A new wave of simple-to-use technology, driven by artificial intelligence (AI), is enabling data-driven sales forecasting. This guide to sales forecasting will explore what sales forecasting is, why it’s important, and how AI-enabled sales forecasting is set to help companies do more with their forecasts.
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Sales forecasting plays a critical role for most companies: Sellers use sales forecasts to create benchmarks, revenue operations (RevOps) teams and leadership use them to make data-driven decisions, and financial teams use them to budget. Yet fewer than 25% of sales companies have a forecast accuracy of 75% or greater. There are myriad reasons that sales forecasts aren’t as accurate as companies would like: subjective information, incomplete data, and outdated forecasting tools, to name a few.
Fortunately, a new wave of simple-to-use technology, driven by artificial intelligence (AI), is enabling data-driven sales forecasting. This guide to sales forecasting will explore what sales forecasting is, why it’s important, and how AI-enabled sales forecasting is set to help companies do more with their forecasts.
What is sales forecasting?
Sales forecasting is the process of predicting sales and revenue for a business over a certain period of time, such as monthly, quarterly, or yearly. To develop a sales forecast, leaders use a data-driven process that takes into account historical sales, industry trends, current sales pipeline information, and more.
For most businesses, sales forecasting is an essential component of business management. Sales forecasts inform sales strategies, inventory management, cash flow and budgeting, and hiring decisions. In other words, businesses rely on sales forecasting to prepare for the future, and accuracy is a constant concern. According to Gartner, fewer than 50% of sales professionals have high confidence in their forecasting accuracy.
In part, this is because sales forecasting, like weather forecasting, makes a prediction based on constantly changing behaviors and conditions. No sales forecast can be 100% accurate — especially when companies use dated sales forecasting techniques that rely too heavily on the past and produce a static, unchanging report. It shouldn’t come as a surprise that those companies are dissatisfied with their sales forecasting process, according to McKinsey: 40% say their forecasts are not particularly accurate and that the process takes too long.
Fortunately, there are AI-enabled forecasting tools that can boost forecast accuracy by providing a real-time, dynamic forecast that gives sellers and leaders a better target to aim for. More on that later in this article.
Why is sales forecasting important for businesses?
The importance of sales forecasting can be felt throughout an entire organization. Almost every employee benefits from the information and predictions that forecasting can provide.
At the leadership or C-suite level, sales forecasting is used to track the overall performance of a company, cash inflows, and revenue projections over the short- and long-term. Business decisions — such as strategic hiring and inventory management — are also based on sales forecasts. So leaders need trustworthy forecasting to make data-informed decisions about their company.
Customer success teams
Customer success teams can use sales forecasting to better prepare for what’s to come in the future. For example, by knowing how many deals are supposed to close and when, they can plan when they’re more likely to see increased call volume.
Like leadership teams, finance and accounting teams use sales forecasts to track financial projections, cash inflows, and projected revenue statements. This information is used to design quarterly or yearly budgets and monitor any potential financial issues or risks the forecast uncovers.
Communications and marketing teams use sales forecasts to strategize, plan, and implement audience-specific marketing campaigns. A sales forecast helps marketing teams develop a budget for ad spends, which can help teams plan for the short- and long-term. Additionally, armed with information about when the sales pipeline is most active, marketing teams can plan lead-generating efforts to closely align slow periods with bigger marketing efforts.
Finally, sales teams use forecasting to make projections about their prospects, leads, and deals. For example, sales forecasting can illustrate how likely a deal is to close and the average time it takes for leads to move through the pipeline. Performance benchmarking for sales representatives is also often based on sales forecasting.
What does sales forecasting include?
The kinds of data included in sales forecasting depend upon the company, how much historical data it has, its industry, and the type of product or service it provides. In other words, no two sales forecasts are alike, but we can learn an incredible amount by studying various sales forecasting examples.
However, there are some general sales metrics and other factors affecting sales forecasting and sales forecast formulas that should be accounted for by most companies. While the data gathered from these factors may vary, each of the following types of data can impact a company’s short- and long-term sales forecasts:
- Historical data: Companies should maintain accurate, up-to-date sales data to draw from. Automation tools can expedite this process. Collective[i]’s Intelligent WriteBackTM automates CRM data capture from any tool sellers use, giving sales teams a productivity boost of up to 15% to 20% and ensuring accurate, timely sales data.
- Seller insights: Surveying, or taking a pulse of how sellers are feeling about their leads and prospects, can and should inform sales forecasting. While sellers’ opinions invite bias, your sellers are experts who may have valuable insights into their deals.
- Employment changes: A change in the amount of sellers — whether that change is an increase or decrease — can impact a sales forecast.
- Territory updates: Similarly, having sales representatives change territories can impact sales forecasting. It takes time for sellers to fully understand their terrority.
- Policy or pricing changes: Any updates or changes to a company’s products or services, including pricing, customer payment plans, or even updates to commission policies, can impact forecasting.
- Competitor product changes: New competitors or innovative products being released can impact sales and should be accounted for when sales forecasting.
- Internal product changes: Changes a company implements to its own product offerings, such as a new complimentary service, can improve sales forecasting and must be accounted for.
- Economic changes: Buyers are much more likely to buy during a strong market. On the other hand, they’re less likely to make purchases during a weak economy.
- Customer market: For B2B sales forecasting, it’s especially important to understand the client’s market. If the clients are B2C companies, it’s important to keep up with their market, too. Any changes can impact forecasting.
- Seasonality: It’s not uncommon for sales to follow the flow of seasons. For example, companies might notice an increase in sales every year in the first fiscal quarter. It’s important to take note of these trends to account for them in a forecast.
What are the types of sales forecasting
What are the types of sales forecasting? Broadly speaking, sales forecasting tactics are often broken down into quantitative and qualitative buckets, with a third category quickly coming to the forefront.
Qualitative sales forecasting
Qualitative sales forecasting is an opinion-based approach that favors personal insight and intuition over cold, hard facts. Think of this as forecasting based on the human element of sales. When a salesperson is at their best, they’re profoundly human. They connect with their prospects on an emotional level. They prioritize opportunities based on information, yes, but also on that gut feeling that tells them, “This is the lead I need to chase first.”
Quantitative sales forecasting
Quantitative sales forecasting is all about the analytical left side. This approach involves collecting data and interpreting it to guide sales forecasts. It looks at numbers like the dollar amount of previous sales, the theoretical value of future sales, timelines to close, and other factors to help businesses predict when they’ll make money, how much they’ll make, and from who. This can be handled from a “top down” perspective, looking at broader economic data, or “bottom up,” which focuses on a company’s own data first. Many experts say to do both — which can be time consuming.
AI-enabled prescriptive forecasting is the way forward for sales forecasting. Combining both quantitative and qualitative data and marrying it with a neural network, this approach gives sales teams more data and automates everything but what a human needs to do to close a deal. While some AI sales forecasting tools can automate certain tasks, like data entry and maintenance, we use AI to find challenges and opportunities to close deals and help sellers identify their best next step.
What are the methods of sales forecasting?
There are three primary types of sales forecasting methods: opinion, augmented, and prescriptive. While opinion and augmented forecasting methods were the industry standard of forecasting for many years, businesses looking to get ahead of the curve are increasingly recognizing the benefits of prescriptive forecasting. But before we get into what is the best forecasting method for sales, let’s explore each type further.
The oldest form of forecasting, opinion forecasting uses qualitative information to make projections about how sales will grow or shrink in the upcoming month, quarter, or year. In other words, this type of forecasting makes use of the opinions and gut instincts of business leaders and sales team members.
Here are some examples of opinion-based forecasting:
- Jury of executives: This technique is essentially a composite of opinions from several experts. First, the experts provide their opinions. Then, they review the opinions of the other experts and revise their original thoughts based on the others’ opinions. Finally, the opinions are combined to create a sales forecast.
- Sales opinion: In this technique, the opinions, gut instincts, and thoughts of sales representatives are taken into account to create a sales forecast.
- Expert opinion: This forecasting technique relies upon industry experts who provide their opinions, thoughts, and observations; a forecast is created from their knowledge.
While there is certainly value in qualitative, opinion-based information, this type of forecasting is prone to inaccuracy due to interviewees’ overconfidence, bias, or lack of awareness of external market factors. That’s where the need for data and AI comes in.
Early augmented forecasting used historical, quantitative company data to build predictive models about future sales and revenue. Then, that data was augmented with opinion-based, qualitative information for a fuller, more accurate forecast.
Techniques used to gather historical data for augmented forecasting include:
- Exponential smoothing: Newer data is given more weight than older data to account for the fact that the newer data is likely more relevant.
- Market analysis: This technique analyzes trends and characteristics in the target market, including average purchase per customer, market growth, and more, to predict future sales. The best market analyses will segment customer populations into unique target markets to facilitate more detailed, accurate forecasts.
- Opportunity stage forecasting: Sellers break down the sales pipeline into stages and discuss the probability of closing a deal in each stage to project future revenue.
- Simple moving average: The average sales of a given year are plotted to visually illustrate trends and make predictions based on those trends.
- Time series forecasting: Best used when companies have many years of historical data, this technique analyzes seasonal patterns, cyclical patterns, trends, and growth rates during a specific period.
- Trend projection: This technique involves graphing annual sales data to note past trends and using those trends to predict future sales.
As technology became more advanced, so did augmented forecasting. More recent approaches make use of AI-lite technology (i.e., tech that focuses on automation and other basic AI tasks) to interpret and analyze the historical data and develop predictive models about projected sales and revenue. Usually, AI-lite technology uses a single data source — often a company’s CRM data — to track and improve the forecasting process. Because it relies more heavily on quantitative data, augmented forecasting can give sales teams a more objective forecast than opinion-based forecasting can provide.
However, where augmented forecasting loses out is that it’s not taking full advantage of the power of true AI. A more modern approach to sales forecasting calls for advanced, AI-enabled technology to provide broader, more dynamic forecasts.
The newest and best method of forecasting, prescriptive forecasting capitalizes on machine learning (ML) and neural networks — hallmark features of true AI — to mimic the thinking of a human brain. Neural networks work by being fed massive amounts of structured and unstructured data. While ingesting the data, the neural networks identify underlying relationships and patterns in the data sets, allowing computer programs to not only solve problems and recognize complex patterns but also learn and improve accuracy over time.
Prescriptive forecasting makes use of a neural network that’s fed internal company data and external networked data. This allows the neural network to understand buyer behavior, identify market trends, and offer real-time insights and recommendations. Companies using prescriptive forecasting can achieve a broader picture of the overall market with more accurate and actionable forecasts.
Rather than producing a static report for teams to work from — as less advanced forms of forecasting do — prescriptive forecasting is designed to:
- Use multiple data sources, including opinion and historical data, real-time market data, internal data sets, and networked external data to provide a better, more adaptive forecast.
- Save teams time by creating an automated, adaptive forecast to work from — eliminating the need for time-consuming Forecast Fridays.
- Provide information and feedback about current buyer behavior to help sales teams better understand relevant trends.
- Create dynamic forecasts that automatically update daily. These forecasts are actionable, providing updates, recommendations, and risk alerts to sales teams so they can course-correct and improve outcomes.
By using the power of AI and neural networks, prescriptive forecasting tools solve the issues of earlier forms of forecasting: bias, wasted time, inaccuracy, and human error. They also turn data into not just insights but recommended actions for enhancing sales. According to Gartner, 60% of B2B organizations will move to data-driven selling processes by 2025 — which means companies that stick with outdated forecasting methods are set to be left behind.
Tools like Collective[i]’s Intelligent ForecastTM can bring teams into the future. Intelligent ForecastTM is the first automated, daily, and adaptive forecasting tool on the market. By augmenting traditional sales signals with a vast network of dynamic market data, companies can achieve a more actionable and dynamic forecast that allows sales teams to adapt to buyer and market changes rapidly. Intelligent ForecastTM also provides users with updates, recommendations, and risk alerts. What’s more, with a more actionable forecast to work from, sellers can identify who is most likely to buy and who isn’t, which facilitates more productive selling.
Why is forecast accuracy important?
Although the benefits of sales forecasting accuracy may not be immediately apparent — especially for companies still operating off of dated forecasting techniques — but for the long term, they are manifold. Here are some of the benefits of accurate, actionable forecasting.
- More accurate budgets: Accurate sales forecasting leads to better, more accurate budgets. As a result, departments won’t have to rely on guesswork when financially planning for the future.
- Enhanced organizational decision-making: Because many business decisions are made from sales projections, sales forecast accuracy can improve leaderships’ ability to make staffing and inventory decisions.
- Improved cash flow management: For financial teams, an accurate understanding of a company’s cash inflows and outflows can improve day-to-day operations.
- More confidence in the sales process: The better the forecast, the more likely sales teams are to hit their benchmarks. Accurate forecasting gives the entire sales team an attainable goal to hit, which can, in turn, increase the trust that leadership has in the entire sales process.
- Improved sales outcomes: Sales forecasts are essentially a benchmark for sales teams to meet — not a singular, static bull’s-eye. With adaptive forecasts, sales team members are empowered to concentrate on the right sales activities and actions at the right time — not just on hitting a forecast at any cost. This allows sellers to stick with deals that have the most potential, even if they require more nurturing, for example.
- Better tracking of potential problems: Accurate and dynamic sales forecasting processes can alert sales managers and leadership teams to potential problems. Early warning can provide them with the time to course-correct or take action to avert potential issues. Collective[i]’s C[i] RecommendsTM provides teams with news, risk alerts, and more to drive the best results throughout the organization — and it’s all automated for ease of use.
What are the steps involved in sales forecasting?
For companies looking to create or update their sales forecasting process, having an understanding of the major steps that go into creating a sales forecast can help them employ techniques to achieve more accurate and actionable sales forecasts.
1. Define goals
As a first step, leaders and teams should define their goals. These will vary from company to company. For example, companies that sell a variety of products or services may want to forecast for each product, while other companies may be more interested in the granular details of subscription renewals. Some companies may be more interested in weekly or monthly forecasts, while other companies may find quarterly forecasts to be useful — the frequency of forecasting largely depends on the average sales cycle length.
Because many different teams use sales forecasts, it is also important to include the needs of those teams in the forecasting goals. Finally, many teams already have goals or quotas in place for their sales representatives. Aligning the goals of the sale forecasting process to those benchmarks can help make the forecast more instructive and actionable.
2. Establish data sources
Once the goals of the forecast are decided, companies must establish the kinds of data that need to be included in the forecast. Earlier in this article, we discussed common factors that should be included in sales forecasting. But companies must also establish their own data processes for sourcing internal and external data.
Internal data Internal data can be gathered from CRMs or other readily available sources. It includes:
- Historical company data
- Seller insights
- Employment changes
- Terrority updates
- Policy or pricing changes
- Internal product changes
External data External data is gathered from outside sources, providing insights into the competitor landscape or the market. This includes:
- Competitor product changes
- Economic changes
- Customer market
- Buyer behavior changes
3. Choose a forecasting method
Companies should choose the forecasting method that fits their structure, history, and available resources. For example, companies with a rich history of data for their products will likely have a different approach to forecasting than will startups with minimal amounts of historical information.
No matter what kinds of internal data a company has access to, prescriptive sales forecasting tools can be a game changer for companies that are to make the most out of their sales forecasting process.
4. Implement sales forecasting best practices
To bring all the steps of a forecast together, it’s important to keep some basic concepts in mind. For those wondering, “What are sales forecasting best practices?” here are three rules of thumb to work from.
Use better data
Companies must invest in ensuring their internal data is accurate and up to date. Sales forecasting won’t be reliable or informative with unreliable or incomplete internal data. Collective[i]’s Intelligent WriteBackTM, data is automatically captured and stored in a CRM, eliminating manual data entry and the potential for human error.
There are two basic ways to get external data. The manual method of having teams attempt to gather this data from case studies, competitor websites, and data repositories is time-consuming at worst and inefficient at best. The second method is to use AI-enabled technology that simplifies the process. Collective[i]’s proprietary IntelligenceTM network contains vast amounts of dynamic market data that users can access at the click of a button to get high-quality and data-driven insights.
A sales forecast isn’t a static document or a one-time exercise — a sales strategy shouldn’t be, either. One of the best ways to take advantage of sales forecasting is by understanding that it’s a constantly moving target. The best sales teams understand the need for flexibility and know how to pivot quickly when necessary. Sometimes this means adjusting sales forecasts on the fly. Using Collective[i]’s Intelligent InsightsTM, sellers take advantage of a daily optimized to-do list based on the opinions and judgements of a company’s top sellers and the ever-changing market.
Modernize your process
At the end of the day, the easiest way to improve a sales forecast is by letting a machine do it. AI-driven tools support the work of sellers and other teams with fast, accurate, easy-to-understand insights. Collective[i]’s Intelligent ForecastTM lets companies and their teams react quickly to changes so they can stay ahead of the curve.
If you’re ready to find out how Collective[i]’s tools can modernize your sales forecasting process and eliminate the need for time-consuming Forecast Fridays, reach out to us today.