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9 Sales Forecasting Methods You Need to Know

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Sales forecasting helps with resource planning, sales and marketing alignment, supply chain management, growing into new markets, and much more.

However, knowing which forecasting method will yield the best results can take research, time, and good old-fashioned trial and error.

To help you find your ideal forecasting approach faster, we’ve gathered the nine top sales forecasting methods so that you can compare and start predicting sales more accurately.

Top-down vs. bottom-up forecasting

Before we get into the different types of sales forecasts you can generate, you should understand the difference between top-down and bottom-up forecasts. Each one can produce different results and meet different needs. 

Top-down forecasting starts by looking outward and assessing your competitive landscape. This route shows you how your product and unique differences will fit within the Total Addressable Market (TAM) and existing demand based on market share.

New businesses or ones expanding into new offerings can use top-down forecasting to estimate revenue potential.

Bottom-up is more granular, starting with your own internal data to generate revenue forecasts. Instead of looking at the company as a whole (such as total sales), this method starts by looking at sales by individual sales reps or channels.

The data you’d find in your CRM or monthly reports can be used in bottom-up forecasting. 

As the name suggests, top-down can be broader and easier to calculate, while bottom-up can be more detailed and specific.

Top-down is better suited for new ventures who want to understand their role within the market, and bottom-up gives you a more detailed analysis. A combination of the two can help provide a clear view and give you more data you can use to make decisions.

The three categories of sales forecasting models

With so many ways to forecast sales, it can be hard to compare and know which one is right for you. To narrow it down further, here are the three main categories of forecasting to consider. 

Qualitative forecasting methods

When you don’t have clear data available, qualitative forecasting can provide insight using external sources and best judgments. It’s less about the numbers you’d see in historical sales data and more about the sentiments of consumers and the market.

Market research, where you collect data through questionnaires, surveys, and the like, and the Delphi Method, which involves questioning a group of experts, are two examples of qualitative forecasting methods. 

Qualitative forecasting isn’t nearly as technically sound as some other methods. It can help you learn about your customers, industry, and buying behaviors, and how that might impact sales. But beyond that, it’s best used to supplement other methods or broaden your view instead of giving you a detailed forecast to work from.

Time series analysis

When you have access to rich historical data, you might conduct a time series analysis to see trends and changes over time. You will need sales data collected over multiple years (the more, the merrier!) in order to truly calculate any seasonality or sales trends. 

But once you have it, you can use that information to apply mathematical techniques and predict future sales. If you want to see the growth rate of certain products or forecast based on year-over-year growth, time series analysis is a good place to start. 

Causal forecasting models

The last option is causal forecasting models. In this scenario, you’ll have access to historical data plus additional internal and external factors that can paint a much clearer picture when forecasting.

By taking a comprehensive look and drilling down into various factors, you can assess the relationships between different influences and better predict the impact on sales. 

While this is the most sophisticated approach, gathering all of this data and executing the right mathematical process to get your forecast can be difficult.

Improve sales forecasting accuracy

The top 9 sales forecasting methods

Now that we’ve discussed top-down vs. bottom-up forecasting and the three types of forecasting models, let’s look at specific methods you can use to forecast. 

1. Historical forecasting

Historical forecasting looks at your past sales data from previous months, quarters, and years to predict what upcoming sales will be for that same time period.

It can be accurate because it’s based on your own data, but it can also be difficult if you don’t have full access to the information you need. 

For example, if you wanted to create a Q2 sales forecast, you’d look at the second quarter of the past three years and form an estimate based on year-over-year growth. 

Pros and cons

  • Accurate sales forecasts because you’re using your own historical data.
  • It can be fast and easy if you’re very familiar with your CRM, sales process, and reporting.
  • Doesn’t factor in any changes you’ve made recently, such as acquiring a business or launching a new service offering that you didn’t have in previous quarters.
  • External factors such as economic changes or new competitors aren’t always factored in.
  • Growth isn’t always steady, so making assumptions based on past data alone could lead you to under and overestimate sales for an upcoming timeframe.

When to use it

  • If you have access to detailed historical sales data through a CRM or similar software.
  • If you want to compare year-over-year growth and changes.

2. Pipeline forecasting

Pipeline forecasting, or weighted pipeline forecasting, requires you to predict future revenue based on opportunities that are currently in your sales pipeline and how likely they are to close. Here’s an example: 

Let’s say one sales rep has three potential customers they’re talking to, each with the potential to bring in $10,000 in revenue. However, this rep estimates that the first two deals are 75% likely to close, while the last is 50% likely.

  • Prospect 1 = $10,000 x 75% likely to close = $7,500
  • Prospect 2 = $10,000 x 75% likely to close = $7,500
  • Prospect 3 = $10,000 x 50% likely to close = $5,000

= $20,000 projected revenue for that quarter

Pros and cons

  • Not as time-consuming as the other methods.
  • Requires reps to be realistic about opportunities when assessing the likeliness to close.
  • Requires everyone to keep the CRM up-to-date at all times to ensure accuracy.
  • You can seek input from other departments to feed into your calculations based on what they know about customer acquisition.

Dan Gray, the CEO of Vendry and a former Head of Growth, uses a weighted pipeline model with his team.

By leveraging existing data to generate predictability while working with marketing to understand what leads are likely to emerge, you can create a very sophisticated sales forecast.

When to use it

3. Opportunity stage forecasting

Opportunity stage forecasting allows you to predict sales revenue by assigning a value to each opportunity based on its stage. The general idea is: the further along in the funnel your opportunities are, the more likely they are to close. 

Pros and cons

  • Using the same percentages for stages can make calculations fast and easy.
  • On the other hand, factoring in where those opportunities came from or how much was spent on lead generation can increase the accuracy of your forecasts.
  • Similar to pipeline forecasting, you need to ensure your CRM and stages are up-to-date in order to properly analyze your opportunities.

When to use it

  • If you want monthly or more frequent reporting and need a streamlined way to predict revenue.
  • If you have the data about your average sales cycle and current opportunities clearly accessible and up-to-date.

4. Opportunity creation forecasting

Like opportunity stage forecasting, opportunity creation forecasting means using your current opportunities to predict sales. The difference here is that you’ll dig into data around closed deals and see what similarities exist.

Then, you’ll compare that with your active opportunities. 

To do this, start with the highest-value customers and identify what trends, behaviors, and demographics unite them. Then, look at your current pool and apply an average close rate based on these factors. 

If your best customers most often have 500+ employees and first discover you through a webinar, those two factors would increase the likelihood of similar opportunities closing.

Pros and cons

  • This method can give you a fairly accurate sales forecast if you have any overwhelmingly clear trends among your customers.
  • It will require upfront research and reporting to understand what you’re looking for in opportunities.
  • After that, it should be somewhat efficient to analyze opportunities with your benchmarks.
  • Taking advantage of your CRM data also helps you learn more about your best customers and can even influence future sales and marketing efforts.

When to use it

  • When you have rich, current CRM data on your existing and potential customers.
  • If you’re also looking to better understand your ideal customer profile and target audience.

5. Lead-driven forecasting

Lead-driven forecasting is about analyzing your current leads and comparing them to your best-converting leads historically.

It’s similar to opportunity creation forecasting in that you’ll need to be able to compare leads by source, and see conversion rates for all of your leads in order to assess them accurately. 

You’ll need a close relationship with other teams, including marketing, so that you can stay informed of any new lead sources or campaigns. 

Pros and cons

  • The initial reporting on customers can take time and cross-department effort.
  • Any new lead sources, such an affiliate program, will lack the historical data to accurately assess them.
  • It really only works if there are similarities across customers and if you have enough of a customer base to analyze.

When to use it

  • If you’re able to drill down and see where most of your customers come from and the associated conversion rates.
  • If you’re closely aligned with marketing — or if you want to be more closely aligned in your efforts.

6. Length of sales cycle forecasting

Length of sales cycle forecasting can seem pretty straightforward on the surface: you simply look at your average sales cycle length and use that as a benchmark to determine how likely a deal is to close. 

For example, if your sales cycle is one year, and you have opportunities that have been in the works for six months, you can estimate that they are 50% likely to close. You can multiply that percentage by the potential revenue from that opportunity to get your forecast. 

Pros and cons

  • The math is fairly simple.
  • The accuracy can vary widely, especially for new products or businesses.
  • Doesn’t factor in external considerations or new initiatives since it’s only focused on the average length thus far.

When to use it

  • If you have a deep understanding of your sales cycle.
  • If you want a simple, fast way to predict upcoming sales.
  • If your goal is short-term forecasting or staying up-to-date on your opportunities.

7. Multivariable analysis forecasting

Multivariable analysis forecasting is all about gathering as many data inputs as possible and feeding them into a formula to generate an accurate forecast every time.

As you’ve likely noticed, many forecasting approaches focus on one avenue to create predictions, such as your pipeline. Multivariable analysis pulls in your historical sales data, information about your market, seasonality, your active pipeline, and more.

This way, you’re gaining a more comprehensive picture of what might happen. 

Pros and cons

  • The accuracy! 
  • The ability to shape the future of your business because you know you have all of the most crucial factors accounted for.
  • Because of the complexity, it’s not exactly feasible for a new business or smaller company to do multivariable analysis forecasting.
  • It’s time-consuming and a bigger lift than other methods.

When to use it

  • If you’re at a larger company or enterprise that has the complex data and sales forecasting tools available to generate these forecasts.
  • If you have sophisticated sales and marketing operations that need the most accurate forecasts in order to make business-changing decisions.
  • If sales leaders are looking to improve their overall sales strategy based on reporting and forecasts.

8. Test-market analysis forecasting

When you’re ready to launch a new venture or product, test-market analysis allows you to see how well it might perform for a small segment of people. You can then take these findings to either fully launch or make adjustments. 

Along with test-market analysis forecasting, you might survey or field customer feedback in order to improve your product. In this way, you can glean more than simply projected revenue from this endeavor. 

Pros and cons

  • Time- and labor-intensive as it requires introducing, tracking, and gathering feedback from prospective customers in order to generate your forecasts.
  • It can be very informative, with rich qualitative data you can share with product, marketing, operations, and leadership teams.
  • While a test market can provide key insight for future sales, other factors can still crop up as you roll out the product to everyone.

When to use it

  • If you’re part of a new business or product that doesn’t have historical data to rely on.
  • If you want to assess how well your business might do in new markets.
  • If you’re in retail or consumer packaged goods and need to confirm theories before rolling out fulfillment or full production of your concept.

9. Intuitive forecasting

Perhaps the easiest to do (but potentially the least accurate) is intuitive forecasting. This process includes polling your sales reps on: 

  • Size of current opportunities
  • Where they are in the sales cycle
  • How likely they are to close

For sales teams that have frequent check-ins and collaborative pipeline reviews, intuitive forecasting can fit right into your existing workflow. It requires a deep understanding of prospects and an honest assessment of opportunities. 

Pros and cons

  • Easy to do if you have an up-to-date CRM and reps know their pipelines.
  • Because it’s based on each rep’s analysis and best guess, the risk of inaccuracy is high as opportunities can be over or under-valued.

When to use it

  • If you’re looking to have an always-current view of your pipeline and what might be coming in terms of revenue.
  • If you’re more interested in getting your reps to assess and meet goals based on forecasts.

How to improve sales forecasting accuracy

If you already have a forecasting method in mind but aren’t seeing the results you want, it’s time to troubleshoot. You can improve the accuracy of your forecasts as a sales manager in three ways. Let’s take a look. 

1. Establish a weekly, monthly, and quarterly operating cadence

The more predictable and streamlined you can make your sales operations, the easier and more accurate forecasts will become. That’s because you’ve outlined a regular cadence that your reps can follow and established expectations that everyone must meet.

Weekly operations might look like pipeline reviews and team check-ins, whereas quarterly projects might include forecasting for the next two quarters and updating the roadmap. 

Establish what happens when and share it with your team so that they’re showing up to meetings prepared and ready to act. 

The more feedback and input you gather through these processes, the better your forecasting will be.

2. Clean up your CRM data

Your forecasts are only as accurate as the data that feeds them. If your forecasts aren’t accomplishing what you want and you find yourself with inexplicable differences in predicted vs. actuals, it might be time for a CRM data audit.

This will help you identify where you have missing CRM data or when reps fail to update records. 

From there, tools like Cloudingo can help you remove duplicate CRM records.

To keep your data clean moving forward, consider automating sales activity tracking. You can use a tool like Weflow to do this.

Weflow can help sales managers track their team’s tasks, notes, emails, and more, and then sync these to Salesforce automatically. This ensures all relevant sales data ends up in the CRM.

3. Account for internal and external factors that can impact your forecasts

With any method you use, you’ll want to include as many other factors as you can in order to improve accuracy. Even if you’re using pipeline forecasting, you can still work with the product team to identify new features that could impact future sales. 

Some of the most common factors that influence forecasts (that you should consider as you’re working on calculations) include: 

  • Policy changes – New pricing or policies that impact how customers use your products. 
  • Hiring and layoffs – Having fewer team members or onboarding new staff can impact how many sales you can bring in. 
  • Territory shifts – Sales reps need time to adjust to new territories or regions, so be sure to factor that in as you move into new markets. 
  • Economic conditions – Are people spending less in your industry overall right now? That will impact sales. 
  • Seasonality – Understanding your busy and off seasons will help you note where to adjust estimates.
  • Legislative changes – Changes to state, federal, or municipal regulations can impact your sales for better or worse. 
  • Market and industry changes – Do you have new competitors who seem to have 10x your marketing budget? That can influence how well you do in upcoming quarters. 

Frequently asked questions about sales forecasting methods

In this section, we’ve answered some of the most frequently asked questions about sales forecasting methods.

What are forecasting models?

Forecasting models provide a framework for businesses to forecast future sales based on a variety of factors. There are many different types of forecasting methods that rely on different mathematical techniques and inputs.

Each method offers a different look at the future based on the data you have available. 

What are the three main sales forecasting models?

The three main sales forecasting models are qualitative forecasting, time series analysis, and causal models. 

Which model is best for sales forecasting?

Because there are so many different approaches, the best model for sales forecasting is the one that utilizes the data you have available and provides answers to your biggest questions.

For example, historical forecasting can help you see what sales will be based on past months and years. Pipeline forecasting helps you assess current opportunities and estimate sales based on the likelihood of deals closing.

Each one is a little different, so it’s best to decide on your objectives first, then choose your method. 

Which factors affect sales forecasting?

A number of factors can affect your sales forecasts, from hiring and layoffs or pricing changes to external factors such as economic conditions and legislative changes.

If there’s a change to your sales team, the way your customers buy and use your product, the economy, or your competition, your sales might be affected. 

What is the biggest challenge to forecasting?

The biggest challenge when forecasting is creating inaccurate or unhelpful forecasts that you then use to make decisions.

This can happen for a couple of different reasons, including not having the data available, using the wrong forecasting method, or not gathering input from other teams when creating forecasts.

Though forecasts are typically owned by sales or revenue teams, everyone from the C-suite to marketing to product and development can provide insights that will improve the accuracy of your forecasts.

A better way to do sales forecasting

Consider using a tool like Weflow to streamline your sales forecasting process.

Weflow allows you to submit, review, and track changes to your sales forecasts with ease while syncing everything to Salesforce automatically.

It supports collaborative forecasting, waterfalls, and quarterly predictions to enable you to forecast revenue with confidence.

Weflow collaborative forecasting

Weflow also helps you improve sales forecasting accuracy through improved pipeline visibility and automated sales activity tracking.

Interested? Get started for free today.

Improve sales forecasting accuracy
Gia Bellamy

Gia Bellamy is a Senior Copywriter and Content Marketer who draws parallels between brands and their audiences. With over 13 years of experience working with agencies, corporations, and SaaS startups, Gia brings a deep understanding of how content can connect people, deliver clarity, and drive results.

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Gia Bellamy