Connecting all of your sales data with AI paints you an accurate picture of your relationships and pipeline, while boosting intelligent sales forecasting.
Sales forecasting is one of the most important things a company does. It fuels sales planning and is used throughout an enterprise for staffing and budgeting. Despite its importance, many organizations use outmoded practices that produce bad forecasts.
A comparison could be drawn with times past, when farmers depended on signals like cats washing behind their ears or the ache in an old-timer’s knee to forecast the weather. With the advent of supercomputers, weather prediction has vastly improved. But in large enterprises, the tools used to foresee sales remain only somewhat more reliable than an arthritic knee.
Just how dubious are sales forecasts? A full 55% of sales leaders, and 57% of quota-carrying sellers lack confidence in forecast accuracy, according to Gartner.
While you might think this state of affairs will improve over time, Gartner estimates that even by 2025, “90% of B2B enterprise sales organizations will continue to rely on intuition instead of advanced data analytics or their B2B CRM, resulting in inaccurate forecasts, sales pipelines and quota attainment.”
What is sales forecasting?
Sales forecasting is the process of estimating a company’s sales revenue for a specific time period – commonly a month, quarter, or year. A sales forecast is prediction of how much a company will sell in the future.
Producing an accurate sales forecast is vital to business success. Hiring, payroll, compensation, inventory management, and marketing all depend on it. Public companies can quickly lose credibility if they miss a forecast.
Forecasting goes hand-in-hand with sales pipeline management. Getting an accurate picture of qualification, engagement, and velocity for each deal helps sales reps and managers provide data for a reliable sales forecast.
A forecast is different than sales targets, which are the sales an enterprise hopes to achieve. A sales forecast uses a variety of data points to provide an accurate prediction of future sales performance.
More options. More conditions. More stakeholders. More circling-back.
Modern selling is anything but simple.
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Sales forecasting methods and techniques
Although different organizations can have vastly different sales structures and processes, the majority tend to use one or a combination of the following primary approaches to sales forecasting:
- Use of historical data to forecast future results. Looking at historical data is perhaps the most common as well as straightforward approach. The data is readily available, and it makes sense that variations based on factors like seasonality and new product introductions would provide directional insight. The limitation, of course, is that external, macro trends that impact sales aren’t necessarily considered – at least not in a systematic fashion.
- Funnel-based forecasting. For many companies, the current state of the sales funnel is viewed as the most accurate predictor of likely sales outcomes. As long as sellers are providing accurate and frequently updated information about the state of given pursuits, use of the funnel can be a reasonably reliable means upon which to make forecasts.
- Forecasting based on multiple variables. Given that both of the above approaches have inherent limitations, some organizations are looking to build more complex forecasting models that incorporate techniques such as intelligent lead scoring alongside macro factors that are likely to impact the closing of deals. The trick is to put in place an approach that’s sophisticated enough to be meaningful without being too complex to manage and maintain.
Common sales forecasting mistakes
The pressure is on for sales teams to deliver, putting the spotlight on forecasting. Facing stiff competition and an uncertain market, expectations for salespeople keep rising – and forecasts are the means by which sales activity, and by extension the health of the business, is most readily monitored.Unfortunately, enterprises continue to make the same mistakes in their forecasting processes. Here are some of the common pitfalls:
- Sales data fails to provide insight into deal status. A limitation of existing forecast approaches is they are heavily reliant on sellers to provide accurate information about the status of specific opportunities. Given the pressure on sellers, it’s not surprising that the information they provide is often rosier than the reality.
- Time-consuming manual processes cut into valuable selling time. It’s estimated that sales reps spend 2.5 hours per week on forecasting, while their managers spend an average of 1.5 hours. Every hour that’s devoted to these time-consuming – and manual – activities would be better spent on actual sales.
- In the push to commit revenue, accuracy is often sacrificed. Under pressure to provide positive numbers, sellers typically overestimate the number of deals that will close. Perhaps not surprisingly, 79% of sales organizations report typically missing their forecasts by more than 10%. Meanwhile, 54% of the deals forecast by reps never close.
Learn the best practices for the sales audit process, including what it is, who to include, what questions to ask, and how to complete the audit.
Back to basics
Fortunately, there are ways sales organizations can build a forecast process that helps achieve greater accuracy – and, ultimately, better sales results.
At the most fundamental level, improving sales forecasting means using data to more accurately predict performance and manage planning to ensure sales success. This includes steps like:
- Ensuring common agreement about the sales process. Seems like a no-brainer, right? Your sales teams operate from a common lexicon about the sales funnel and the stages within it that your organization employs. In reality, there’s frequently a genuine disconnect.
- Set realistic sales goals or quotas and communicate them. Again, this may seem obvious. But many companies either set unrealistic sales quotas, or fail to effectively communicate individual goals and how they ladder up to the broader plan.
- Benchmark your basic sales metrics. Forecasting involves using historical data to effectively estimate future results. Benchmarking ensures that there’s a sound basis for comparison with prior results.
- Understand your current sales pipeline. If you want to achieve better forecasting, accuracy starts now. New technologies provide sales teams with intelligence that enables them to scrub leads that aren’t actually viable, realistically assess those that are, rescue ones at risk, and commit to a higher degree of precision going forward.
One commonality across these points is that they illustrate the need for cultural change in the sales organization. In other words, you can only drive accuracy in forecasting if salespeople don’t feel pressure to inflate the forecast.
And, by extension, they need to feel comfortable sharing information about deals even when it is not favorable.
Data integration: Key for accuracy
Given all the benefits of accurate sales forecasting, what’s keeping companies from pursuing more modern approaches?
For one thing, regardless of approach, the quality of forecasts is inextricably linked to the quality of the data on which they are based. And it’s not enough to merely have all the data available; it needs to be integrated in such a way that it can be readily analyzed in real time.
Unfortunately, this type of data integration is anything but common. According to APQC’s Planning and Management Accounting Benchmark, only 14% of organizations currently house operational and finance data in a single integrated system. This means that for most companies, forecasting requires the gathering of data across organizational silos and disparate systems, which becomes time consuming and costly.
The good news, however, is that data integration enables organizations to take better advantage of technologies such as AI and machine learning that are ideally suited for spotting the types of trends that data can reveal.
By incorporating state-of-the-art tools into an integrated approach for data analysis, organizations can transform sales forecasting into a strategic advantage.