Chad Cameron is the Head of Solutions Consulting at BoostUp and leads the design and initial deployment of complex B2B software solutions for large enterprise customers. He has 16 years of combined experience in sales, solutions consulting and sales engineering.

The sales forecasting process, the practice of predicting future revenue and performance, is vital for data-driven decision-making, financial planning, and efficient resource allocation. Despite its importance, 66% of companies state that they do not use automated sales forecasting, instead they rely on gut feeling or manually built and operated spreadsheets. This leads to inaccuracies that cause missed opportunities or overspending. 

A solid forecasting process allows companies to set realistic targets, synchronize sales and marketing strategies, and make strategic decisions for sustainable growth. This guide will show you how to create an accurate sales forecast tailored to the specific needs of your business.

Before You Start - Key Considerations

Before you start with the actual sales forecast, it's crucial to lay the groundwork by considering a few key factors. Skipping this step can lead to inaccurate projections and misaligned efforts. What do you really want the sales forecasting process to do for your business?

Establish Clear Forecasting Goals

The first step is to clearly define your forecasting goals. Do you want to forecast product sales? Looking to forecast annual recurring revenue for your SaaS offering? Or is your priority centered on acquiring new customer logos? 

Your goals determine the metrics and data points that should influence your sales forecast. Get stakeholders aligned on these objectives from the outset to ensure your forecasting efforts stay focused and relevant.

Study Your Average Sales Cycle

Knowing the typical time it takes to close deals in your business is essential. If you sell low-cost solutions with a simple buying process, your sales cycle may be a matter of days or weeks. 

For larger, more complex sales, it can span several months or even years. Analyzing historical data on the average length of your sales cycle will help you set realistic forecast periods and better estimate when closed deals will contribute to revenue.

Leverage Your Sales Team's Expertise

Your sales reps are the boots on the ground, closest to your customers and deals. Therefore, their input is invaluable in creating an accurate sales forecast. Involve them early on and have an open dialog. They can provide insights into deal priorities, potential blockers, and subjective factors that data alone may not capture. 

At the same time, their buy-in and engagement are critical to the forecasting process. A forecast that is too complicated or misaligned is likely to be ignored, so make it a collaborative effort.

The Sales Forecasting Process Steps

Now that we've covered the key considerations, it's time to move on to the actual sales forecast process flow. This proven 7-step approach will enable you to create accurate, data-driven projections that are tailored to the needs of your business.

What is the sales forecasting process really about? It's about building a comprehensive, cohesive system that transforms historical data, market insights, and on-the-ground expertise into reliable revenue predictions. Each step plays a critical role in ensuring your forecast accounts for all the relevant factors impacting your sales performance.

From formalizing your sales process, to leveraging cutting-edge forecasting tools and techniques, to setting up strict review protocols -this framework covers all the bases. You'll gain visibility into past trends and current realities, while developing the processes to continually refine and enhance your projections over time.

The end result? A living, breathing sales forecast that provides a clear line of sight into your pipeline and empowers strategic decision-making across your organization. Get ready to leave inaccurate crystal ball predictions behind as we step through this comprehensive sales forecasting process.

Step 1 - Formalize Your Sales Process

Before you can create an accurate sales forecast, you need to have a standardized sales process in place. Without clearly defined stages and activities, tracking deal progress becomes a guessing game. How can you predict closed revenue if you don't know where deals currently stand?

Start by mapping out each step of your sales cycle - from lead generation to negotiation to closed-won. Provide explicit criteria for what qualifies an opportunity to advance from one stage to the next. This creates transparency and alignment across your team.

Additionally, document key sales activities like calls, demos, proposals, etc. That way, you can analyze their correlation to deal progression and revenue. Perhaps opportunities with more customer touchpoints close faster.

With a formalized sales process, you'll be able to systematically track the progress of each deal. This verifiable data forms the basis for your sales forecast and replaces your gut feeling with quantifiable performance metrics. This gives you a rock-solid basis for accurate predictions.

Step 2 - Gather and Analyze Historical Data

To build an accurate sales forecast, you need to understand your past performance. This is where collecting and analyzing historical data comes into play. Start by consolidating data from your CRM, spreadsheets, business intelligence tools, and any other relevant sources into a centralized database or data warehouse.

Standardize naming conventions and define uniform processes for recording sales activities. This lays the foundation for clean, reliable data analysis. The most important historical data for B2B sales forecasts includes:

  • Past closed opportunities (win rates, deal sizes, sales cycles)
  • Prospect/lead conversion rates
  • Key performance indicators of the sales team
  • Volume of sales activity (calls, emails, meetings, proposals)
  • Sales velocity (time from opening an opportunity to closing)

Search for correlations between these data points and successful deals. For example, do shorter sales cycles lead to higher close rates? Do more customer contacts improve the conversion rate? If you recognize these patterns, you can model future projections more accurately.

Advanced forecasting solutions use machine learning to automatically analyze your historical data, gain insights, and even provide data-backed confidence scores for sales forecasting accuracy. This enables a more scientific, less subjective approach to sales projections.

Step 3 - Account for Seasonality

For many businesses, sales don't follow a linear trajectory throughout the year. Some peaks and troughs are determined by seasonal trends, economic factors, and buying patterns. If you don't take this seasonality into account, your sales forecast can deteriorate drastically.

The first step is to find out if and when your business is subject to seasonal fluctuations. Analyze annual sales data to identify recurring cycles or periods of higher or lower activity. Maybe you sell more products around certain holidays or see an increase in SaaS renewals each fourth quarter.

Once you've identified the seasonal patterns, you can model how they might affect your upcoming forecast. Use historical data to calculate a seasonal index - a percentage adjustment based on the peaks and valleys. Apply these indices to your baseline projections to get a more realistic view of expected sales.

Leading forecasting solutions automatically detect seasonal fluctuations in your data and incorporate them into prescriptive forecast modeling. This ensures that your projections accurately reflect the seasonal nature of your business.

Step 4 - Utilize Sales Forecasting Tools

While spreadsheets can get you started, they quickly become cumbersome and error-prone for comprehensive sales forecasting. To elevate your projections, leverage purpose-built tools and advanced forecasting techniques.

Forecasting software integrates with your CRM to automatically pull in deal data, historical trends, and real-time updates - no more manual data wrangling. With all the insights centralized, you can model various scenarios by adjusting assumptions like win rates, sales cycles, and deal values. Run Monte Carlo simulations to estimate probabilities and forecast ranges.

These solutions often utilize machine learning to analyze your data and identify patterns that may not be apparent. For example, AI can surface the top reasons for stalled deals or highlight which activities most impact conversions. You gain deeper visibility into drivers of past performance to better predict future outcomes.

Leading sales forecasting platforms also provide dashboards for multi-dimensional sales forecasting views. Slice your projections by product line, region, rep, or any other parameter. This allows you to pinpoint risks and opportunities across different segments of your pipeline.

Don't underestimate the value of human judgment either. While data and algorithms are powerful, your sales reps have on-the-ground visibility into unique deal dynamics. Integrate their inputs through intuitive forecasting interfaces to increase accuracy.

The best approach blends historical data, advanced forecasting techniques, and strategic human guidance. With the right tools in place, you can confidently map out your revenue potential.

Step 5 - Monitor and Refine Your Forecast Regularly

A sales forecast is not a set-it-and-forget-it exercise. It's a living, breathing projection that needs to be constantly monitored and refined as the situation changes.

Establish a rhythm of reviewing your forecast on a weekly or bi-weekly basis. As you receive new sales data and pipeline updates, input those into your forecasting model to understand how projections have changed. Perhaps key deals have slipped, been won, or updated in value - adjustments are needed.

Don't just look at the numerical outputs either. Analyze the driving factors behind the forecast fluctuations. Are certain products selling faster than anticipated? Is a new competitor affecting your market share? This qualitative analysis will allow you to adjust the price accordingly.

Leading forecasting solutions make this easy by automatically incorporating CRM updates and highlighting areas that have changed significantly since the last review. The best platforms even perform automatic change tracking to highlight deviations from the forecast over time.

Step 6 - Set Up a Review and Approval Process

A sales forecast is only as reliable as the care with which it is prepared. Establish a structured review and approval process to ensure accuracy and accountability.

Have sales reps initially submit their projections based on the opportunities they manage. Their managers then review them and may adjust them based on additional information. Then, management reviews the consolidated forecast before finalizing it.

You can also configure customized locking rules. For example, prevent changes to the forecast after a certain date without manager override. This promotes discipline.

Step 7 - Communicate Your Forecast Effectively

Once your sales forecast steps are done and the prediction is finalized, it needs to be communicated effectively throughout the company. Don't let it gather dust in a spreadsheet. Leading forecasting platforms make this easy with built-in reports, dashboards, and visualization features.

You can present a consolidated view to management, but also provide summarized, role-based views - without overwhelming employees with too much data. Management should see the team and individual performance. Reps only need their projections.

Use customizable dashboards to highlight key metrics such as quota attainment, pipeline coverage, forecast accuracy, and risk factors. This enables data-driven organizational alignment on goals and priorities.

Distributing a clear, understandable forecast promotes accountability and acceptance of the forecast. When everyone understands the "why" behind the numbers, your projections become a driving force.

Supercharge Your Sales Forecasting with AI

While the core sales forecasting process provides a solid foundation, adopting AI-powered forecasting tools can take your projections to new heights of speed and accuracy. By harnessing machine learning, these solutions automate and enhance many of the critical steps.

Instead of manually gathering historical data, an AI forecasting platform seamlessly integrates with your CRM and other data sources to continuously import updated information. Its algorithms then analyze this data, automatically identifying relevant patterns, correlations, and predictive insights far beyond human capabilities.

From deal progression trends to sales cycle benchmarks to activity-based likelihood modeling - AI factors in numerous dimensions to produce statistically sound forecasts. Rather than just straight-lining based on an average, it accounts for nuanced deal dynamics like seasonality, sales stage, product mix, and more.

The AI enhances human judgment too. It provides accurate, data-driven confidence scores to validate (or invalidate) subjective forecasts from reps and managers. This promotes accountability and alignment across the organization.

Ultimately, learning how to create a sales forecasts with AI-powered tools combines historical realities, current signals, and advanced modeling techniques to predict your revenue potential with pinpoint precision. While upfront implementation requires an investment, the dividends quickly pay off through improved forecast accuracy, efficiencies, and optimized decision-making.

Final Thoughts

An effective sales forecasting process is essential for any business aiming to drive predictable revenue growth. By following the sales forecasting steps outlined here - from formalizing your sales process to leveraging AI-powered forecasting tools - you'll gain an accurate view of your pipeline and the ability to confidently map out your path forward.

Using BoostUp.ai’s sales forecasting platform, companies like SheerID achieved a 15% increase in week-over-week forecast participation and 2x better pipeline hygiene after implementing AI-powered forecasting solutions. At Unily, sales forecasting accuracy improved by 16% from the first day of the quarter due to implementing the new process and software solution.

Reliable forecasts drive data-driven decision-making, strategic resource allocation, and alignment of the business to achievable goals. They allow you to identify risks early and course correct before they derail your numbers.

When you use this comprehensive forecasting framework in conjunction with cutting-edge AI forecasting tools, you can say goodbye to spreadsheets and welcome a scientific, AI-powered sales forecasting process that maximizes your revenue potential.