Sales forecasting doesn’t work…at least not the way most teams do it.
Less than 25% of sales organizations have a forecasting accuracy of 75% or more.1 Why do you think it’s that common?
Because most reps make forecasts based on their optimism, opinion-based probabilities, or unreliable pipeline data.
But that’s not a forecast. It’s more like a sales fiction.
And here’s the truth most sales teams won’t admit: their forecasting isn’t off because of the method. It’s broken because they trust the wrong signals.
This blog will help you fix that. We’ll show you how high-performing revenue teams forecast based on facts (not hope) and how to build a forecasting system that you can use repeatedly.
What Is Sales Forecasting?
It helps you understand how much your team is likely to close in a given period such as monthly, quarterly, or annually.
But sales forecasting isn’t just limited to projecting numbers. In the larger scheme of things, being good at forecasting is about making good decisions. You can rely on forecasting to make a headcount plan, get approvals on time, and influence the product roadmap.
In that sense, incorrect forecasting doesn’t just hurt your revenue. It erodes trust, delays results, and puts your team at a disadvantage.
And that’s why we believe that sales forecasting isn’t just about crunching numbers. It’s a strategic function that can create ripples across marketing, sales, product, and engineering.
Why Sales Forecasting Matters
Sales forecasting matters because it helps your business plan smarter. It guides your budgeting decisions, reduces risks, improves cash flow management, and ensures resources are aligned with future growth.
Effective sales forecasting informs you what you can and should do next. Here are a few things good forecasting lets you do:
1. Predict Cash Flow
B2B sales isn’t a straight line. You have to deal with multi-year contracts, renewals, expansions, and churn.
A solid forecast helps you take all of that into consideration so that you can estimate your true runway, plan your investments, and survive down markets.
Without this kind of clarity, your company’s growth relies on guesses. And guessing isn’t a sales strategy.
2. Align Hiring Plans
Hiring is often an investment companies make based on what they think the ARR will look like six months down the line. If you miss your numbers, you're stuck with a burn rate you can’t justify, or forced to lay off people you can’t afford.
But a reliable forecast gives you the control to make sound hiring decisions that you won’t second guess.
3. Earn Trust from Stakeholders
If you have investors, they need to know that you have control over the revenue. If you repeatedly hit your numbers, you show them that you understand your pipeline, your market, and your growth levers. That builds trust.
And trust gives you leverage. That means more say on future decisions, more support from the board, and fewer awkward conversations.
Internally, accurate forecasting means you know what you are doing. And people respect that.
Knowing why forecasting matters is one thing. But to actually forecast with confidence, you need to know which models to use and when.
Sales Forecasting Models: Which One is Best for You?
Most forecasting techniques look good on paper, but only a few survive the real world.
And the best forecasting model is the one that fits your company’s growth stage, sales motion, and team processes.
Let’s talk about the different types of forecasting methods that most SaaS companies, big and small, use.
These forecasting methods fall into two buckets: qualitative and quantitative.
Qualitative Forecasting Techniques
Qualitative forecasting is handy when you don’t have much historical data or if your market conditions are changing too fast for data to keep up.
Qualitative forecasting relies heavily on humans, which also means it’s prone to biases. Here are the two most common qualitative forecasting models:
1. Expert Opinion
When to use it: If you’re an early-stage company without enough sales data. For example, if you’re creating a completely new SaaS category.
What it looks like: For this to work, you need to have experts in your sales team. Think of a VP of Sales, Head of RevOps, or Sales Manager who can build a forecasting model based on your company’s niche or their instincts.
Why it works: Expert sales leaders are really good at identifying patterns and risks. When data is scarce, experience is your best friend.
When it doesn’t work: When there’s too much bias and guesswork. Instinct without scrutiny can derail your forecast, even when it comes from an “expert.”
2. Delphi Method
The Delphi method is a great way to forecast sales numbers by gathering opinions from a panel of experts through multiple rounds of anonymous surveys. It’s an extension of the expert opinion-based forecasting technique, but without the risk of biases.
Think of asking a group of experts the same question over and again until they arrive at a more or less agreeable conclusion. After a few rounds of questioning, the smartest ideas will emerge like a clear pattern.
When to use it: You’re in a fast-changing market, like CRM, or new territories where individual judgment isn’t enough.
What it looks like: The Delphi method is like a virtual roundtable, except no one knows who said what. Once all experts share their anonymous forecasts, they derive an average prediction based on the collective prediction.
Why it works: Since responses are anonymous, there’s less chances of people influencing each other with their job titles or seniority. The final consensus is based on ideas, not strong opinions. That means more thoughtful, less biased forecasts.
When it doesn’t work: When time is of essence. The Delphi method needs patience because it takes time. Also, if your experts lack domain expertise or context, you’ll just end up averaging bad guesses.
Quantitative Forecasting Techniques
It’s always better to rely on a data-backed forecasting number when you have a good chunk of historical sales data. The techniques below use numbers instead of “gut feel” to project numbers.
1. Historical Growth Rate
When to use it: If you have a predictable pipeline and the company is growing consistently over the last few quarters.
What it looks like: It’s pretty simple. If your MRR has grown steadily at an average of 25% month-over-month for the past six months, apply the same rate to forecast the next six months.
Unless, of course, you’re chasing aggressive sales goals (in which case you should raise the stakes).
Why it works: Because the past is often the best indicator of the future. It’s the same way meteorologists forecast weather, sports analysts bet on outcomes, or investors pick stocks.
When it doesn’t work: When past performance doesn’t reflect current reality. For instance, if your company has been acquired, came out of a recession, or pivoted to a new category.
2. Regression Analysis
When to use it: You want to understand how different variables like cutting down marketing spend or hiring more AEs impact revenue.
What it looks like: You enter your historical sales data into a regression model, add specific variables, and see how each one affects the revenue.
Why it works: It helps you validate “what-if” scenarios. For instance, what happens if you hire five more AEs? Or double ad spend targeting competitor keywords?
When it doesn’t work: If your data hygiene is poor, outdated, or missing key context, regression analysis will most likely give you the wrong answers.
3. Time Series Analysis
When to use it: You have seasonal trends (e.g. Q4 deals) or recurring patterns (yearly renewal rush during Q1) in your revenue.
What it looks like: This method looks at past sales performance over time to predict future results. But it also takes into consideration nuanced details like sales slumps, spikes, or cyclical behavior.
Why it works: Time Series Analysis is best for identifying revenue trends that repeat so that you can prepare better for low-performing months.
When it doesn’t work: If you don’t have enough historical sales data or if it’s all over the place, time series can give you mixed signals. No pattern often means no prediction.
4. Pipeline Stage Forecasting
When to use it: You have a mature sales process and a CRM that automatically tracks deal stages.
What it looks like: This technique assigns a probability to each stage in your pipeline. For instance, a deal in the “discovery” stage might be 20% while the one in “negotiation” is 80%.
The forecast calculates your chances of winning based on how far a deal is in the pipeline.
Why it works: It’s based on real pipeline activity. It gets better if you use it along with AI-powered insights.
When it doesn’t work: When reps don’t update the CRM by discipline. If stages don’t reflect reality, your forecast turns into fiction.
How to Build a Reliable Sales Forecast
Companies with accurate forecasting systems hit quota 97% of the time, compared to just 55% without.2
But we get it. You don't want to hear about yet another great forecasting method. You just want a model that doesn’t break mid-quarter.
Below, we’ll share the exact steps you can follow to build a forecast that can adapt to your process and withstand any scrutiny.
Step 1: Clean Your Data
Technology only amplifies what you feed it. If your CRM data is messy, your forecast will be too.
Building forecasts on bad data is like using cracked bricks to build a house. Sooner or later, it will collapse under pressure.
The biggest forecasting mistakes often stem from outdated pipeline data, missing CRM activities, and reps forgetting to log discovery calls.
This is where tools like MeetRecord can do the heavy lifting. It automatically records sales calls, syncs notes, and pushes deal activity into your CRM, like HubSpot. Basically, it helps you create a single source of truth for all your deals so that there are no blind spots.
Step 2: Choose The Right Forecasting Model
This is where it gets tricky because there’s no one-size-fits-all forecasting model.
Most companies pick a random forecasting method and expect it to work just because it worked for someone else.
But your sales motion changes as you scale, and your forecasting model should be able to keep up the pace.
Here’s how to match the model to your growth stage:
- Early-stage? Go with expert opinion. You probably don’t have enough historical data anyway.
- Growth-stage? Choose a time series analysis or regression model. They will show you what’s working, what’s not, and help you avoid costly mistakes.
- Somewhere in between? There’s nothing wrong with mixing multiple models. Use expert judgement where data is lacking or lean on data when it’s available.
So how should you choose a forecasting model? Pick the one that fits your needs.
Step 3: Build The Forecast
Believe it or not, sales alone can’t build a good forecast model. But you can build the perfect one for your business if you collaborate with marketing, finance, and RevOps teams.
Why? Because revenue doesn’t happen in isolation.
Gather insights from across the board. Understand finance’s reporting process. Match your forecast with how the CFO reports to the board. And make sure it reflects how the entire business actually runs.
Step 4: Review It Weekly
Deals have a short shelf life. Monthly reviews in a fast-paced domain like sales can be too slow. Your forecast should reflect how often the deals move, how active each rep is, or how fat your pipeline is.
The more often you review, the faster you can course-correct.
Set up a weekly review cadence to keep your finger on the pulse. Use MeetRecord to capture call and email activities automatically. That way, you don’t have to rely on reps to manually update the CRM.
Why Forecasting Fails (And What You Can Do About It)
Most sales forecasts don’t fail because of bad data.
But like a rocket veering 0.0001 degrees off course, a tiny error in your forecasting can leave you miles away from your target.
Let’s discuss the most common reasons forecasts fall apart and how you can fix them.
1. Sandbagging and Happy Ears
Some reps sandbag deals to make their numbers look good in the next quarter. Others ignore pricing pushbacks and weak buying signals.
These behaviors often lead to forecasts that don’t reflect the real deal health. And by the time you find out, it’s usually too late.
2. Overweighting the Pipeline
More pipeline doesn’t equal more revenue. If most deals in the pipe are stalled or stuck with unqualified prospects, then the pipeline data is just noise.
But many revenue leaders often get blindsided here. They base their forecast on quantity instead of deal velocity, deal quality, or actual opportunities.
3. Ignoring External Variables
Sales is subject to many external influences such as competitors, their pricing, new feature launches, market slump, and seasonality. Most of these variables affect close rates.
But most forecasting models ignore these signals like they don’t exist. This leads sales teams to make foolhardy predictions that are way off the mark.
How To Fix Forecasting Challenges
Fixing your forecast means spotting the weak spots and making necessary improvements. Here are some practical tips to bring more accuracy into your forecasting:
1. Use Revenue Intelligence To Detect Risk
Relying on manual rep updates means increasing the chances of human errors. Instead, you can use MeetRecord to automatically detect risk signals from sales calls and emails.
It can help you highlight moments that indicate concern, such as pricing objections, delayed timelines, and competitor mentions. That way, you know which deal needs more attention before it falls off your forecasting radar.
Besides flagging risks, the right tool gives you full visibility into deal progress. This will help you assess how far a conversation has moved from first contact to close, and spot stalls before they become silent losses.
You can also track competitor mentions across deals to get patterns that help you understand where you’re winning or losing in the market narrative.
Similarly, you’ll also get insights about you how healthy buyer-rep interactions are. This has meetings, follow-ups, and key stakeholder involvement. You can use these insights to coach teams before momentum fades.
And you’ll also know exactly which deals are heating up or going cold based on actual buyer activity.
2. Use Scenario Forecasting
Scenario forecasting lets you plan for multiple possible outcomes instead of just one. The best teams forecast across three lenses: best case, worst case, and commit.
That gives you flexibility instead of being pigeonholed into a single forecasting number. For instance, when a big deal slips, you’ll likely have a Plan B or C to fall back on.
3. Get Insights from CS and Marketing
Folks in marketing know which channels are driving high-intent leads. Customer success can tell you which accounts are ripe for expansion, or showing signs of churn.
Ignoring these insights means not covering all your bases. Make sure you involve them in the forecasting process to avoid any blind spots.
Sales Forecasting Best Practices: What the Best Teams Do Differently
The best revenue teams treat forecasting like maintaining a car. It needs regular cleaning, tuning up, and optimizing for the best results.
Here are some tips to make your forecasting model foolproof:
1. Regularly Update Your Forecasting
Sales moves fast and waiting for a monthly update just doesn’t cut it anymore. Deals progress, or fall through, on a weekly basis. Your forecast should reflect that.
Set a weekly cadence to review your predication. Map it to real-time pipeline data. That’ll give you clear visibility into where every deal stands and the confidence to stand behind your numbers.
2. Bridge the Gap between Sales Conversations and CRM
The longer it takes for reps to log a sales conversation in the CRM, the more details get lost. That throws off your forecast. Manual updates are slow, error-prone, and unreliable.
Instead, use a revenue intelligence platform to bridge the gap. It can help you log key insights automatically, track deal risks, and keep your CRM up-to-date. The result? You’ll build a forecasting engine based on actual conversation data.
3. Train Sales Managers
Forecast accuracy depends on what data you feed, but it’s the managers who enforce that discipline. If your forecast is full of half-baked deals and outdated opportunities, it’s a hygiene problem.
Train your managers to review forecasts like clockwork. Have them go through call recordings, comb through CRM notes, and ask the tough questions.
When managers are in the trenches, reps are more likely to own their numbers and report real forecasting outcomes.
Best Sales Forecasting Tools
Only 28% of sales leaders say they’re confident in the accuracy of their forecast.3
The rest are most likely going by their gut feeling.
Using the right forecasting tool changes that. It’ll show you why a forecasting number makes sense, where the risks are, or what levers to pull.
Let’s break down what to look for and which tools actually deliver.
What Capabilities to Look For?
The best forecasting tools offer control, clarity, and context. Here are a few things to look out for:
1. CRM integration
If a forecasting tool doesn’t plug into your CRM, give it a pass. What good is a sales tool that can’t play nice with your CRM, every sales team’s nerve center?
2. Real-time pipeline visibility
The forecasting tool should tell you the pipeline health that matches the actual deal velocity, not what reps updated two days ago.
3. Customizable models
Ideally, your forecasting platform should allow you the flexibility to switch between various models as your strategy evolves. Otherwise, you’ll be boxed into a single forecasting approach forever.
4. Team collaboration features
Forecasting is a team event. Your tool should let sales managers, RevOps, or even marketing to chime in when needed. This avoids the scatterbrain approach to collaborate across Slack, emails, or spreadsheet.
The Best Sales Forecasting Tools Compared
Here’s a quick snapshot of some go-to forecasting platforms:
Sales Forecasting Case Studies from Leading Brands
Forecasting is a sales muscle that you build over time once you master the right tools and processes. And some companies do a great job of turning it into a strategic advantage.
Here are some examples from well-known B2B brands.
How Carta Uses Scenario Forecasting
Carta, the equity management platform, doesn’t rely on pipeline numbers for its forecasting. Instead, it uses scenario-based planning to map out best-case, worst-case, and committed outcomes.4 This helps them prepare for any unexpected situations like deal slippages or sudden churn.
What sets Carta apart is its cross-functional approach. The marketing, finance, and product teams take turns to feed data to the forecasting model. This ensures that the projections reflect company-wide sentiment, not just the sales team’s perspective.
How MongoDB Uses Data-Driven Forecasting
MongoDB doesn’t just forecast based on rep commits or pipeline stages. Instead, it takes a data-backed approach that combines buyer signals with dynamic customer intelligence.
For instance, it tracks usage trends, developer activity (MongoDB’s main ICP), and funding milestones to gauge account readiness. This helps the sales leaders forecast with better precision.
MongoDB also uses AI extensively to surface deal signals and enrich CRM data. This means their forecasts scale with if the accounts do. In short, MongoDB’s forecasting process adapts to buyer behavior as much as seller input.
Good Forecasts Lead to Good Decisions
And that’s it. Now that you’ve seen the models and the mistakes to avoid, you know what’s best for your business.
A good forecast gives your CRO confidence, your team clarity, and better decisions.
And that’s exactly what MeetRecord helps you do, too. It gives revenue teams a forecast that nudges deals forward with real-time call insights, CRM sync, and risk detection.
Want to improve your forecasting accuracy?
Book a demo with MeetRecord and see what conversation-led forecasting really looks like.
Sources:
1. https://www.kornferry.com/insights/featured-topics/sales-transformation/the-top-4-challenges-in-sales-forecasting
2. https://argano.com/insights/articles/6-shocking-statistics-about-sales-forecasting.html#:~:text=The%20Promise%20Of%20Sales%20Forecasting's,the%20top%20of%20their%20field.
3. https://www.gartner.com/en/newsroom/press-releases/2020-02-12-gartner-says-less-than-50--of-sales-leaders-and-selle
4. https://www.youtube.com/watch?v=HfgQvoPOV3A
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