The Complete Guide to Building a Sales Forecast

By: Paul Bookstaber
Writer, Salesblazer
November 9, 2023 | 11 min read

Building a sales forecast is both an art and a science. Accurate sales forecasts keep your leaders happy and your business healthy. In this guide, we’ll explain everything you need to know about sales forecasting — so you can get a clear picture of your company’s projected sales and keep everyone’s expectations on track.

We’ve organised this reference guide by the top questions sales teams have about the sales forecasting process, based on our internal conversations and more than 20 years of experience developing sales solutions & sales analytics software.

If you’re a sales leader who’s already well-versed in the who and what of sales forecast techniques, skip to the sections on designing a sales forecasting plan and tools to improve sales forecasts for more relevant knowledge. Sales forecasting can become especially tough when we face an unexpected turn of events, so head to the section on what happens to sales forecasts in unpredictable times for more on that.

• What is a sales forecast?
• Why is sales forecasting important?
• Who is responsible for sales forecasts?
• Who uses sales forecasts?
• What are the objectives of sales forecasting?
• How do I design a sales forecasting plan?
• What happens to sales forecasting in unpredictable times?
• How accurate are sales forecasts?
• What tools do you use to forecast sales revenue? And how do CRM systems forecast revenue?
• How is forecasting better with CRM vs. other methods?

A sales forecast is an expression of expected sales revenue. A sales forecast estimates how much your company plans to sell within a certain time period (like quarter or year). The best sales forecasts do this with a high degree of accuracy, and they’re only as accurate as the data that fuels them.

A strong data culture is at the heart of an accurate sales forecast. This means all sales data is available to everyone at the company, and all teams do their part in keeping it updated, leaning on sales AI and automation to help. More on that in the section on tools used to forecast sales revenue.

All sales forecasts answer two key questions:

• How much: Each sales opportunity has its own projected amount it’ll bring into the business. Whether that’s $500 or $5 million, sales teams have to come up with one number representing that new business. To create the number, they take everything they know about the prospect into account.
• When: Sales forecasts pinpoint a month, quarter, or year when the sales team expects the revenue to hit.

Coming up with those two sales projections is no easy feat. So sales teams factor in the important ingredients of who, what, where, why, and how to make their forecasts:

• Who: Sales teams are responsible for sales forecasting.
• What: Forecasts should be based on the exact solutions you plan to sell. In turn, that should be based on problems your prospects have voiced, which your company can uniquely solve.
• Where: Where is the buying decision made, and where will the actual products be used? Sales teams see better accuracy when they get closer (at least for a visit) to the centre of the action.
• Why: Why is the prospect or existing customer considering new services from your company in the first place? Is there a compelling event making them consider it now? Without a forcing function and a clear why, the deal may stall inevitably.
• How: How does this prospect tend to make purchasing decisions? If you’re not accounting for how they do it now and how they’ve done it in the past in your forecast, it may be fuzzy math.

Forecasting lets leaders set realistic sales targets, create attainable and motivating quotas for sales reps, and gauge expected revenue, aiding in budgeting and spending decisions for the whole company. If forecasts are inaccurate, businesses may overspend (putting themselves in a risky spot), and set unreachable quotas (which is demoralising for reps).

To understand why sales forecasting is so important to business health, think about two example scenarios: one with a car manufacturer and another with an e-commerce shop.

In the case of a car manufacturer, cars take a long time to build. The manufacturer has a complex supply chain to ensure every car part is available exactly when they need to build cars, so the number of cars available to purchase will meet demand.

When you buy something online, whether that’s from a large marketplace or a small boutique, you get a delivery estimate. If your delivery comes a day or a week after it’s promised, that’ll affect your satisfaction with the company — and decrease your willingness to want to do business with them again.

Sales forecasting is similar in both cases. Sales forecasts help the entire business plan resources to ship products, pay for marketing, hire employees, and beyond. Accurate sales forecasting yields a well-oiled machine that meets customer demand, both today and in the future. And internally on sales teams, sales revenue that delivers in its estimated time period keeps leaders and collaborators happy, just like a shipment that arrives on time.

If forecasts are off, the company faces challenges that affect everything from pricing to product delivery to the end user. Meanwhile, if forecasts are on point and sales quotas are met, the company can make better investments, perhaps hiring 20 new developers instead of 10, or building a much-needed new sales office in a prime new territory.

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There are a variety of ways to forecast sales. Today, most teams handle forecasts with sales analytics tools and a CRM. These tools allow leaders and sales reps to understand team performance, trends, and to inspect deals.

A well-oiled sales CRM contains a trove of important data that can be used to create predictive models. To create a sales forecast, leaders would leverage their CRM data along these lines:

Collect historical data: Analyse past sales performance with interactive dashboards to identify trends and patterns.

Evaluate current pipeline: Assess active deals to estimate their likelihood of closing within the forecast period.

Consider market conditions: Account for external factors like economic shifts or industry changes that could impact sales.

Incorporate their team’s input: Gather insights from sales representatives about their active opportunities and expected closures.

In our more detailed guide to sales forecasting, we also suggest keeping a regular cadence in order to keep everyone aligned. To get more info on sales forecasting tips and best practices, check out the guide here.

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Creating a sales forecast is relatively straightforward. Historical data is analysed to predict future trends. But accessing the data and turning it into valuable insights can be time consuming without the right tools. Still, the sales forecasting process can be understood along the following lines:

Define the forecast period: Determine the time frame for the forecast, such as monthly, quarterly, or annually.

Gather necessary data: Collect relevant information, including historical sales data, current pipeline details, and market analyses.

Select a forecasting method: Choose an approach like historical forecasting (using past sales data to predict future performance), pipeline forecasting (evaluating current deals by their likelihood of closing), or a combination of methods. A combination of methods would measure trends gleaned from historical data with the current state of your pipeline.

Analyse and project: Use the collected data to identify trends and project future sales figures. Then, regularly revisit the forecast to adjust for new information or changes in the market.

With historical forecasting, for example, if your sales grew by 10% every year for the past five years, you might forecast a similar growth rate for the coming year. With pipeline forecasting, your CRM might show $500,000 worth of deals in advanced stages with an 80% probability of closing, so your forecast projects $400,000 in revenue from these deals.

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Each organisation has its own sales forecast owners. These are some of the teams who are usually responsible:

• Product leaders: They put a stake in the ground for what products will be available to sell when.
• Sales leaders: They promise the numbers that their teams will deliver. Depending on the seniority of the leader, how they forecast varies. For example, first-line managers forecast collections of opportunities, where third-line managers consider a wide set of numbers and traditional close rates to come up with an overall forecast.
• Sales reps: They report their own numbers to their managers.

No matter how a company calculates its sales forecasts, the process should be transparent. And at the end of the day, sales leadership has to be responsible to call a number. Whether met, exceeded, or missed, the forecast responsibility falls on them.

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Sales forecasts touch virtually all departments in a business. For example, the finance department uses sales forecasts to decide how to make annual and quarterly investments. Product leaders use them to plan demand for new products. And the HR department uses forecasts to align recruiting needs to where the business is going.

At some level, sales forecasting affects everyone in the company.

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The main objective of sales forecasting is to paint an accurate picture of expected sales. Leaders are looking to these numbers when they’re building out their operational roadmap and budget. If they’re confident in the projected growth, they can get to planning.

They could decide to staff more customer service touchpoints, fund more external marketing events, or invest more in the community. They could get ahead of purchasing new equipment or upgrades that get more expensive the longer they wait. Without a sales forecast, leaders are making critical spending decisions in the dark. If sales don’t go as planned, it could lead to cutting workforce, reducing support, or halting product development.

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Sales forecasting is a muscle, not an item to check off your to-do list. While you should absolutely design a framework for your sales forecasting plan each year, you should also change up your strategies from time to time so new muscles develop.

Craft a sales forecasting plan with your team by focusing on three primary activities:

• Calculating number and time period: Your plan should explain how you’ll calculate the estimated monetary amount and what the timeframes will be. See the section on how a CRM can help with forecasting later in this guide for more on the sales forecasting tools you can use to do this.
• Reviewing and revising: You should also plan to review the forecast at key milestones and revise it if necessary. Most sales leaders track progress against their forecast daily! But you’ll also want to schedule designated check-ins throughout the quarter. Make sure you’re reviewing the latest numbers with sales automation software & tools that sync your CRM’s forecast data.
• Breaking the patterns: Even the best sales organisations need to shake up their sales process once in a while. Breaking your patterns can help you find new ways of crafting even more accurate forecasting. Try skip-level forecasting, ask different questions, have executive sponsorship reviews, and take different angles of the data.
  
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Unpredictable events have an enormous impact on your sales forecast. Extreme weather or economic crises all dramatically change your forecast. What you thought you knew about expected revenue growth can be suddenly flipped on its head.

As soon as an extraordinary event hits, sales and finance leaders at your company will quickly want to know:

• How’s our sales pipeline looking today?
• What are the best- and worst-case scenarios?
• How has the forecast changed from a week or a month ago?

Your forecast implicates resourcing, headcount, and more (see the section on sales forecasting objectives). So although things may be changing quickly, you don’t want to give up on your forecast.

Rather than attempt to recalculate your forecast based on dubious estimates or conjecture, your best bet is to rely on sales analytics software & tools to get an accurate view of deal status and pipeline in real time.

During a crisis, reps need to feed their CRM with data as events unfold so leaders have clear visibility into the rapidly evolving pipe. That data enables those leaders to support their reps with corporate-level decisions about where they should be focusing their time — and craft the new forecasts. Your forecast is only as good as the data coming into it from your sales teams.

In uncertain times, quick access to sales data and the ability to pivot sales territory and resource deployment accordingly can make the difference between business continuity and dissolution. There’s no silver bullet to forecast perfectly in a crisis or unforeseen scenario. But vigilantly updating what’s in the pipeline and analysing sales data more frequently than usual will help you see trends and retool your forecast accordingly.

Empathy and care are always fundamental, but this is especially true in these situations. Empathising with your customers’ challenges and caring for your own sales reps should come before anything else. Build trust with internal and external partners. That trust will help you grow again in the future. Learn more about maintaining customer relationships as a sales leader .

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Only 45% of sales leaders are confident in their organisation’s sales forecasts, according to Gartner . While it’s natural for sales reps to bring in some intuition to their sales forecasts, that’s where room for error can creep in.

This brings us back to embracing a strong data culture . To get a more accurate forecast, everyone in the sales cycle — from reps to managers to execs — should have a stake in making sure those numbers reflect the latest reality. Reps can keep all prospect info up to date, managers can track sales pipeline progress, and leaders can review how all teams are tracking toward those forecast numbers, with AI playing backup to spot any inaccuracies or chances to adjust along the way.

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A CRM gives sales leaders a real-time view into their entire team’s forecast. The tool forecasts revenue by giving you:

• An accurate view of your entire business. Comprehensive forecasts in a CRM come with a complete view of your pipeline.
• Tracking of your top performers. See which reps are on track to beat their targets with up-to-the-minute leaderboards.
• Forecasting for complex sales teams. Overlay splits allows you to credit the right amounts to sales overlays, by revenue, contract value, and more.

A forecast is based on the gross rollup of a set of opportunities. You can think of a forecast as a rollup of currency or quantity against a set of dimensions: owner, time, forecast categories, product family, and sales territory. You can collaborate on forecasts with all the necessary people to see how opportunities are stacking up. Drill down into opportunities by sales leader, operating unit, manager, and individuals.

We also love a CRM with reports and dashboards . These highlight where the business challenges are, in plain and simple terms. It could be that four of five selling teams are at the right growth rate, and we just need to focus on another one. It could be that a certain product is challenged. The data opens up new doors to grow sales and see what could be working more effectively.

Another thing that’s great about a CRM is the guidance from AI. An AI for sales tool offers a neutral perspective on what’s actually happening in sales. For example, AI might note that an opportunity has been pushed out three quarters in a row — a finding that would’ve taken an individual reviewing the data longer to discover. Think of AI as your personal data scientist, taking your forecasting and entire sales operations to a new level.

Predictive AI tools take a look at historical sales data to give you a glimpse of what you might expect in the future. The AI will analyse factors like win rate or number of customer meetings. It takes some of the guesswork out of sales forecasting and helps you get to more accurate numbers. Try to analyse sales data for at least 12 months. Otherwise, there may not be enough data to get accurate sales predictions.

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AI is already used in sales forecasting to analyse sales data and predict future sales. Since forecasting requires processing a ton of historical data in order to identify patterns and trends, AI is a natural choice to help businesses save time while making informed projections.

There are a few ways AI helps sales forecasting:

• AI-driven insights: AI analyses large datasets to uncover patterns and provide more accurate sales predictions, while giving sales reps a comprehensive view of account health with KPIs like engagement, win rate, and average deal size.
• Automated data collection: Automation tools gather and update sales data in real-time, reducing manual entry and errors.
• Predictive analytics: AI models predict future sales outcomes based on historical data and current trends, allowing proactive strategy adjustments.

Modern CRM systems integrate AI and automation. Ideally, the use of AI to work through large sets of data should decrease inaccuracy and increase the reliability of sales forecasts. Now, more AI applications are able to execute tasks with autonomy, rather than acting as a copilot. These AI agents use complex reasoning to carry out tasks end-to-end, delivering accurate results and saving time.

While sales forecasting is highly important, putting all of the pieces together – especially at a regular cadence – can be time consuming. In the near future, AI agents will be able to take some of the most time consuming aspects of sales forecasting and fully automate them, much in the same way they automate certain skills like lead nurturing, sales coaching, or quote generation.

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Sales forecasting is significantly more accurate when using a CRM instead of a spreadsheet. When a company is just starting out, sales teams usually rely on spreadsheets or back-of-the-napkin ways to calculate their sales forecasts. This may work for a while, but eventually, you’ll find this doesn’t scale.

The reality is, selling is more complex than ever. It involves everything from how demand generation campaigns are performing to how your phone calls to prospects are landing. The more you want to sell, the more you’ll want to rely on a CRM.

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