On the other hand, those still using manual or subjective forecast methods are increasingly at risk of being left behind. Despite this growing imperative, Korn Ferry reports that a whopping 80% of companies miss their sales forecasting numbers by 25% or more.
The disconnect lies in failure to adopt the tools, strategies, and approaches to sales forecasting required by fast-changing and technology-driven sales environments.
Your company doesn’t have to fall into that trap.
With the right knowledge and resources, you can implement a sales forecasting strategy that builds confidence in your sales team and drives revenue for your organization.
The sections that follow will serve as a primer for what you need to know about sales forecasting — its definition, benefits and challenges, strategies and best practices, technology tools to enhance it, and actionable ways to implement it effectively at your company.
Let’s get started.
- Sales forecasts estimate sales revenue for a defined period of time (typically: monthly, quarterly, or annually).
- Decentralized processes and siloed teams are two of the most significant roadblocks to accurate sales forecasting.
- Modern technology tools have reduced the need for subjectivity in sales forecasting and enabled companies to plan proactively for potential disruption.
- Successful sales forecasting positively impacts operations and strategy across the organization (including in marketing, operations, finance, and the C-suite).
- Sales forecasting software centralizes and automates forecasting processes, enabling higher levels of agility and scalability.
What are sales forecasts and why are they important?
A sales forecast is a prediction about future sales-driven revenue. It estimates the amount of product or services that will be sold in a defined period of time — monthly, quarterly, or annually. It can be determined by offering, region, sales team, individual sales rep and more.
Sales forecasting impacts every area of your business. Sales teams use forecasts to set goals and measure performance. But they’re also used by marketing to guide strategy, by operations for resource planning and allocation, by finance to set budgets and predict overall revenue, and by company executives to understand the overall health of the organization.
When sales forecasts are accurate, a virtuous cycle is created in which departments across the organization can optimize their own customer-facing strategies. Things like accurately targeted marketing messages, sound budgets, and precise inventory planning all help create better customer experiences on the front end, ultimately driving higher sales (and the cycle continues).
Challenges to accurate sales forecasting
While the overwhelming majority of sales organizations use forecasting as part of their strategy, far fewer are able to achieve high levels of accuracy. Korn Ferry reports that less than a quarter of sales organizations have a sales forecasting accuracy of 75% or greater.
What is holding these organizations back?
There are common challenges sales organizations encounter when implementing a sales forecasting strategy, including siloed teams, decentralized data sources, too much subjectivity, and failure to plan for disruption.
Let’s take a closer look at each of these challenges.
Decentralized data sources
Accurate data is the cornerstone of sales forecasting. When a company’s data is decentralized — living in different systems or siloed by department — it’s impossible to make informed forecasts because pieces of the puzzle are missing.
For example: performance metrics from marketing campaigns can inform sales teams about levels of market interest for particular products. Without that information, product-based sales forecasts are nothing more than educated guesses.
Siloed teams are common because in traditional business settings, it was practical for individual departments to operate on their own. In today’s interconnected world, it’s no longer possible.
Companies with siloed teams are slower to react, less able to anticipate potential problems, and less informed when it comes to building strategy and making decisions. On the other hand, companies with high levels of alignment and collaboration are able to share insights that inform smarter decision making and more accurate forecasting.
For example: when finance and sales teams are aligned, finance can share budget plans that show anticipated marketing resource allocation. When sales teams are informed about planned investment in particular campaigns or other marketing efforts, they can align their sales forecasts accordingly.
Too much subjectivity
It’s the age-old struggle of the optimistic salesperson: wishful thinking. Or, even worse, the salesperson who only gives deal visibility at the latest stage of the deal when it is practically closed anyway. In times past, when data analytics were slow and less comprehensive, it was common and even expected that some level of intuition would be part of every sales forecast. After all, there was nothing else to go on.
Today, that’s just not the case. Companies with the right technology tools in place have access to sophisticated analytics that make objectivity attainable. Allowing wishful thinking to take over in today’s sales environment can lead to financial losses in the forms of unused inventory, overhiring, or poor marketing and sales ROI.
Failure to plan for disruption
In 2022, the inevitability of disruption is clearer than ever. It’s a mistake for companies to make sales forecasts based strictly on current conditions or a long-time status quo.
Again, technology has made it possible for companies to plan for disruption (and continue to make accurate forecasts in the face of it) with adaptive forecasting. Adaptive forecasting uses AI and sophisticated data analytics to make variable forecasts that account for multiple potential scenarios and analyze the likelihood of each.
Strategies for Making Accurate Sales Forecasts
So what are the right ways to make sales forecasts? How can you implement strategies to avoid common forecasting pitfalls and ensure your sales forecasts are on target? These six tenets of sales forecasting outlined below can help you achieve consistently accurate forecasts your sales teams — and departments across the organization — can reliably depend on.
Prescribe rules for the sales process
A clearly defined sales process creates consistently across sales reps and teams, driving higher pipeline predictability and, ultimately, more accurate sales forecasts. Create rules around when leads should be pushed forward or regressed in the pipeline, and when to close out potential deals as a loss. Educate your sales teams on these rules with consistent training and accessible resources.
Enforce the sales process
Equally important to defining the sales process is consistently enforcing it. This is done through effective sales coaching on the part of your managers. It can be enhanced by a strong revenue intelligence system that can automatically give sales reps signals and reminders about needed action steps and follow ups.
Enforce pipeline hygiene and formalize the sales process
Pipeline hygiene is driven by accurate, up-to-date data that gives an accurate picture of deals currently in the pipeline as well as those recently closed (both by win or loss). Pipeline management can and should be automated whenever possible using your revenue intelligence and other sales software tools.
Make it easy to update your CRM
CRM systems often serve as the main reference point for sales reps and managers as they track deal progress and overall sales performance. It’s also one of the primary informers of sales forecasts. Sales reps must have easy access to update your CRM system every time deal status changes.
This is another area where automation is becoming essential. Manual CRMs are cumbersome and often result in inconsistent updating. Revenue intelligence systems work in coordination with your CRM platform to provide smart, AI-driven data capturing techniques so you can be sure deal status is updated immediately and with minimal effort.
Capture the right artificial intelligence signals
Artificial intelligence is most powerful when companies use it to enhance — not replace — human insights. Be sure to remain active and intentional about setting parameters for your AI tools. Choose signals that analyze both quantity (like number of deals in the pipeline) and quality (like deal progression) to inform sales forecasts in a well-rounded, comprehensive way.
Analyze deals by rep and by time
The five strategies above set the foundation for companies to achieve scalable sales forecasting strategies — ones that can make overarching predictions but also drill down to granular insights. You can use the latter to coach individual sales reps and forecast likely sales numbers based on each rep’s historical performance data and real-time funnel and activity data.
Data sources to use in your sales forecasting
We know now that accurate sales forecasts are data-driven. Choosing the right data sources to pull insights from is critical to your forecasting success. Let’s look at 10 important data sources you should be using in your sales forecasting strategy.
Individual sales rep performance
Every sales rep is responsible for reporting their sales numbers in your CRM system and to sales managers. These numbers can be compared to past performance data for each rep to determine the probable accuracy of their reporting and coach them to make accurate individual forecasts.
Close rates are important at a basic level because they show the number of prospects in your pipeline that become paying customers. Beyond that, however, close rates can provide important insights about factors that impact your sales team’s ability to close deals. They’re a useful starting point for analyzing things like lead quality and external market conditions that impact sales potential.
Average contract value
Average contract value measures the average revenue generated by a single deal in your pipeline. It’s a critical metric for understanding the total revenue potential of your current sales pipeline and creating accurate forecasts based on that information.
Measuring average contract value also helps you analyze whether you’re pursuing the right customers and/or if your targeting strategy needs adjusting to meet revenue goals.
Win rates by stage
Win rates by stage is a metric that tells you what’s happening to prospects at each stage of the pipeline. You can gather important information such as points in the sales process where most prospects convert or, conversely, where you’re losing the most deals to competitors.
When it comes time to make sales forecasts, you can look at your current pipeline holistically and use win rates by stage to understand how — as a whole — it will translate to revenue.
Length of sales cycle
Your length of sales cycle refers to the amount of time deals spent in the pipeline between initial touchpoint to final close. Knowing the duration of your sales cycle enables you to make an accurate forecast for sales revenue based on a particular time frame. For example, does your sales cycle lengthen when deals are bigger, or does it appear to be arbitrary?
Just like sales numbers and overall revenue, your sales cycle length should be a predictable metric. An unpredictable sales cycle means an inability to pinpoint sales forecasts based on a given time period.
Seasonal buying trends are a natural part of any sales cycle. It’s important to track seasonality trends and incorporate them into your sales forecast to ensure proper budgeting and resource allocation.
Selling to existing customers is more lucrative, cost-effective, and time-efficient than winning new customers in almost any market. Particularly as subscription-based and “as-a-service” business models grow in prevalence, companies must shift their sales strategies (and forecasts) to include upselling potential.
Rate of CRM adoption
Your CRM is the central point of visibility for data provided by your sales team — but it is only as valuable as the rate at which your reps use it. It’s critical, then, to both understand and continually facilitate high levels of CRM adoption to ensure the data you pull from it is accurate and complete.
Low CRM adoption rates leave gaps in your sales data that make it impossible to create accurate forecasts.
Customer lifetime value
Customer lifetime value (CLV) is one of the most important revenue-related metrics that companies measure. As it relates to sales forecasts, understanding CLV is critical to the ability to target the right prospects and predict the ROI you’ll earn from each customer segment.
Using CLV as a sales forecasting metric helps you allocate sales resources at the right stage of the sales cycle (i.e. initial interest vs. retention) and forecast the revenue you’ll earn based on your current pipeline.
Unique sales variables
Of course, there is no one-size-fits-all approach to sales forecasting. It’s essential to consider the data sources and other factors that influence your unique organization and industry. For example: have you had a period of particularly high sales turnover? If larger portions of your staff are ramping at the moment, you may experience a temporary dip in sales revenue as they get up to speed. It is important to be able to understand the impact of these reps on overall organizational success.
If your industry took a hit based on external factors beyond your control (for example, the restaurant industry during the initial months of the pandemic), you must adjust your forecasts — and your strategy — accordingly.
How to make accurate forecasts in unpredictable times
While the past two years have shown particular levels of unpredictability, the nature of business today tells us that markets will only become faster-evolving and more complex to navigate as time goes on. Customers are using multiple channels to connect with companies. Globalization and increased connectivity mean geopolitical factors will impact business more heavily than ever. Rapid advances in technology mean that companies must be ready to change and adapt at short notice.
What does it all mean for sales forecasting? In short, businesses must adopt adaptive forecasting methods that allow them to consider and prepare for multiple financial scenarios based on a wide range of factors.
They can do it with adaptive forecasting software. Using AI-powered analytics and high levels of automation, adaptive forecasting software not only uncovers every potential future scenario but analyzes the likelihood that each will actually occur. While more traditional versions of sales forecasting happen periodically, adaptive forecasting takes a continuous approach and provides real-time visibility into short- and long-term predictions.
As a result, companies achieve higher levels of agility, both in sales and across the organization. They have a better ability to innovate and anticipate potential challenges, minimizing their negative impact on revenue.
Why use sales forecasting software?
Sales forecasting software streamlines the many moving parts related to sales and revenue forecasting. It centralizes your forecasting resources and reporting so that you can maintain a more informed, consistent, and confident strategy over time.
Key benefits of sales forecasting software include:
By automating steps in the data reporting and analysis process, you can be sure critical information never slips through the cracks.
AI insights enhance human intelligence by analyzing large datasets and using sophisticated predictive analytics to inform your sales forecasts.
Sales forecasting software analyzes data at a scope and speed simply not possible through manual methods. It allows companies to scale forecasting strategies without compromising critical focus in other areas.
Single source of truth
A sales forecasting software system serves as a single, centralized source of truth for sales-related data. It eliminates reporting inconsistencies and encourages higher levels of visibility and accountability that drive better performance.
Level Up Your Sales Forecasting with Xactly
Xactly’s sales forecasting solution delivers data-informed pipeline analytics to drive consistent sales execution and accurate forecasting for accelerating predictable revenue. With Xactly solutions in place, you’ll experience:
- Pipeline Flowlytics - Identify pipeline changes quickly, see where deals have moved, and drill deeper to understand why.
- AI Forecast Prediction - Remove potential bias with objective, future-looking visibility into what to expect for revenue numbers.
- Opportunity 360 - Consolidate relevant opportunity data into a single pane to quickly view opportunity health, momentum, and more.
- Guided Selling - Promote effective sales execution by guiding sellers on best next actions, opportunity milestones, and actionable alerts to move deals forward.