Accurate sales forecasting: Moving from intuition to data
Updated on Sept. 5th, 2022
Only about 45% of sales leaders are confident their organizations have accurate sales forecasts, according to a Gartner survey. Yet, accurate sales forecasting can be the difference between maximizing sales opportunities and recording reduced sales.
Of course, no company can predict exactly how much revenue they will make in a month, quarter, or fiscal year. There are simply too many variables to consider. But perfection is not the goal. Accurate sales forecasting is about getting close enough to make good decisions.
So, how do you engage in accurate sales forecasting when there are so many moving parts? The answer lies in your data models.
What is sales forecasting?
Sales forecasting is a systematic process of estimating how much sales a person, team, or company will record within a specific period. Accurate sales forecasting from the sales team provides the rest of the company with insights for making informed decisions about cash flow, resource utilization, business growth, hiring, and financing.
When you make accurate sales forecasts, you are in a better position to improve your sales qualification process, optimize sales engagement activities, and enhance the sales velocity of your deals. In other words, accurate sales forecasting is a crucial component of managing your sales pipeline.
However, it is important to note that a sales forecast is different from a sales target. A sales forecast estimates future sales and potential revenue, while a sales target is simply a business goal that a salesperson, team, or company aims to reach.
Types of sales forecasting methods
Each type of sales forecasting technique has its own unique strengths and weaknesses.
Factors such as the size of your sales time, the length of your sales cycle, how much data you have, and what degree of accuracy you desire as the size of your sales time will determine the right sales forecasting methodology for your team. While sales forecasts aren’t expected to predict exact sales numbers, accurate sales forecasts should give you a frame of reference for your expected figures.
Historical forecasting method
The historical forecasting process is a simple method that uses historical sales data from one accounting period to predict the potential sales volume for that same period in the future. For instance, if you want to make a sales forecast for Q1 2023, you could look back at the actual sales number from Q1 2022 and use it as a basis to make an inference about the coming quarter. Or you could use the percentage of increase or decrease between two periods and use that as an estimate for that same time frame.
The historical forecasting method tends to be relevant in markets and industries with steady sales velocities. The challenge with the historical sales forecasting method is that it doesn’t account for how market dynamics and micro and macro events could change buyer demand. As an example, any historical data from 2019 would not have produced accurate predictions for the pandemic-fueled e-commerce boom in 2020, which in turn wouldn’t lead to accurate predictions for declining e-commerce sales in 2021.
Intuitive forecasting method
The intuitive forecasting method is the path of least resistance when trying to create sales forecasts — because it relies on gut feelings rather than hard data. Using this method, sales leads ask their sales reps to provide an estimate of how many deals they expect to close within the next month. The sales representative might respond with something along the lines of “I’m in advanced communication with five companies; I’m confident that four of them will close in the next three weeks, and the deals will be worth XYZ.”
Intuitive sales forecasts tend to be common with early-stage startups, small businesses, or companies launching new products that don’t have historical data for benchmark sales quotas. While the intuitive sales forecasting method considers the intuition of the people closest to prospects, it places too much emphasis on a salesperson’s expectations. Sales reps’ confidence in their intuition tends to lead to overly generous sales forecasts.
Another challenge is that intuitive sales forecasts aren’t scalable. As your team grows, it becomes increasingly difficult to verify the assumptions of the individual salespeople on your team.
Deal forecasting method
While the intuitive sales forecasting method can feel like a guessing game, the deal forecasting method is based on hard numbers. Deal forecasting is based on deals that are actually at an advanced stage in the sales pipeline and are expected to close based on data from your sales system or CRM. The sales rep’s expectations are still a factor, but the rep uses actual data rather than intuition to make a prediction.
Using the deal forecasting methodology, a rep would identify the deals they expect to close and tally up the sales revenue. That’s it — and at small organizations, it works well. With the help of a CRM, there’s very little admin overhead, and it gets your sales team in the habit of keeping track of future wins.
The problem with this model is that you’ll quickly run into limitations if you want to forecast beyond your average sales cycle. For example, if your sales cycle is 30 days, it’s impossible to forecast the following month as those respective opportunities wouldn’t have been created yet.
Weighted forecasting method
The weighted forecasting method (also called Opportunity Stage Forecasting) uses all the different stages of the sales pipeline to create a predictive model on the possibility of closing the deal.
When he was CRO at Huddle, Neil Ryland found his team’s estimates were off by 40% when they used the deal forecasting methodology. Ryland realized he needed a different model if he was going to forecast the past 30 days.
Using the weighted forecasting method, a deal is assigned a probability for closing based on its pipeline stage. For example, all deals at stage 4 in a 5-stage system may have a 75% chance to close.
Ryland and his sales reps collaborated to build out a clear sales pipeline that included BANT (budget, authority, need, and time frame) and the steps required to close a deal. Then, using historical sales data, he gave each stage a Closed Won probability.
The final model assigned probabilities ranging from 10% at the lead stage to 90% at the procurement stage:
Huddle’s sales managers could weigh each deal according to the pipeline stage it was in and forecast from opportunities created.
This model is perfect as you start scaling up and have reps juggling 8–12 deals a month. You’ll need reps that are disciplined and educated in determining the stage of a deal.
Category forecasting/predictive model
One of the problems with the weighted forecasting model is that it requires a decent amount of time and energy to be effective and doesn’t account for how a representative is feeling about a deal.
A solution to this, at least for Huddle, was adding a field in Salesforce that was required for the representative to express more than just hard data. Reps had to mark whether the deal was:
- Commitment - there is verbal or written intent to sign
- Upside - the deal is likely to close
- Pipeline - no clear understanding of the decision-making process
Combining this with the weighted data was a game-changer: it meant representatives needed to follow a sales process which forced a higher forecast accuracy and included data from the rep working the deal.
Later, Ryland moved on to Peakon and iterated on this model further to weigh leads individually rather than by a probability attached to each pipeline stage. Each lead was assigned a unique close probability based on historical conversion rates of similar leads.
Data points like the opportunity’s age, vertical, and company size meant they were able to better determine if an incoming lead could have a good business match and subsequently a higher chance of closing.
Sales forecast accuracy and the role of data
Accurate sales forecasts require a great deal of data — this is the key to making data-driven sales forecasts rather than forecasts powered by intuition or ego. A survey revealed that a lack of predictive data is a top challenge for 38% of sales executives, and 31% of execs say limitations in technology prohibit accurate sales forecasting.
The more relevant and reliable data you have about your sales process and past sales, the better your chances of making accurate sales forecasts. For data-driven sales forecasts, you’ll need to rely on two broad types of data: endogenous (internal data) and exogenous (external data), to formulate and track the right metrics.
Endogenous data include variables such as product categories, pricing, point of sale data, sales team strengths, marketing data, sales channels, and sales performance. Exogenous data include variables such as seasonality, competition, consumer behavior, current events, and geopolitical or socio-economic trends.
For instance, with the weighted forecasting method, the quality of your data influences the accuracy of your forecast. If the data you have on employees’ personal win rate, lead score, opportunity age, or other factors is complete and accurate, your entire forecast will be more accurate.
Likewise, data can help you spot sales or closing trends based on the job title of the contact, company size, or geographic location. You can then assign unique probability data to each lead based on the historical data you have on the success rate of contacts with similar profiles.
For example, before you assign an X% chance of closing to a new signup from your landing page, your CRM data could help you evaluate the lead source, the country of their IP location, job title, and other details. You can then cross-reference the CRM data of this lead against the CRM data of your current customers to know what percentage of leads with similar profiles you’ve closed.
Remove the guesswork from sales forecasting with accurate data
It’s important to remember that all sales forecasting processes involve educated guesswork. There are too many internal variables and outside factors that can influence your business in ways that are impossible to predict.
The goal is not to create a perfect sales forecasting model but rather to have a way to make predictable, informed decisions in a repeatable manner.
Data is the key to reducing the impact of known unknowns that affect the accuracy of your sales forecasts. When you use data to support your forecasting, you create realistic sales goals, optimize sales operations, and improve your sales strategy.
To learn more about how to implement data-driven prediction models in your business, grab a copy of our free e-book Data-Driven Sales and jump into chapter five today.
