Sales forecasting is essential for uncovering key insights and ensuring data-driven decision making. But its often also a high-risk game of chance. Today, too many organizations rely on disparate data and gut instincts to inform their revenue predictions.
A businesss ability to hit revenue targets consistently and predictably can mean the difference between hitting your revenue goals and losing all credibility. Those who forecast too high suffer an embarrassing miss in expectations with company leaders and investors and leave the business with insufficient bookings to hit cash flow and profitability targets.
The opposite is no better. When leaders forecast too low, surprise over-performance raises doubt that they will achieve future forecasts. Over time, they may earn a reputation of being a sandbagger who intentionally misrepresents their numbers to look good. The unexpected cash flow and profitability set a challenging precedent for future quarters.
Proper forecasting that is, forecasting that results in accurate projections backed by reliable data requires an understanding of purpose, context, and intended outcome. These components will help you determine the right approach for your unique use case and enable you to build credible, consistent predictions that boost both efficiency and success.
Your organizations growth hinges upon its ability to properly forecast. This may seem like an overstatement, but without accurate revenue predictions, you simply cant glean the insights required for:
Choosing the right sales forecasting method is equally important as forecast itself. In August 2021, Outreach commissioned Forrester Consulting to conduct a survey of 212 sales leaders representing more than twenty industries at companies with headquarters in the UK and North America. Forrester found that only 43% of respondents are forecasting within 10% accuracy. Even more glaring, 10% of respondents indicated that they regularly miss their forecasts by 25% or more.
Revenue leaders whose teams are weak at forecasting are perpetually at risk of surprise outcomes. They do not trust their available data, so they are forced to make educated guesses based on intuition and experience. Because they cant see what is actually happening in their deals, they dont know what actions to take to improve the outcomes.
This is largely a result of inaccurate, stale data, which stems from outdated or disjointed tools for collection and manual, error-prone data entry processes. For revenue organizations that still use these traditional methods, forecasting is an imprecise science and a huge time investment. However, when you can build a confident forecast, you can enhance both internal and external operations. Consistent, precise predictions can help you to set and achieve realistic goals and how each reps performance measures up to those objectives. You can also make operational decisions, like hiring, budgets, and investments, with greater confidence, as the right approach (paired with the right technology) promises credible, accurate insights.
Your sales forecasting method should align with your business goals, needs, and resources. Not all methods will garner the results you're looking for, so as you evaluate your forecasting options, you'll need to keep some key considerations in mind:
Use/purpose of the forecast - Its important to align the method you use with the actual objective(s) of your forecast. This allows you to balance the forecasts cost (i.e. scope, required resources) vs. value (i.e. precision) based on the impact a certain level of accuracy will have on the audience. The technique required for a forecast that will be used to make decisions around production and inventory, for example, will need to be quite sophisticated to reap reliable, highly accurate results. On the other hand, a forecast that will be used for a more general projection of growth (without any changes to existing sales and marketing strategies) can be built with less accuracy and more flexibility.
Business context - Long-term sustainability relies on a deep understanding of all the trends, impacts, and relationships between your business and the industry in which it operates. For a newer, emerging organization, this might mean your initial forecasts will be built on simpler, less accurate methods, as you wont yet have robust data around how your business operates within the larger marketplace. A more mature company, though, should take into account a broader dataset that reflects competitive performance and utilize a more advanced forecasting method for projections.
Amount of historical data - The method you choose will always be limited by the amount of historical data at your disposal. If you've been capturing accurate data over a longer period of time, for instance, you can use that data to create a forecast that acts as a benchmark for future demand. If you're a brand new business, you simply wont have the foundational data needed for any forecasting method that relies on a wide breadth of past information.
Time to complete forecast - Some forecasting methods take quite a bit of time to generate the report particularly if you dont have the proper systems in place to handle complex calculations. If you cant provide high-quality, timely data or if you dont have an intelligent tool to execute intricate calculations, then you should avoid any forecasting method that relies on specific variables that are unique to your company (e.g. win rate, opportunity value, etc.). If not, you'll risk wasting precious time using imperfect data to create forecasts that dont provide any real value.
Accuracy needed - When determining which method to use, you should determine whether the results you're expecting should be qualitative or quantitative in nature. If you're entering into a new market, for example, a qualitative forecast can certainly be sufficient, so the method you use can rely upon industry knowledge and observations. If your business is shifting strategies to become more data-driven, however, make sure the forecasting method you choose is qualitative. That way, your predictions will be based upon the highly accurate data, historical analysis, and pipeline visibility required for a more data-driven approach.
Once you've carefully weighed the factors above, you're ready to actually choose the forecasting method that best fits your objectives. Before we dive into the specifics of each approach, keep in mind that there are three basic types of forecasts under which each method falls:
Forecasting can seem like a bit of an uphill battle for fledgling businesses because they lack strong, historical data. But that shouldn't scare you off from building forecasts altogether, as they're a necessary part of understanding risks, needs, and potential opportunities for a young organization. Whats more, theyre crucial for defending your value and assumptions to possible business investors.
The new business approach focuses on the ways in which your operations impact the customer journey. By mapping out the three most prominent parts of your customer journey (i.e. sales drivers, product mix, and customer lifetime), you can better understand how target buyers interact with your brand and use that information to build your forecast.
Start by identifying your preferred customer acquisition strategy (e.g. direct sales approach, marketing approach, or a combination of the two) as well as your expected outcomes. Here are some common growth models for each strategy:
Next, you'll need to determine how each new customer equates to actual revenue. Take a detailed list of the products and/or services you sell and identify the percent of customers that will buy each. You can use that number to convert new customers into unit sales.
Finally, you'll need to identify how target customers interact with your company. This is largely based on your existing business model (one-time sales vs. subscription-based sales) and whether or not your offerings drive multiple future purchases or long-term subscriptions. Make sure you focus on factors like customer retention and rate of repeat purchases. This will help you to more accurately predict customer behavior.
One of the quickest methods you can utilize is historical forecasting, which takes into account past sales data over a given period of time. This approach is best for organizations that operate within a steady marketplace thats not consistently impacted by changing dynamics (seasonality, a market boom, etc.). It does require a fair amount of clean, reliable data, so it might not be a great fit if you dont have strong data collection tools at your fingertips.
Its important to note that a historical forecast operates on the assumption that buyer demand will increase and that your ability to close deals wont be affected by external factors thus, it should be treated as more of a benchmark than your be-all-end-all prediction.
The easiest way to calculate a historical forecast is by looking at monthly recurring revenue (MRR). For example, if your sales reps sold a total of $100,000 in June, you'd lean on the assumption that they'd make at least $100,000 in July, too.
To make your projection a bit more accurate, add in your historical growth percentage. For instance, if your sales team has consistently increased sales by 5% each month, you can safely estimate that they'll reach $105,000 in sales for July.
If you're looking for extremely accurate forecasts, multivariable analysis is the way to go. Keep in mind, you'll need a large amount of clean data and likely a sophisticated tool to handle some complex equations so skip this approach if you still rely on manual methods for tracking the deal progress and individual sales activities within your pipeline.
Multivariable analysis relies on predictive tools that take into account many different factors, like average sales cycle length, probability of closing based on opportunity type, and each reps performance.
For example, you might have two sellers working individually on two separate deals. Sales rep A is further along in the sales process for a large deal size, with a certain number of days remaining in the sales quarter. That, combined with her average win rate for this specific stage in the sales process, might indicate a 50% probability of her closing the deal; giving you a forecast of, say, $10,500.
Seller B is still in the beginning stages of the sales process for a smaller deal size, and he has a higher average close rate. Based on these factors, you might calculate that he also has a 50% probability of closing the deal, with a forecast of $7,200.
When you add them together, you'll get a combined quarterly sales forecast of $17,700. Of course, this is an overly-simplified example, as a real multivariable forecast considers many variables for many different reps.
Opportunity stage forecasting
If your existing sales process runs like a well-oiled machine, you can use opportunity stage forecasting to predict the likelihood of each opportunity closing (based on prospects current position within the sales process). As deals move further along in the pipeline, theyre more likely to close.
Keep in mind, this method doesn't take into consideration the age of each opportunity, so it produces more of a rough estimate than an accurate projection. Also, because opportunity stage forecasting relies on historical data, its not a great method for businesses who frequently change messaging, offerings, or parts of the sales process.
Start by determining a reporting period, which should be dependent upon your sales cycle length and sales teams quota, then multiply the potential value of each deal by the probability of it closing. For example, your past data might help you determine these likely-to-close percentages for your pipeline stages:
The opportunity stage forecasting model would predict that an $8,000 deal at the relationship building stage would have a 45% chance of closing. Thus, its forecasted amount would be $3,600.
Sometimes, you just need to rely on gut instinct to make your predictions. This is especially true if youre pressed for time or you just dont have reliable data at your disposal. Intuitive forecasting is based on the opinion of your reps regarding whether or not each opportunity will close within a given period of time.
Its a highly subjective option, as reps are generally optimistic about their ability to close a deal. On the other hand, it takes into account the perspective of your most valuable, experienced resources: your salespeople.
This method can be made more accurate if your sales leaders and managers have access to reps meetings, phone calls, and other customer interactions. If your organization uses intelligent virtual assistant technology, for example, then validating rep assessments for accuracy is likely worth the effort. But if they dont have the right tools for support, theres simply no way to realistically scale that kind of verification.
It can be difficult to accurately predict the likelihood of an opportunity closing if you rely on subjective information. The length of sales cycle method takes into consideration key factors like the age of individual opportunities and how a prospect entered the pipeline to provide a more precise projection.
Lets say a sales rep books a meeting with a prospect who they just started talking to this week. Based on the likely-to-close percentages we outlined in the opportunity stage forecasting section, this would mean the prospect has an 80% chance of closing. But that calculation doesnt take into account the fact that they're unlikely to buy because of how young the opportunity actually is.
Length of sales cycle forecasting can even be used for different sales cycles (e.g. normal leads vs. referrals vs. leads from field events). With this method, you can categorize each type of deal by the average sales cycle length, which helps to boost accuracy.
But its important to remember that this technique is only precise if your reps track when and how prospects enter their pipelines. They need intelligent, integrated tools that let them track and manage these details without having to waste precious time on manual, error-prone data entry.
If you plan to deploy a new product or service, you might have a difficult time predicting your future sales. With test market forecasting, you focus on two smaller target regions and apply two separate sales strategies for each. By measuring the results, you can better understand how the product or service will perform and how much revenue it will likely generate.
For example, you might establish a direct sales strategy for Market A, while taking a heavy marketing/advertising approach for Market B. Then, you can collect data for each stage of the sales process, respectively, and glean insights into:
Moreover, test market forecasting can help you to determine whether or not the new product or service is truly viable, without spending excessive amounts on broader sales efforts. Its not always a true reflection of the general market, though, since the smaller regions you choose might have more or less buyer demand than the industry as a whole.
Regardless of the forecasting method you choose, accurate predictions require sophisticated tools for support. Many modern businesses have realized that going it alone just isn't realistic in terms of scalability and precision. Thus, they've begun to leverage some of these common tools:
Most forecasting solutions are a black box for customers- they're forced to trust projected numbers without any details. But leaders should have the tools they need to tweak assumptions and see the logic behind their outcome.
Modern, intelligent software offers tools that build confidence in forecasts by showing its evolution over time, including how and why it changed. This gives leaders visibility into rich data inputs and historical snapshots to augment their specialized forecasting methodology.
As you evaluate your technology options, make sure you choose a tool that enables you to model assumptions and make adjustments based on knowledge and intuition. That way, youll always have the flexibility to build impactful forecasts that enable you to make meaningful decisions - all for improved efficiency and increased revenue.
Its true that choosing the right method is a crucial part of gaining valuable, accurate forecasts. But the right method will only get you so far if you dont have tools that offer transparency, reliable data, and the ability to fix potential issues before its too late.
Sales teams' standard approach to forecasting is often filled with gaps when the data they rely on is pulled from disparate tools and dashboards. Without a consolidated view of pipeline health and buyer insights, teams end up guessing their forecast number and they're perpetually at risk of surprise outcomes.
With a unified Sales Execution Platform, forecasting can shift from a critical gap to a seamless, highly-valuable component of your business. Outreach's forecasting delivers real-time pipeline data and buyer engagement signals to bring science to the art of forecasting, enabling revenue leaders to go from guessing the future to changing it with recommended actions. What's more, Outreach Commit shows you the math behind every prediction, so can understand what's actually driving the number and how to change it.
Todays shifting economy means revenue leaders have to do more with fewer resources. So how do you deliver on lofty revenue targets while also reducing costs? It starts with more efficient forecasting processes. Instead of spending anxious hours on manual forecasts, modern revenue leaders are embracing ways to save time and refocus their energy on growing revenue. For Outreach's top resources on forecasting efficiency, download the free content bundle: Your Road to Forecasting Efficiency.