What is Sales Forecasting?
How sales forecasting benefits your organization and tips for getting started
Just as your favorite weather app predicts if you’ll need an umbrella on your commute, sales forecasting attempts to predict an organization’s future revenue generation.
Sales forecasting is a simple concept, yet one of the most important and challenging things to get right. And not just for your sales team, but for your entire organization. The sales department is, after all, the revenue-generating side of your company. Predicting how much money your organization will bring in affects everyone.
But things can get complicated quickly. This is especially true as you start to unravel the complicated puzzle of B2B sales. How do you get started in sales forecasting? What tools do you need to accurately forecast? What goals should you set for your team? What model is best for your organization?
This post will answer those questions and many more. By the end, you’ll be well on your way to accurately predicting future revenue.
Benefits of sales forecasting
Just like any other business practice, you should always start with “why?” It’s important to have a firm grasp on the real value sales forecasting brings your organization, and to be able to communicate that value to your team and to stakeholders.
It may seem obvious, but it’s helpful to be able to clearly articulate these benefits.
Forecasting helps you make more informed business decisions
Knowing how much money your team will bring in trickles down to every business decision you or your organization makes. A surplus of anticipated revenue might mean it’s time to take a risk and break into another market. A dip in revenue may mean certain departments need to go on a hiring freeze.
As a general rule, being surprised by something in business is not good. Especially when that surprise has the potential to affect the livelihood of employees or the viability of your organization.
Another way to think of sales forecasting is the art and science of making your fiscal future a little less uncertain.
Making a business decision “fiscally blind” carries with it immense risk. Sales forecasting, while not a perfect science, greatly helps to mitigate that risk and offer a sense of confidence when making new and potentially risky business decisions.
Forecasting shows you where to allocate resources
As a sales or marketing leader, you have dozens of resources at your disposal, but by far your most valuable resource is your people. A sales forecast offers insight into struggling opportunities, and with that. reps that potentially need coaching, support, or attention.
If a certain deal is languishing and causing a bottleneck in the pipeline, you can reallocate high performers to work with the struggling rep and move the deal along.
This goes for other departments as well. Everyone depends on the projected revenue of the sales team to best understand where to allocate their people and resources.
Forecasting helps you predict accurate budget and growth goals
There are a few companies out there that aren’t keen to grow, but that’s a rare exception. Most organizations, especially those with VC funding or in the tech space, are seeking consistent year-over-year growth.
A sales forecast offers greater insight into growth progress and how well companies are performing against growth goals. All of this is tied into how confidently departments can plan future budgets, which likewise affects strategy.
It’s incredible how dependent each part of an organization is on sales forecasting. With that said, let’s take a look at some commonly used sales forecasting models and how you can implement them today.
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Sales forecasting models
Sales Stage forecasting
Sales Stage forecasting is also sometimes called Opportunity Stage forecasting. It’s simple and very easy to implement. The basic idea is that you determine your revenue forecast by what stage a particular deal is in.
This is best understood with an example.
Let’s say you have the following stages in your sales cycle:
- Closed Won
- Closed Lost
Each of these values would be given a percentage associated with their likelihood to close. Obviously, the more a deal progresses down the funnel, the more likely it is to result in a Closed Won sale. This may look something like this:
- Prospecting (10%)
- Qualified (25%)
- Proposal/Quote (50%)
- Negotiation/Review (80%)
- Closed Won (100%)
- Closed Lost (N/A)
These are arbitrary numbers, but any time you can reference historical data, you’ll have a much more accurate picture of how certain sales stages predict success.
If the deal in question is worth $5,000 in revenue, you would multiply that dollar value by whatever percentage the deal is currently in.
In the prospecting stage, its forecasted value would be $500 ($5,000 x 10%). In the negotiation stage, it’d be worth $4,000 in projected revenue.
Sales Stage forecasting is great if you’re looking for a quick, simple, “back-of-the-napkin” formula for determining a sales forecast. However, its simplicity comes with a cost to accuracy and accounting for complex market factors.
Another problem with this model is that sales reps need to have immaculate pipelines and perfect alignment on their sales stages in order for the forecast to be accurate.
What is the threshold for moving a deal from Prospecting to Qualified? How long does a deal have to be in a certain stage for it to regress due to inactivity? The answers to these questions and more need to be understood and practiced in a consistent way by your sales employees.
Furthermore, this model doesn’t account for stalled deals or factor in the age of deals. If an account is stuck in the Proposal/Quote stage for months, it’s not likely to have the same closing probability as another deal flying through the process at a fraction of the time.
To conclude, here are the pros and cons of the Sales Stage forecasting method:
- It’s very simple to set up and can be calculated in minutes
- It’s dependent upon meticulous pipelines and perfect alignment between reps
- It doesn’t account for market factors or the age of deals in the pipeline
Opportunity Age Forecasting
While one of the downfalls of the previous model is the inability to account for age, this next model focuses on that factor exclusively.
Rather than forecasting a deal based on its pipeline or funnel stage, the Opportunity Age forecasting model forecasts deals based on — you guessed it — their age.
The way that you’d set up an Opportunity Age forecast is to use historical CRM data to calculate your average sales cycle length. In other words, from the moment a prospect enters your CRM to the moment payment is received and the deal is won, how much time elapses on average?
Then, working backward, you assign percentages to time thresholds. These percentages determine each deal’s monetary forecast, similar to the last model.
For example, let’s say you sell a big-ticket item with a relatively long sales cycle, something like six months.
Your Opportunity Age rubric may look something like this:
- One month (10%)
- Two months (20%)
- Three months (40%)
- Four months (60%)
- Five months (80%)
- Six months (95%)
So a $10,000 sale in the pipeline for five months would produce a forecast of $8,000. Like the previous example, this simple model can be set up in minutes with some quick math.
However, as an experienced seller you probably already see the issue: every deal is unique in its needs and closing time. Some deals close faster than others, and this model doesn’t account for those variances at all.
And similar to the Sales Stage forecast, it requires very tight alignment on sales strategy and leaves almost no margin for error.
A benefit of both the Sales Stage and Opportunity Age models is that it doesn’t rely on the intuition of the sales rep, but raw data. However, that data needs to be captured and kept up to date. And if you’re not leveraging sales AI to capture, analyze, and visualize the data, then your forecasts are most likely grounded on bad or incomplete information.
One of the many benefits of Accent’s Marketing Insight platform is that all prospect interactions — whether emails or phone calls — are automatically logged into your CRM. The software then uses AI technology to help predict not only how soon a deal will close, but the recommended steps to get there.
To conclude, here are the pros and cons of the Opportunity Age forecasting method:
- Like the Sales Stage model, it’s simple, cheap, and quick
- It’s dependent upon meticulous pipelines and perfect alignment between reps
- It doesn’t account for market factors or variances in funnel velocity
If you’re fortunate enough to have several years’ worth of historical data, you can set up a forecasting model that doesn’t depend on your sales reps at all.
Historical forecasting looks at your past data (wins, losses, etc.) and identifies trends in success indicators. For years this practice was only feasible if you had an immaculate CRM data set for a few years back. However, with innovations in sales AI technology, teams are now able to sift through their most adopted sales tools (email, phone calls, calendars, CRM) and extract missing buyer/seller communications and activity records. With this capability, more teams are migrating to a historical forecast approach because they are able to harvest a reliable data set to get started.
But while using historical sales data is a great way to account for seasonal market factors, it isn’t very dynamic. It doesn’t consider important variables like individual buyer pacing, new products, new employees, market segments, competitors, or growth goals.
Historical forecasting is often best used in tandem with one of the aforementioned models, as we’ll discuss next, or in conjunction with sales AI technology that can reference a standard model (or ideal customer profile) to account for new products, markets, strategy pivots, or other new variables.
Multivariate Analysis Forecasting
I could go on to describe several other forecasting models based on single factors such as reps’ specific win rate, the likelihood of a particular demographic to close, deal health, and many more.
However, you’ve probably already noticed a consistent issue: any forecasting model that only accounts for one single variable will always fall short. The reality is that B2B sales have several interdependent variables that all weave together and collectively impact the likelihood of a “Closed Won” deal.
The smartest and most accurate sales forecasting model is one that accounts for the following variables in tandem:
- Sales stage
- Opportunity age
- Rep win rate
- Seasonal/historical factors
- Demographic close-rate
- Deal size
These are just the most important variables. I’m sure there are other factors unique to your organization’s needs that are important to track as well.
If you don’t have AI sales software that can collect this data and make these calculations for you, a Weighted Scorecard Matrix is a manual (albeit time-consuming) way to forecast with multiple variables.
The process is as follows: you assign each of the above factors a weight of importance from one to ten.
For example, you might assign the “opportunity age” factor a weight of eight out of ten based on your experience. But other reps may not feel that opportunity age is a very good predictor of success, and may weight it more along the lines of a three or four.
Keep in mind that without some sort of AI-based solution, there’s no guarantee of precision. Much of the weight of these factors are based on intuition and anecdotal data.
You’d then take each opportunity age threshold and assign it with a number from one to ten. Let’s take our previous example and apply this process:
- One month (1)
- Two months (2)
- Three months (4)
- Four months (6)
- Five months (8)
- Six months (10)
Apply this formula to every single factor you’d like to track (sales stage, win rate, etc.) to have your fully weighted scorecard.
I won’t go into the weeds of how to calculate your weighted averages, but you can read up on this process more here if you’re interested. If you choose to go this route, these calculations are best suited for a program like Excel or Google Sheets.
What you need to know is that while it gets the job done, it’s an extremely manual process.
You can clearly see how comprehensive forecasting is a grind without a piece of AI software to assist. Just typing these formulas out is exhausting, and I’m not even the one doing the calculations!
Accent’s Supercharger AI platform makes this process simple. Supercharger offers at-a-glance opportunity scorecards. These are AI-driven visualizations that score the activity, energy, and health of a deal in comparison to your company’s standard model.
For example, if a rep claims that a deal is closing but the communication and energy score are low, then it’s probably not an accurate forecast. This is all reflected natively within your CRM when using Supercharger AI.
Complex forecasting models require complex calculations. Accent Technologies creates the software that does the heavy lifting of these calculations and allows you (and your reps) to get back to what you do best.
More tips for ensuring a sales forecasting success
Prioritize rep alignment
It’s a prerequisite before you implement any sales forecasting process that your sales team be aligned on goals, definitions, and priorities.
When a prospect should or shouldn’t be in a certain sales stage isn’t something that’s best left up to chance or discretion. These sorts of questions need to be clearly defined for your team so there’s no ambiguity in any facet of the sales process.
It’s no secret that sales reps aren’t fans of being “wrangled,” but if you’re looking for an excuse to drive some accountability and alignment into your team, there’s never a better time than before a sales forecasting initiative.
Establish forecasting quotas for individual reps and teams
Every rep should have goals to strive toward, not only for their net revenue but for their forecasts as well. This hearkens back to our previous post about collaboration versus competition.
Collaboration with the team should be encouraged, and competition should be channeled in a healthy way against sales reps’ own previous milestones and KPIs.
Everyone should strive to be better than they were yesterday or last quarter, and forecasting goals adds an extra layer of motivation for reps to see their projected success on a daily basis.
Track and analyze appropriate data
Like I mentioned above, B2B sales are a tight knot of interdependent variables. In order to best understand what factors lead to success, you need to be tracking several factors associated with your deals.
- Deal size
- Rep win rate
- Prospect engagement and deal health
- Pipeline velocity (how fast it’s moving toward a Closed Won status)
- Average deal revenue
- Conversion rates at each sales stage
- Length of time in a given sales stage
Tracking this data is one thing, but interpreting it successfully is another challenge altogether. That’s why it’s essential to have some sort of automated way to collect and synthesize data.
An advanced sales AI platform will make easy harvesting your buyer/seller data, analyzing it, and visualizing it in a simple, easy-to-understand dashboard. Without a machine’s help, accurate forecasting is a tall order, a very burdensome chore, and honestly, somewhat of a gamble.
Automate wherever possible
As a sales leader, it’s imperative that you have accurate sales forecasting information at a moment’s notice. This means not wading through Salesforce lead records and quickly throwing together spreadsheet calculations before an important stakeholder meeting.
In addition to automating your data collection and your sales analytics, sales management AI offers leaders an exclusive dashboard that visualizes the health and activity of all deals in the pipeline on a plot chart. It helps you answer the simple, yet crucial question: “Are all the opportunities I’m projecting to close actually active and progressing?”
Sales forecasting tools
Excel or Google Sheets
This is probably a given to any experienced sales leader, but it’s worth mentioning.
Many of the rudimentary forecasting models we discussed earlier in this post can be accomplished using Google Sheets or Excel. Even if you choose to go with a more robust multivariate forecasting model, some sort of spreadsheet program is essential for manipulating and presenting data in an efficient way.
Another obvious but essential tool is a CRM or Customer Relationship Management software. The most popular CRM by a longshot is Salesforce, but there are other contenders out there like Zendesk Sell, Hubspot CRM, Zoho CRM, and many more.
CRMs often serve as the repository for all crucial sales data, however many don’t include the features necessary to provide intelligent insights on how to move deals forward.
We call our sales platform Supercharger because it supercharges your CRM by giving you and your sales team crucial predictive insights right within your CRM’s environment.
Sales enablement platform
We’ve written extensively on the subject of sales enablement, the importance of a sales enablement team, and how great sales enablement content directly affects the rate at which deals are won.
Our Connect sales enablement platform not only serves as the central repository for your sales enablement content, but also tracks the efficacy of content in moving deals forward.
These insights can be integrated into your CRM to not only help you forecast more intelligently but actually close deals faster.
Marketing automation platforms like Marketo, Pardot, Infusionsoft, and ConvertKit work in tandem with your CRM and your sales enablement platform to reduce the amount of manual work needed in any given sales cycle.
Sales and Sales Management AI
Accent Technologies is the first and only SaaS company to bring together Sales AI and Content Management in a true Revenue Enablement Platform. We provide both sales and marketing with better visibility into the performance of their teams.
This drives revenue through intelligent recommendations for complex sales scenarios and provides the data for rich analytics that power better coaching, forecasting, and long-term customer support. Learn more about our solutions or request a live demo to see it in action.
These platforms can help your sales and marketing team assess the viability of certain prospects even before they enter the “official” sales cycle — at the very top of the lead generation funnel. Marketing automation platforms help teams accomplish complex tasks like lead scoring, automated email marketing and custom-tailored lead nurturing paths.
Sales forecasting is an essential skill to perfect, and every organization has a slightly different way of going about it. However, there are certain commonalities and practices that are tried-and-true to produce accurate sales forecasts.
Implementing the methods mentioned in this post will set you up for optimal sales forecasting success. No matter your organization’s specific forecasting needs, these techniques in combination with a powerful sales enablement platform leveraging AI will give you the insight and confidence you need to take most of the uncertainty out of your future success.