Sales forecasting

Collective[i]'s sales forecasting solution leverages machine learning algorithms that correlate data from your historical sales and revenue, comparing them to market trends — from similarly-sized companies and industries — to autonomously generate an accurate forecast with minimal to no human intervention.

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It's time for accurate sales forecasting

The most powerful forecasting tool from the world’s largest network of sales professionals.

  • Dramatically improve forecast accuracy
  • AI-driven forecasting
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It's time for accurate sales forecasting

Automated daily sales forecasting

Updated daily based on your data and external network factors that influence your sale.

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Automated daily sales forecasting

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Sales forecasting

What is sales forecasting?

Sales forecasting is the process businesses use to predict their future revenue. Though the word “sales” is right in the name, it’s an activity that touches every aspect of a growing business—not just the sales team. The sales forecasting process can look different from organization to organization; some make use of Customer Relationship Management (CRM) tools, while others still rely on ever-complicated, old-school Excel spreadsheets. There are a wide variety of sales forecasting methods that help sales teams collect data they can use to predict their future sales cycles.

What kind of data are we talking about? Most often it’s historical data: what customers bought in the past, how much they spent, and how frequently they purchased. But it can also be data that’s much more difficult to quantify, like the opinions salespeople have formed through experience. This includes collated estimates from individual sellers of the deals they expect to close and their estimate of closing probability.

Putting all of that information together to produce accurate revenue predictions can be a significant challenge—especially when managing historical data in a CRM feels more like a brick wall than a useful tool.

A survey by Gartner revealed that only 45% of sales leaders and sellers have high confidence in their organization’s forecasting accuracy. Low confidence in sales forecasts limits sellers’ ability to make sound, data-driven decisions. And unwieldy forecasting tools exacerbate that skepticism.

If you’re ready to take the guesswork out of your sales forecasting techniques, you’ve come to the right place. In this guide, we’ll unpack different types of sales forecasting and the best practices needed to own the sales forecasting process—and unlock sales teams’ potential with Collective[i]’s powerful adaptive sales forecasting tools.

Types of sales forecasting

What are the types of sales forecasting? Broadly speaking, sales forecasting tactics are often broken down into quantitative and qualitative buckets, with a third category quickly coming to the forefront.

Qualitative sales forecasting

This opinion based approach favors personal insight and intuition over cold, hard facts. Think of this as forecasting based on the human element of sales. When a salesperson is at their best, they’re profoundly human. They connect with their prospects on an emotional level. They prioritize opportunities based on information, yes, but also on that gut feeling that tells them, “This is the lead I need to chase first.”

Quantitative sales forecasting

At a high level, quantitative sales forecasting is all about the analytical left side. This approach involves collecting data and interpreting it to guide sales forecasts. It looks at numbers like the dollar amount of previous sales, the theoretical value of future sales, timelines to close, and other factors to help businesses predict when they’ll make money, how much they’ll make, and from who. This can be handled from a “top down” perspective, looking at broader economic data, or “bottom up,” which focuses on a company’s own data first. Many experts say to do both — which can be time consuming.

AI-enabled forecasting

We believe AI is the way forward for sales forecasting. Combining both quantitative and qualitative data and marrying it with a neural network, this approach gives sales teams more data and automates everything but what a human needs to do to close a deal. While some AI sales forecasting tools can automate certain tasks, like data entry and maintenance, we use AI to find challenges and opportunities to close deals and help sellers identify their best next step. Here’s how we do it.

Sales forecasting methods

Let’s cut to the chase: Which forecasting method is the best, and why? Think of both qualitative and quantitative forecasting methods as complementary parts of the sales forecasting process. How much water any sales team puts into either bucket can vary, and each type comes with its own challenges and opportunities. Relying on qualitative sources of information like intuition means combatting the biases any one sales professional might bring to the table. And quantitative approaches are only as good as the data itself—garbage in, garbage out. That’s why AI-enabled forecasting is the best overall approach to sales forecasting; it combines the tactics from the other methods with powerful automation tools that empower salespeople to focus only on what works.

Here are some of the most common sales forecasting methods under each approach.

AI-enabled forecasting methods

It’s no longer enough to rely on antiquated sales forecasting methods. For example, putting too much emphasis on your company’s historic sales data can result in constantly shifting goalposts. Historic data can’t factor in major shifts in the market just around the corner, and it’s often difficult to ensure data is accurate and up-to-date. Likewise, one seller’s opinion of how likely a closed deal is can be subject to a lot of unknowable external factors. AI-enabled forecasting offers the best tools for accurately forecasting future revenue, like:

CRM automation

Cutting down on busywork is table stakes for a viable AI-enabled forecasting tool. Using machine learning, this approach automates things like activity and contact capture, reporting, attribution, and workflow building. The less time sellers spend managing data in a CRM, the more time they have to focus on more human elements of sales like conversation and follow-up.

Forecasting improvements

Another way AI-enabled forecasting tools help improve accuracy and provide focus is by improving the data itself. Some tools can automate internal data capture and make sure databases are communicating via API, but the real magic lies in the progressive interpretation of that data. AI can continue learning and analyzing data to provide daily updates, which lead to better recommendations based on both the buyer’s and the seller’s latest activity.

Networked intelligence

If the above two tactics represent what AI can do with one company’s data, networked intelligence is what it looks like when data from other companies come together in a growing neural network. With enough data to work with, machine learning tools can do things like tell you exactly who you should be talking to at a prospect — and who you know that can make an introduction. This kind of neural network-based forecasting tells you what you couldn’t possibly know on your own so you can take the right steps with promising leads and drop the ones likely to go nowhere.

Qualitative forecasting methods

Numbers can rarely tell a complete story. Unless we’re talking about something like astrophysics, stories of human interaction require heart as much as they do mind. Since sales forecasting is really the practice of learning from sales interactions to anticipate the future, it makes sense that there’s room for artistry. So, what is a qualitative method of forecasting? Here are a few examples:

Jury of executives

You may have experts on your product or operations teams who can inform sales about upcoming product launches, service updates, or anything else that could impact what sales will look like. Gleaning their insight can provide a glimpse into what you’ll be selling in the near future.

Sales opinions

Your sales team is a treasure trove of valuable insight into what makes your customers tick. Intentionally soliciting opinions from the rest of the team about new opportunities is a solid, if not limited, qualitative forecasting method.

Expert opinions

Your experts can be both in-house and external sources, as well. One of the most popular qualitative forecasting methods is the Delphi Method, which is a specific approach to collecting expertise. By surveying experts in rounds, allowing the panel to review others’ responses after each round, you can slowly pull opinions out of a vacuum and help groups reach consensus.

Quantitative forecasting methods

What is a quantitative method of forecasting? If qualitative forecasting is an art, quantitative forecasting is a science. Quantitative forecasting methods ground your sales team’s instincts in concrete information.

Historical forecasting

The most foundational qualitative forecasting method involves taking a look at sales data from a specific period of time — a time series of sales — to make predictions about future periods.

Test market

Another approach involves launching a small test market, selling to a specific geographical market or type of customer. Using sales data from that trial, teams can predict what broader rollouts will look like.

Consumer self-reporting

Want to know how customers will purchase in the future? Go straight to the source. Ask existing customers or similar representational subjects about their intent to purchase moving forward.

Market analysis

You might choose to account for things like macroeconomic indicators or customer surveys when understanding what led to sales looking how they did in the past. That, in turn, helps you think more broadly about the future.

Lead-driven forecasting

This narrow approach gives each lead a value score based on their likelihood to convert, helping teams prioritize opportunities.

Opportunity stage forecasting

Similar to lead-driven forecasting, this tactic allows you to establish clear stages in the buying journey and estimate timelines for closing deals based on where a lead is in the process.

When both qualitative and quantitative forecasting methods are brought together in harmony, well, that’s when the real magic happens. As new technologies like Collective[i] empower sales teams with more data that’s more reliable, businesses can put more faith in their sales forecasts — and the amazing work of their sales team to close deals.

Sales forecasting process

Now that you understand the different tactics you might use, it’s time to dive into the actual sales forecasting process. Like much of the world, the basic rules of sales forecasting have evolved in the face of the COVID-19 pandemic. There are now two approaches you can take: One that embraces the forecasting process steps of the pre-COVID world, and one that’s better suited to changing business norms in a post-COVID market.

Pre-COVID sales forecasting process

In some ways, COVID-19 has served as an accelerator for trends that have been happening in sales for much longer. Over the last five years, forecasting windows have gotten shorter and shorter in order to get more accurate. It shouldn’t come as a surprise that as sales teams have moved from quarterly to monthly to weekly and daily forecasting, the approach has changed. AI and neural networks have advanced in the past five years, driving sellers to become more customer-focused. Now, with COVID making in-person forecasting impossible, inefficiencies in the old way are becoming increasingly apparent.

No matter the time frame, if you’re still doing manual forecasting, the steps are largely the same. So, in the pre-COVID model, what are the steps in sales forecasting?

  1. Establish your goals: First, be clear about the purpose of any given sales forecast you’re working to build. Is it to give your company a clear look at the next six months so other departments like HR can make more informed hiring decisions? Is it to envision what the next five years could look like in the light of new macroeconomic events like the COVID-19 pandemic? Focus will help guide you as you determine what kind of data you want to use and how you’ll interpret it.

  2. Define your market: No one knows your customers like you do. Whether your team sells to different industries, different sizes of companies, or one clearly defined market, the second step is to set boundaries for your forecasting data. You might consider creating forecasts for different divisions of the company, or various market sectors. The important thing is to be clear about who you want to predict revenue from moving forward.

  3. Collect your data: Next, you get to work on the various quantitative and qualitative forecasting methods we outlined above. If you’re already using a CRM, you have a good start. Can you segment out the customers in the market you’re forecasting? And what about quantitative tactics like customer surveys, or Delphi Method panels with your internal team?

  4. Test your assumptions: Maybe the most complicated step in this process is to start exploring different models for forecasting available to you from your CRM or other sales tools. Excel and most CRMs can let you test different variables to see the impact of a change — try inserting real-world information into your model like a new six-figure client or the loss of an existing customer. As you test assumptions your team would make about the impact of those events, you can see your forecasting at work and choose a model that works for you.

  5. Validate and repeat: The final step is to make sure you don’t get too comfortable on your laurels. As time goes on, compare new historical data to the predictions your model made. Are they accurate? If so, how close did they get? Getting within 10% accuracy is a sure indicator that you’re on the right track. If you see a large discrepancy, it might be time to re-check your data, or to explore a new model. Try, try again and don’t settle for inaccurate forecasts.

When doing quarterly forecasts, this entire process will likely start fresh with each new forecast. Shorter time frames like weekly forecasts might see steps 1-2 remaining the same from week to week, with most of the focus being on updating data, testing assumptions, and validating results. The same is true for daily forecasts; While there may be less data to update from day to day, there’s still a ton of manual labor in going through steps 3-4 manually each time.

  • Post-COVID sales forecasting process
  • Before remote work became a central part of most sales teams’ lives, it was a bit easier to quickly stop by a sales manager’s desk to give an update. It wasn’t particularly efficient, but it was one way to try to operate from a daily perspective with forecasts. But Zoom meetings and daily check-ins can quickly turn into hours-long meetings in a post-COVID world. More than that, though, consumers’ expectations have shifted rapidly since early 2020. Today’s buyers expect instant access, personalized content, less friction, and the ability to make quick decisions after doing their own research. Sellers need to be just as nimble if they’re going to keep up.

    With that in mind, and considering the potential of AI-enabled forecasting tools, daily forecasts have quickly become the standard. Fortunately, that list of sales forecasting process steps has also shrunk radically:

    Let a machine do it: By relying on AI-enabled sales forecasting tools, sellers can trust neural networks to handle every step of the old-school process. When tools like Collective[i] take in updated sales data from an entire network of sellers, it can adapt instantly to provide daily priorities and goal-oriented updates. With this method, it’s less incumbent on sales teams to set huge goals for a year, quarter, or even month and spin their wheels trying to reach targets set by outdated data. AI-enabled sales forecasting turns the whole process from best-case scenario hopes into a data-driven yardstick that’s always evolving to keep up with what’s actually happening right now in the marketplace.

    And that’s it. What used to be a time-consuming process that constrained sales teams has become an empowering tool. AI-powered tools like Collective[i] get smarter the longer they work, which means they can eliminate redundancies and errors in your CRM and pull the latest information from an even broader field of data. While other sales forecasting solutions focus entirely on things like eliminating manual CRM entry, Collective[i] gives your sales team a true secret weapon to help them punch above their weight. That means more accurate forecasting, more attainable goals, and more revenue.

    Sales forecasting accuracy

    The modern approach to sales forecasting starts with accepting that no human forecast can be accurate. Everyone is responding to what they know about what has already happened to manage what’s likely to happen — but we know the past simply cannot predict the future. The modern, AI-driven approach to sales forecasting presumes that change is constant and asks how we can manage that change.

    Put another way, the old approach to forecasting is all about control and accountability. Businesses needed some way to predict how their business will grow into the future, so the focus with old-school forecasting was on calling a number then hitting it. Modern sales teams use forecasts not as a target, but the world’s best yardstick. They use them to optimize future sales activities and recognize that a good deal can go bad quickly.

    The ultimate effect is that companies that beat their number by staying nimble get much higher multiples. Companies that simply manage to a number with the old method tend not to get as many multiples, and their sales teams end up getting punished for missing more often. It’s better to aim for optimal outcomes because you can make more money.

    What are the major benefits of sales forecasting?

    The benefits of sales forecasting accuracy are happy (if not random) outcomes for old-school sales teams, but for modern sales teams, these benefits are simply a byproduct of an efficient system. Here are just a few:

    More accurate budgeting

    Perhaps the most obvious benefit of good forecasting is the ability to build budgets that will actually keep their shape. The more accurate your estimates are, the less guesswork there will be for the entire business to build and stick to budgets.

    Organizational alignment

    When you can predict with a certain degree of accuracy not just how much you’re going to sell, but of what and to whom and when, it becomes a lot easier for HR and other stakeholders to make decisions about things like staffing and stocking. If, for example, you predict that you’ll be selling a lot of product from one division, your business can put more effort and expense into hiring for that division. The same logic applies to decisions about purchasing parts and supplies from vendors, or where marketing should focus their creative energy.

    Better cash flow management

    Cash flow and budgeting might be similar, but they are different animals. Budgeting can be a helpful set of guardrails for spending, but cash flow is the bowling ball. If it’s too small, it doesn’t matter if you have guardrails or not. You won’t be knocking down any pins. Likewise, accurate sales forecasting can give leadership a better idea of when deals will close, and by extension, when dollars will be coming into the business. With enough warning, credit can be secured with better terms, and investors could be more easily convinced of the value your business will produce in the near future. All of that adds up to a great deal of security.

    Increased trust in sales

    When sales forecasts are accurate and your organization begins experiencing the benefits above, it will have one more seriously valuable impact: Increased faith that the sales team knows what it’s doing. When you can make good on your estimates, it’s only natural that company leadership will be even more comfortable letting sales do what it does best. What’s not to like about that?

    Of course, there are disadvantages of sales forecasting the old way. It can take a lot of time to get everything just right, and if models are using inaccurate data, it can lead businesses on wild goose chases. If your sales team is facing questions from leadership like, “How accurate should a sales forecast be?” it might be time to reframe the conversation. The entire sales forecasting process should be less about controlling the uncontrollable, and more about making smart moves quickly when circumstances change.

    To recap: What is accurate forecasting? For the modern sales team, it’s a foregone conclusion.

    Sales forecasting best practices

    A common question among sales teams and executive leadership is, “How can I improve my sales forecast?” Sellers ask it because they want to be held accountable to more attainable goals, and leaders ask it because they want as much clarity as possible on things like revenue and cash flow. But modern sales teams are utilizing AI to shift their time away from improving forecasts towards managing the reality and strategizing how to act upon it. The goal is to increase revenue, not waste time trying to hit a number that’s little more than a guess.

    Ready to do just that? Employ these modern sales forecasting best practices to set your sales team on a path towards continuous improvement:

    Get better data

    Using accurate historical sales and CRM data is table stakes for accurate sales forecasting. If you don’t know with 100% certainty that you’re calling the right person within a company to sell your product, everything else is moot. And sometimes better data is just more data; What don’t you know about your prospects that could be impacting your ability to sell, or their willingness to buy? By leveraging network-based forecasting tools like Collective[i], you can trust that your data is up-to-date and learn from other sales professionals’ experiences selling to your prospects.

    Be flexible

    You can put a ton of time into crafting the best sales forecast ever, but if something like a pandemic comes out of nowhere… You might need to adjust. That’s a lesson that we’ve all faced recently, but it’s only a big example of a smaller reality that every sales team faces: Things change. Competition comes and goes. Prospects lose urgency. Internal resources shift. As you learn about new developments that could impact your sales process, your sales forecasting models should benefit from that new information, too. Think of sales forecasts as being less like a map, which always looks the same no matter where you are, and more like a compass, which can help you find north even as your position changes.

    Save your time

    Sales forecasting can be a time-intensive process. Find ways to automate and streamline the more administrative tasks associated with forecasting (rely on AI to keep your CRM data fresh instead of manually updating it, for example) and buy your team more time to work together on the creative aspects of sales.

    Keep it simple

    Finally, avoid the temptation to overcomplicate things. Experts tend to agree that it’s better to rely on tools to keep track of changes to your forecasts over time and handle the math for you. Likewise, it’s better to have a more accurate forecast for the short-term than to try and predict what sales will look like 10 years from now. Remember: Forecasts evolve over time. View each forecast as a work in progress. The simpler you keep it, the easier it is to update.

    Prescriptive forecasting

    Prescriptive forecasting is the newest method of forecasting, which uses a neural network — a set of machine learning algorithms that mimic the human brain to identify underlying relationships in data sets — combined with deep learning to analyze data, taking forecasting up a notch. Deep learning uses a vast amount of internal and external data to provide a broader picture of the market that updates regularly, so sellers aren’t focused on reaching a static sales goal.

    Bring your sales forecasting to the next level with Collective[i] Highly effective salespeople are experts at balancing information with instinct, planning with perception, and contracts with conversations. The less time they spend trudging through data entry in their CRM or following up with the same frosty lead again and again, the more time they have to build the kinds of connections that lead to fruitful business partnerships.

    At Collective[i], we believe that machine learning is the key to unlocking the human heart of sales. Our AI-driven sales forecasting platform absorbs information from across our growing network to uncover insights never before available to sales professionals. We look at more than your own historical data, but real-time sales information to learn about your prospects: Who else is selling to them? What else are they buying? How long do their purchases generally take? We synthesize that data and turn it into clear next steps to help you meet your goals quicker and more efficiently.

    Ready to dream even bigger? Get started with Collective[i] today.

    Work together, win together

    Request an invitation to join IntelligenceTM, the world’s first global network of sales professionals.

    On no, we ran into an issue submitting this form. Please ensure each field was filled out correctly and resubmit.

    If this problem persists, please reach out to us and we will be more than happy to follow-up from there.

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    In the meantime, explore Collective[i] and find answers to frequently asked questions.