generate forecasts. But what is the best sales forecasting method to use?

One that will give you a high degree of accuracy yet be simple enough for everyone in your company to understand and apply without a lot of extra effort or added cost? One that your sales team is excited about using because it’s easy, interactive, and fun to explore the possibilities? This article will discuss several of the most popular sales forecasting methods along with examples.

But first, here’s what NOT to do:

  • The first thing you need to do is stop using simple averages (i.e., last year’s sales); forget about taking a wild guess, and don’t pick a number out of thin air (unless you like eating Ramen for the next six months because you missed it by that much).
  • Another point you can add to your “Don’ts” list is to not depend on siloed data in different spreadsheets. Use a CRM to track your sales and marketing efforts and conversions. Leveraging modern, efficient business analytics platforms to gain insights will definitely fuel your forecasting techniques.
  • Do not neglect evolving trends and impacting sales factors once you’ve forecasted the revenue. Of course, you will forecast at the beginning of a specific time period, but be ready to incorporate any factors into your forecast that come into play in real-time.

Now that we know what not to do, let’s understand the right way to look into the future:

Sales forecasting using historical data

Sales forecasting using historical data

They say don't dwell on the past, but when it’s critical business decisions, this advice needs rethinking!

As should be obvious, this method involves leveraging your historical sales data and mapping out patterns to calculate past sales growth rates. This will provide you with the data you need to predict future sales revenue - assuming conditions remain the same. 

For example, imagine your sales team has closed deals worth $50,000 over the last quarter. Now you know that your revenue for the next quarter needs to cross $50,000 to mark revenue growth. 

Bonus tip: If you know your quarterly revenue growth rate (let's say it’s 4%), your predicted revenue should be 50,000 + 4% of 50,000 = $51,250.

While this method doesn’t always provide the most accurate forecast, it does come with significant advantages. The main one is that there is no need to set up complex forecasting models which require a lot of time and research. The problem with this method, however, is that it does not consider seasonality, market changes, or buyer demands. 

Asking your sales team

This method is as simple as asking your sales reps- “Do you think this deal will close?” “How much do you think it would close for?” 

Sometimes, the best way to know how well your sales pipeline will convert in the future is through taking the opinion of your dedicated sales reps. After all, they are in closest contact with both past and future customers so their input into what has sold well recently and what will be selling well in the future is vital.

However, in a world governed by data, intuitive forecasting is not the best option to rely on for making your business decisions. Moreover, there are plenty of reasons your sales team could go wrong with their forecast. Those could be: being hesitant about predicting future sales revenue, the stress to hit their sales quota, over-forecasting revenue just in the attempt to go up the leaderboard, and more. This method would never be free of bias

Sales forecasting needs to be free of bias

However, there are just as many ways to avert the forecasts-gone-wrong/ ”but-I-put-my-faith-in-you” scenarios:

  • As a sales manager, create a healthier dialogue with your sales team for forecasting, appreciate their transparency and honesty, even if they come up with figures you expected to be better.
  • They don’t know what you know. As a sales manager, you are probably a part of more client-facing scenarios than your sales team. If you uncover any red flags at any level of the sales process, impart that information to your concerned sales team and rectify your forecast accordingly.

With these little tips, you can try to facilitate this intuitive process, but it is clear that this method is, indeed, unscientific and not the standard forecasting approach. In a world driven by data, insights, and statistical functions, it’s best to go for this method in addition to one of the other models described here, rather than depending on it entirely.

Opportunity stage forecasting

Here, we set standards for every stage a deal goes through. From the first call to the most-awaited congratulations, hand shakes, hugs? Too much?

You have to analyze the process you follow as you take your leads down the sales pipeline. Let’s say you have your first call with a lead; that is your first stage. You did it well; now you have a qualified lead. For a product company, the following stages will be them signing up for a product demo, then trying out your product, then nailing the sale on the final call, and closing the deal.

Now let’s assign probabilities to each of these stages in your sales process:

Now, if you have three leads in your pipeline and want to forecast how much they would contribute to your bottom line, you would use something like the table below!

With these forecast figures on hand, you know which deal would come home and which one you should make extra efforts for. 

However, there’s a loophole. The opportunity stage forecasting method doesn’t consider the age of the lead. For example, your prospect might be at the ‘product demo’’ stage for six weeks while having the same sales growth rate as a hot lead that reached the demo stage in just two weeks. 

Sales cycle forecasting

Instead of relying on success rates based on the deal stage, or using your sales rep’s gut feelings, this sales cycle forecasting method uses the age of the deal to estimate when it is likely to convert. 

Sales cycle forecasting

The calculation is very intuitive.

You add the total number of days it took you to close each of your recent deals, and then divide that by the number of closed deals. That is your average deal cycle, and you can look at different deals in your sales pipeline to estimate when they might close.

Pipeline forecasting

This method looks at each deal or opportunity in your pipeline and calculates its conversion rate based on pre-set data variables you assign, such as the opportunity size or that sales rep’s win rate. 

Pipeline forecasting is a great forecasting technique if you’re looking for a more sophisticated but reliable method, as long as your team updates the data on a frequent basis. This includes looking at current and past sales, potential leads, customers, website or online performance, and more.

Pipeline forecasting is good if you update data regularly

This, however, might not work for early-stage companies without a lot of data in their system and will also need a sophisticated sales forecasting tool to do the math.

Multivariable analysis forecasting

We all know about the scientific method: observing an effect, coming up with a hypothesis about its causes (i.e., providing a possible explanation), developing an experiment to test your hypothesis, and gathering data to see whether your hypothesis holds up.

Multivariable analysis forecasting works similarly. It observes historical sales data and makes a forecast based on that past sales information. However, multivariable analysis takes into consideration several factors that can impact the accuracy of this prediction — such as the deal size and the probability that certain events will occur — and adjusts accordingly for a more accurate forecast.

In contrast to simpler, more basic models that rely on simple or weighted averages for their forecasts, multivariable analysis takes into consideration the impact of specific events on forecast accuracy. It incorporates both quantitative and qualitative data points to more accurately predict future results, allowing it to take into account:

  • The average length of sales cycle for certain types of deals and opportunities
  • The probability that a deal will close, based on the opportunity type and other factors
  • Factors related to individual team members — such as their win rate and quota attainment — which may affect the overall performance

This is the most complex of all sales forecasting methods and requires a sophisticated analytics tool to pull it off.

Make your own future

Whatever model you pick for your sales forecast, if you are constantly over-forecasting, or even if you just feel that your sales team can do better, Wingman can help you identify the gaps in your sales process and salespeople’s training. With Wingman, you can:

  • Track your sales calls and assess all the data points to deliver in-depth insights about your prospects’ and sales reps’ behavior.
  • Leverage our sales acceleration stack and AI-enabled insights such as the likelihood of conversion, talk to listen ratio, etc. help you better understand your prospects and find data-driven ways to close deals.
  • Inculcate more prospect-oriented factors to pace your sales forecast in a more definite and data-defined way.

So use sales forecasting to peek into the future, so that with Wingman, you can make it your own!

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