If you’re reading this right now, chances are it’s because you’re not sure what sales forecasting method is best for you and your organization. And you have a goal to get it right, whether it’s for short-term growth to decide budget plans or if you’re gearing up for an IPO.
You’re not alone. According to CSO Insights, 60% of forecasted deals never close. Their data also shows that 25% of sales managers are unhappy with their sales forecast accuracy.
That’s why we’re diving deep into the 12 different sales forecasting methods and the pros and cons of each.
We broke down the top methods to forecast sales into forecasting processes, techniques and tooling:
- Total Revenue
- ARR vs. One-Time
- Commercial vs. Enterprise vs. Strategic
- By Product Line
- Using spreadsheets for sales forecasting
- Using CRM reports for sales forecasting
- Using a DIY toolset for sales forecasting
- Using a revenue operations platform for sales forecasting
Let’s get to it!
Business Process Sales Forecasting Methods
Business process sales forecasting starts with the sales goals of the company to develop your sales forecast. While every team should track these critical sales forecasting metrics, the number you forecast by is dependent on each company or team’s individual goals.
Sales Forecasting by Total Revenue
Both small and large companies forecast based on a single revenue number. For SaaS businesses, it’s most likely ARR or annual recurring revenue, which is basically the amount of revenue a company can expect to receive from a customer in one year. ARR has become a new metric of success due to the emergence of the subscription model in sales. If the goal for your team is to drive revenue growth, then your team should be focused on that number. If your business incorporates a subscription business model, the total revenue number would include net new ARR, renewals, cross sell and upsell.
In addition, even if you sell in a subscription business model, you might still generate revenue from one-time services, such as implementation or proof of concepts. When this becomes and important part of the business, it can be useful to track and forecast against those one-time activities.
Sales Forecasting by Business Segment
If the goal is to grow your customer footprint in a specific segment, then you may consider forecasting by the number of new logos (or new customer contracts) in that segment you’re able to acquire in a time period.
For example, if you have established yourself with commercial or mid-market customers but want to move upmarket into enterprise accounts, you may create a forecast to track both of those segments. In the next quarter, you might forecast onboarding 60 new commercial customers, but just 4 enterprise accounts since it’s new territory for you and your sales team.
Reasons companies may use this sales forecast method:
- Maybe you’re a smaller company focused on showing the market how many customers you have to provide validation to prospects. The more logos you have, the better.
- If your strategy is to land and expand, you may care less about initial contract value and more about getting into as many accounts as possible so you can upsell or cross sell from the inside
Sales Forecasting by Product
Sales forecasting by product is usually reserved for more mature businesses. If a company has multiple product lines that generate varying levels of revenue, it may be wise to choose a forecast method that allows you to account for each product or product line individually. This would allow you to anticipate the future sales of a certain product or product line to not only determine the health of that product separate from the rest of the business, but also plan for re-investment into the development of new product based on the sales forecast.
Sales Forecasting Methods in Practice
As we’ve mentioned in previous blogs, accurate sales forecasting is reliant people, process and technology. The techniques in this section are all about the people on your revenue operations team that are looking at data and calling their number. These sales forecasting techniques incorporate a variety of methods, each with pros and cons. Ultimately, it will depend on your business and sales process to choose the one right for you.
Top-Down Sales Forecasting
Top down sales forecasting starts by identifying your total addressable market or TAM for each business segment. It takes a higher-level approach to viewing your business. According to the Corporate Finance Institute, your TAM is developed by researching market valuations from reputable sources, such as Gartner. You then estimate how much market share you’ll be able to capture and the revenue you’ll be able to acquire. Calculating revenue is done by multiplying the TAM by the percentage of market share you think you’ll be able to achieve.
So let’s say you’re in the event software space looking to expand into Europe and your research shows that the TAM for European event software is valued at $500,000. You think you can capture 2% of that based on quantitative or qualitative analysis of historical deals. Your top down sales forecast would $500,000 X 2% = $10,000.
The benefits of top down sales forecasting is that your forecast is based on real-world market value, but can be tricky because it doesn’t take into account actual opportunities in play. The leadership team at the top can estimate their forecast based on market, historical data and past performance, but without really knowing which deals are happening in real time and contributing to the forecast, a top down method won’t be grounded in the real deal data. Top down models are limited by the simple fact that they’re just that: models.
Bottom-up Sales Forecasting
On the opposite spectrum is bottom-up sales forecasting, which starts with the reps instead of the managers and actual opportunities in play instead of models. In this sales forecast method, every rep calls their number based on the opportunities they have in pipeline. Managers work with reps to inspect pipeline, understand projections and look through sales activity data in order to justify the forecast.
The benefits of a bottoms up sales forecast is that it’s based on real-world opportunities happening in real-time. Reps and managers who are in tune with each of their deals can provide a more accurate sales forecast based on the opportunities in play. The challenge with forecasting sales with the bottoms up method is that reps and managers must have clear visibility into the health of their deals, including sales activity and customer engagement data the help signal how deals are progressing. Luckily, there are tools that help automatically capture sales activity data and provide AI insights into the likelihood your deal will close based on historical data.
Qualitative Sales Forecasting
Qualitative sales forecasting involves the estimation of sales performance based on long-time expertise or well-versed industry knowledge. According to AccountingTools, qualitative sales forecasting “...relies upon the knowledge of highly experienced employees and consultants to provide insights into future outcomes.”
Organizations typically use qualitative sales forecasting when entering into a new market or if historical data isn’t available. This type of sales forecasting works best when you have a hunch that the market won’t perform as it typically would. For example, if hurricane season is predicted to be much worse next year in a disaster-prone area and you’re in the insurance industry, you might anticipate an unusually higher demand which would be a departure from the performance of previous sales periods. While we talk about qualitative sales coming from well-versed industry knowledge, often times, it’s an educated guess that comes from gut feelings and is ultimately based on sales execution. That means it’s often based on observations rather than real data.
A con of qualitative sales forecasting is that it can be risky and require a lot of resources to build these models. You’re relying on insufficient data, with no guarantee that your sales forecast will be positively accurate. Companies that use qualitative sales forecasting methods need to be cognizant of leaning too much towards making a decision based on models and instincts rather than reliable insights into what is actually happening or will happen based on data.
Quantitative Sales Forecasting
Quantitative forecasting model uses historic sales data to calculate accurate forecasts. It’s based on past performance and can be done in two ways (Chegg). The first method is a time-series model which looks for patterns in the data to build the forecast and predict where you’ll land based on current sales pipeline coverage. The second method is called the associative model and it uses assumptions to build a linear regression-based forecast. The linear regression shows where you’ll end up based on those assumptions.
Organizations choose to use quantitative forecasting because it’s the first step in being data-driven. However, a major drawback from using this method is that it is highly dependent on an accurate data set, analysis of historic performance and complete visibility into pipeline. With these insights in hand, it’s possible for organizations to land within 2% of their forecast, just weeks into the quarter.
Tools to Implement Sales Forecasting Methods
Once you’ve identified your sales forecasting method and process, you’ll need some tools to help you manage, monitor and execute on your sales forecast.
Using Spreadsheets for Sales Forecasting
Organizations typically use spreadsheets for sales forecasting when they are smaller or don’t have enough resources to purchase a more sophisticated platform. Using spreadsheets is easy to share with the team; however, the spreadsheets become obsolete the moment data gets manually added in. Without an automation tool to automatically add this data in real time, the forecasting approach is never truly accurate.
Using CRM for Sales Forecasting
Using your CRM for sales forecasting seems like a pretty logical plan. I mean it’s serving as your system of record to track and manage every opportunity, so it should be easy to develop a forecast, right? Organizations typically use CRM reports for sales forecasting when manual spreadsheets become too laborious to upkeep and teams need a tool to aggregate those numbers.
One drawback to using your CRM for forecasting is that a database is only as good as the data that gets put into it and, once again, your forecast accuracy is tied to the diligence of your sales team. Are they updating their fields accurately and in real-time? Are they capturing all of the opportunity activity in CRM?
In addition, CRM doesn’t:
- Track history, which means they don’t have enough data to “teach” machine learning algorithms for AI insights.
- They don’t process deal signals coming from other systems in your stack like Marketo, Outreach or Gong.
- They depend on relational databases that don’t allow you to adapt very easily when you launch a new product line or growth initiative.
Using DIY combination of CRM/Spreadsheets/Business Intelligence Tool for Sales Forecasting
As the company grows, the forecast becomes more complex and sales forecast accuracy becomes more important, companies realize CRM is essential for doing business, but it’s not enough.
Organizations typically turn to a DIY combination for sales forecasting that gives them more control over building the forecast, but still requires manual upkeep and a reliance on accurate data input by reps. Plus, it might also take a long time to develop this approach so that it’s customized to meet your business needs.
Unfortunately, this disconnected arrangement of spreadsheets and reports from business intelligence systems is not scalable and impossible to standardize, requiring heavy lifting from sales ops to validate, standardize and consolidate the myriad reports into a single document.
Using a Revenue Operations Platform for Sales Forecasting
With the emergence of AI and automation, new tools are available that make sales forecastingmore connected, efficient and predictable. Revenue operations platforms bring a new way to generate revenue with the same level of transparency and rigor that companies expect from any other mission-critical business process (ERP, supply chain, etc…).
This can include:
- Trustworthy data and automation. Automated data capture and history snapshotting across your rep and buyer activity signals.
- Deeper revenue insights. AI-powered visibility into all engagement and activity data to show you where you have sales pipeline risk and opportunity to help you allocate resources.
- Consistent execution. AI-dirven automations and user experiences that make all members of your revenue team more productive and efficient, from sales to marketing to customer success.
- Predictable forecasting. Ability to accurately predict revenue results for any segment of your business, including net new logo and pipeline creation, upsell, cross-sell, renewals and churn.
A revenue operations platform is the engine to a powerful sales forecasting machine that relies on accurate and up-to-date data and automation to give our entire revenue team the tools to not only predict where they will land, but execute to make their number. (Check out this 4-part video series from our VP of Sales Anthony Cessario on how he uses Clari’s Revenue Operations platform to come within 2% of his Week 1 forecast.)
How do you decide which sales forecast method is right for you?
You might find that you try out several different sales forecast methods before settling on one. Or you may find that you start out with one, but soon outgrow it. The important thing to remember here is that you have to look at where your business is today and where you want it to go.
And, unless you’re using a revenue operations platform like Clari, the reality is that all of these methods typically require manual data collection and consistent, cooperative effort from the sales reps, as well as very accurate data that's inputted manually — which rarely happens in the real world.
Let Clari take the guessing game out of sales forecasting and set you up for success using real data-driven techniques.
Start developing forecasting as a real-time function that guides your business, instead of scrambling to consolidate spreadsheets, trying to make sense of disjointed reports and wasting time with manual data input.
Clari adds a whole new layer to sales management beyond what your CRM can provide. By leveraging the automated activity capture, as well as the AI-powered insights that let customers improve team productivity, you’ll be able to drive and convert more pipeline, forecast the business and reduce churn. These types of insights are impossible with manual sales forecasting methods.
Interested in learning more? Request a demo.