You can’t predict the future—but you can forecast it.
Sales forecasting is a tried and tested way for sales teams to get one up on the competition—even if the competition is their own performance last quarter.
Yet according to a recent Gartner study, only 45% of sellers and sales leaders have high confidence in their company’s forecasting accuracy. Poor data quality is a major contributor to this distrust, and inaccurate forecasts make you an easy target for criticism when things go wrong.
Still, in the game of sales, it’s better to commit to informed goals based on data-backed analysis with the help of solid sales forecasting processes and software tools than to try to pull sales success out of thin air. What’s important is figuring out how to forecast sales as accurately as possible, even with a growing portfolio of products and unpredictable market trends.
Sales forecasting is the process of estimating the total revenue or number of deals you will close in the future based on past data.
Sales organizations combine private historical data as well as relevant public economic data and past trends to create a sneak preview of short-term and long-term possibilities for a company’s success. Forecasts are used to establish monthly, quarterly, or yearly revenue benchmarks, and can be a factor when calculating team commissions.
Forecasting is widely considered to be a foundational aspect of business analytics. That’s because sales forecasts will often form the basis of strategies around everything from hiring new business associates to targeting specific accounts to predicting restock needs for storefronts based on geographic location, to name a few. That’s the power of sales forecasting!
Although forecasting is based on factual information, uncertainty and risk are important factors to consider in the overall equation. Because uncertainty is, well, uncertain, and the effects of risk-taking aren’t entirely predictable either, it’s good to keep in mind that a forecast isn’t guaranteed to come true.
Still, much like a weather forecast, it’s better to have an umbrella with you and not have to use it than get caught in an unexpected downpour without one!
Just like there is more than one way to win a sale, there is more than one way to create a sales forecast. There are four primary sales forecasting methods, each with its own definition, purpose, and process:
Trend analysis is a type of sales forecasting that analyzes past sales data to find patterns. Patterns can exist in many different categories including seasonality, geographic location, target audience, and more. The findings from a sales trend analysis are used to make revenue projections and track potential changes in performance.
Trend analysis is important because it gives companies insight into short- and long-term performance. Companies can focus on different segments of their business to come up with an educated guess about what they need to continue or stop doing to alter future results.
For example, a high-grossing candle and home fragrance company may use trend analysis to understand which popular scents have been in the most demand over winter holidays for the past five years. This information can then be used to more accurately plan for upcoming production needs, create marketing campaigns, and determine how they can either recreate or build on past revenue achievements.
Regression analysis is the sales forecasting method that inspects how individual sales strategies (the independent variable) affect performance (the dependent variable) over time. The model uses past performance data to predict what could potentially happen if the strategy continued or if another was used in its place. This method is more heavily rooted in math than others.
Regression analysis is essential for companies that need to have a deeper understanding of how their sales are doing and how they can change it on a detailed, granular level. In order to conduct this analysis, sales teams will need to have a clear understanding of what is affecting their sales both internally and externally. They’ll also need to have collected enough data regarding these variables over time in order to come up with an accurate forecast.
That also means the variables they assess will have to be measurable, which is why this method typically examines sales strategies over time since companies have access to all of the information needed.
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Time series analysis in sales forecasting uses data collected at various time intervals in order to track changes over time. This can be used to create new sales strategies, determine how likely a certain outcome is, or understand the underlying cause of a predicted outcome.
One of the biggest benefits of using time series analysis over other sales forecasting methods is the ability to predict patterns over seasons, cycles, and trends. It’s also helpful in uncovering irregular past data points that don’t create patterns.
In order to properly use time series analysis to make predictions for the future, you’ll need to consistently record your sales data. Some real-world applications for this method include pricing out physical goods that are affected by supply chain shortages and revenue anomaly detection that should be taken into consideration when planning next quarter’s sales strategy.
Causal analysis is a type of sales forecasting that assesses and predicts how market fluctuations will affect a company’s profits. This type of forecasting makes it possible for sales teams to develop strategies and plans for the foreseeable future. It can also help them develop sales and advertising models that make goals as future-proof as humanly possible.
A causal forecasting model starts with assessing the current state of the market and identifying the factors that will influence its direction over a certain period of time. These include the company’s current position, the independent variables, and the dependent factors.
For sales teams, causal analysis ensures that your department is ready for anticipated demand. This means you can stay on top of potential slow periods such as a recession or industry shakeup as well as periods of high growth where a boom in the market is soon expected. As we’ve mentioned earlier, this sales forecasting method isn’t guaranteed to be completely accurate. But it does make it possible for leaders to not get unexpectedly caught in the rain.
The point of sales forecasting is to find an answer to the question, “How can we expect our sales to perform over a certain period?” All of the above types of forecasting methods are designed to provide those answers.
Regardless of which sales forecasting method you choose, you’ll need to complete the following steps to effectively use it:
Clarify the problem, outline how the information from the forecast will be used, identify who the findings are intended for, and determine how the forecasting project responsibilities will be divided up.
This includes relevant data, information from your CRM, and tools you may need to complete your forecasting method of choice.
What are your predicted outcomes for this forecast? Consider relevant factors such as seasonality. You’ll later compare your expectation versus the result. Also, consider the pros and cons of the particular forecast method you’ve chosen and how that may affect the way you look at its prediction.
Run your model and look for an outcome. The outcome should make sense with the question you asked. If it doesn’t, look for errors and then adjust before running it again. Make sure to record results from each run just in case.
Did the information you received from the forecast method align with your expectations? Why or why not? If the following output data were to come to fruition, how would it affect what your sales team does moving forward?
Still have questions about sales forecasting methods? Check out the frequently asked questions below to get answers!
The benefits of sales forecasting include:
The best way to forecast your sales is to use whichever method is based on your historical sales results. When your sales projections are very close to your actual sales numbers, within a reasonable margin, you know you’re using the right method.
The number one rule of sales forecasting is to use data that’s as accurate as possible. Your forecast will only be as precise as the data it’s based on. It’s also critical that your forecast is conservative and consistent with your historical sales data.
Sales forecasting methods help teams identify potential opportunities and develop a strategy to achieve their sales quotas. Each forecasting method involves using historical data to make a prediction of the future and serves a number of useful functions for any sales team. There are many different techniques you can use to create a sales forecast, but the right one for you depends on what you’re trying to achieve and why.
Data-powered tools such as Nutshell Pro’s forecast report make it easier to view and compare the data you need to make informed predictions. Try our simple, affordable CRM free for 14 days and see for yourself how Nutshell can help your sales team dive deep into closed deals to date, pipeline size, projected sales, and whether or not your team is on track to hit your targets.
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