Sales forecasting methods
Modern sales teams need a go-to method for sales forecasting that can keep up with the demands of today’s buyers. In this quick resource, we’ll take a look at the traditional methods of forecasting—and the AI-enabled sales forecasting methods that successful sales teams are using to take control of their process.
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Sales forecasting methods
Traditional sales forecasting methods rely on a number of factors to determine a company’s future revenue. What are the methods of sales forecasting? Historically, they’ve fallen into two groups: qualitative and quantitative. The factors at play in qualitative forecasting include historical data of sales transactions and applicable market research data. Quantitative methods include a seller’s instincts about consumer behaviors to determine the likelihood of a successful sale.
But there is one more important method that has proven its worth over time –– AI-enabled forecasting. This type of forecasting brings in the power of both quantitative and qualitative methodologies while boosting the relevancy of each with neural networks and machine learning.
At Collective[i], we believe that AI-enabled forecasting is the way of the future. First, let’s take a look at old-school sales forecasting techniques including qualitative and quantitative forecasting methods, then we’ll explore how AI-enabled forecasting is empowering modern sales teams to transform their approach to selling with exceptional results.
Qualitative forecasting methods: The human heart of sales.
Can any one person predict the future? No. But they can give informed perspective on things like an existing customer’s buying tendencies or upcoming product updates that can create new sales opportunities. Qualitative forecasting methods provide sales teams with insights into future market conditions that are based not on hard data, but on these more subjective instincts and opinions.
When a salesperson gets off the phone with an excited prospect and just knows that deal is going to close sometime next week, that’s a qualitative forecasting method. When an industry expert knows about disruptive new technologies coming from a competitor that could change how customers think, that’s a qualitative forecasting method.
So, what are examples of qualitative forecasting methods? Here are some of the most common, along with some real-world examples of what they look like in practice.
Jury of executives
One reliable qualitative method involves interviewing a jury of executives representing different elements of an organization. By gaining insights from these internal experts about things like supply, demand, hiring goals, and cash flow, sales teams can add valuable perspectives about what they’ll be selling and to who in the coming months. A popular approach to this is called the Delphi Method. Experts in the company provide their answers to the questionnaires, but their answers can be adjusted based on the several group results that come from the questionnaires. Multiple rounds can commence before the group reaches what they believe is the best possible decision or opinion about a given topic
Real-world example: A sales team wants to learn about new products their company might be launching in the near future to prepare. By utilizing the Delphi Method with leaders in their research and development division, they learn not just about what will be coming, but how company leadership views those developments will impact the market.
Every seller has a unique perspective on their market. They have ample experience selling to prospects and can provide insights into common objections or the shared qualities of customers who purchase most quickly. Individual sellers can leverage this perspective to frame their own expectations about how many deals they may close in a given period of time. More formally, some sales organizations make regular group meetings an important part of forecasting.
Real-World Example: A sales team has a standing monthly meeting where each seller shares highlights from their work in the past month. People will share which deals they’re working on that are most likely to close, which they think won’t be moving forward anytime soon, and what they’ve learned from deals that ended up going nowhere.
The same way sellers communicate with internal experts on what might be coming down the pike, they can tap industry experts to provide insights they might not have on their own. These experts can come from companies that represent prospects or might be academics conducting research on a specific industry.
Real-world example: As the COVID-19 pandemic has impacted the way a company’s customers tend to purchase, the sales team decides to speak with a journalist who has recently published an article on evolving consumer habits. Any insights they glean can help them change the way they sell — and help them shift their own expectations about things like sales cycle lengths.
With every advantage, however, comes a disadvantage. The biggest downside to qualitative forecasting is trusting too much in potentially biased opinions. Even the most informed opinion will come with a certain set of blinders on it; sellers may know how their best customers have behaved in the past, but they can’t possibly know what might be happening at a company that can change that behavior moving forward. While each of the above forecasting process steps can provide invaluable insights, they can’t paint a complete picture on their own.
Quantitative forecasting methods: What do the numbers say?
In finance, there’s an adage so fundamental that lawyers often won’t let an ad go out without featuring it as a disclaimer: Past performance is not indicative of future results. While an investment advisor can showcase the success they’ve had growing client income in the past, there’s no amount of historical data that can accurately predict the future.
That said, statistical forecasting methods have long played a central role in sales forecasting. Quantitative forecasting methods complement qualitative methods by collecting, analyzing, and interpreting historical data. That data might include a company’s past sales figures or broader, macroeconomic information that speaks to the market as a whole. And there’s value to be found inside those numbers. When a sales team can look at a decade of sales data to see that their customers tend to purchase more in the second and third quarter than the first and fourth, it’s reasonable to expect the same moving forward. That insight helps the entire business prepare to weather winter doldrums and ramp up for increased sales come the spring and summer.
Let’s take a look at some examples of quantitative forecasting methods commonly utilized by sales teams seeking to learn from the past.
The most basic qualitative forecasting method focuses on past sales data to help predict the future. A good example of this is the time series forecasting method, which aims to predict what sales will look like in a given future period based on data from a similar, previous period.
Real-world example: In preparing a Q4 forecast for their manufacturing company, a sales team collects figures from Q4 of last year.
When a company has a new service or product to launch, they may start with a small, defined test market. By selling to one geographical region or type of customer, they can begin to collect sales data about how that market reacted: How much did customers buy? What was the sales cycle like? What kinds of customers purchased the most? Using that small set of data, they can forecast what a broader rollout might look like.
Real-world example: A smart thermostat manufacturer has developed a new kind of voice-activated device at a much higher price point than their other offerings. They create a test market of retailers in one city to gauge both B2B and B2C demand before setting goals for a national product launch.
One of the best ways to learn how much a customer might purchase in the future is to go straight to the source. Sellers sometimes ask existing customers about their future intent to purchase, then base forecasts on what they learn.
Real-world example: A seller has begun a good relationship with a new customer, who was satisfied with their first purchase order. To better predict the cadence and size of future orders, the rep asks their contact to complete a survey about their future plans.
A company’s own historical sales data can’t factor in future movements in the market that could impact buyer behavior. To account for this, sales teams will either conduct first-hand market research or rely on third-party reports about things like what their competitors are selling, the impact of political cycles on spending within their industry, or any number of other macroeconomic indicators of shifting demand.
Real-world example: At the start of the COVID-19 pandemic, a company selling office supplies wanted to understand attitudes among their normal customer base about remote work. Getting a better grasp on how customers might be spending dollars that would have been spent upgrading equipment at the office can lead to new ideas for selling for home office purposes.
This narrow approach eschews broader datasets for the buying patterns of individual leads. By comparing historical sales figures for similar clients (or, in the case of repeat buyers, the same lead’s own history), sales teams can assign value scores to leads that indicate their likelihood to close future deals.
Real-World Example: A sales team’s CRM is set up to give “points” to leads based on actions they take like opening an email or clicking a link to watch a webinar. The more points a lead has, the higher their deal becomes on an individual seller’s priority list.
Opportunity stage forecasting
In a slightly more generalized version of lead-driven forecasting, pipeline or opportunity stage forecasting treats each step in the buying process as its own “bucket” of leads. Sellers might know that historically when a lead makes it to the third step in the process, their likelihood of closing doubles compared to leads who are still in the second step. This is a dynamic way of re-evaluating leads as they move along towards a sale to build more predictable forecasting models.
Real-world example: As part of their normal sales process, sellers follow up after an initial phone meeting with a request for a Zoom call where they can share a deck and dive deeper into their product’s value propositions. Only 20% of leads will move forward with that second meeting, but 40% of leads who make it to that second Zoom meeting end up closing. By factoring these close rates into their forecasting models, sellers can get a high-level prediction of what percentage of their current leads will close depending on which stage of the buying process they are currently at.
No matter how good historical data is, it still cannot predict what will happen tomorrow. No matter how much research we do as consumers and sales professionals, we run the risk of losing our predictions to those things that are unforeseeable. That’s why modern sales teams are relying on models that include traditional sales forecasting methods, qualitative and quantitative alike, amped up by the power of artificial intelligence.
AI-enabled forecasting methods: The comprehensive approach.
All of the expert opinions and historical sales data in the world isn’t worth much if sales teams can’t be confident that they’re making informed decisions about what to do next. A new kind of sales forecasting method makes accurate data collection and maintenance a foregone conclusion: AI-enabled sales forecasting.
Because what happens if someone forgets to update the CRM when an old contact leaves a company and emails disappear into the void? Or when a seller is positive a lead that’s dragging their feet is mere days away from buying, but that company has actually decided to stop purchasing any new services this quarter? That seller’s contact might not even know that this is what’s preventing a contract from being signed by the higher-ups, which means a lot of time can be wasted on deals that simply won’t move forward. In turn, any forecast that relied on that seller’s opinion is suddenly going to have a shortfall.
AI-enabled forecasting methods avoid these situations altogether by collecting and analyzing data from an entire network of sales organizations. These methods automate what can be automated, they spot opportunities or challenges a seller might not be able to see otherwise and provide daily recommendations for next best steps. The result is a sales forecasting approach that saves time and increases revenue instead of forcing sales teams to remain accountable to arbitrary goals based on inaccurate data.
Here’s how it works.
This forecasting method uses machine learning to eliminate the tedious tasks associated with quantitative data. With machine learning at the forefront, AI-empowered forecasting tools automate activity reporting, workflows, attribution, and contact capture.
Real-world example: A sales team upgrades their CRM with an AI-enabled forecasting tool that keeps all customer data up to date. With the time saved collecting data from marketing sources and updating attribution reports, they can spend more time interacting with leads and moving deals towards completion.
AI-enabled forecasting greatly enhances the ability to capture historical data and allows for daily automated forecasting and buyer-seller recommendations. By letting machine learning take control of the data, sellers can provide daily updates to leadership instead of the monthly or quarterly forecasts that caused friction in the past.
Real-world example: After switching to an AI-enabled forecasting tool, a seller realizes that a deal they thought was going to close soon might be farther away than anticipated. They decide to prioritize another deal based on recommendations from their forecasting tool and close that deal more quickly than they would have if they’d continued chasing the other deal with no results.
One of the most impactful shifts made possible by AI-empowered forecasting is networked intelligence. Using neural networks, modern forecasting tools like Collective[i] can factor in not just one company’s historical sales data, but a growing set of data from an entire network of sellers. Without sharing specific data points between sales teams, AI tools can analyze things like a lead’s buying activity from many vendors. That, in turn, offers a more holistic set of quantitative data — with machine learning to help interpret that data in real time.
Real-world example: A seller has been investing significant time in closing a deal from a brand-new prospect. While they have been having good conversations with a contact who is eager to get a contract signed, the deal seems to be stalled at higher levels of the company. Thanks to a new AI forecasting tool with networked intelligence, the seller is able to get an introduction to the right decision maker — someone their contact didn’t even know — and get buy-in to close the deal.
Adaptive forecasting methods like this aren’t the future — they’re the present. The past five years have seen major leaps in automation, leading to practical applications like machine learning and neural networks to interpret qualitative and quantitative data alike. Those tools help sellers stay nimble and react to daily changes in the market without the guesswork and manual labor that comes with traditional sales forecasting methods.
Embrace modern sales forecasting methods with Collective[i].
Modern sales teams embrace technology to remove uncertainty and wasted time from both qualitative and quantitative forecasting methods. With Collective[i]’s powerful AI-empowered sales forecasting tech, sellers spend less time guessing about future sales and more time on actions that drive revenue. With daily forecasts based on a growing neural network of real-time sales data, sellers can seize opportunities to build strong connections and exceed sales goals.
Click here to learn more about how Collective[i] can empower your sales team today.