Business leaders rely on forecasting to make decisions on the direction of their organization. Yet, with so many forecast methods, it’s hard to know the right approach. Bottom-up forecasting allows you to get a clear picture of projected revenue by breaking down the underlying components that ultimately drive revenue generation, profits, and growth.
Here, we’ll take a deep dive into bottom-up forecasting, what it is, how it differs from top-down forecasting, and how companies can use it within their own revenue organization.
What is bottom-up forecasting?
At a high level, bottom-up forecasting is a projection of micro-level inputs to assess revenue for a given year or set of years. For example, revenue teams often use this method to estimate the business's future performance based on individual sales or rep performance.
Put another way, bottom-up forecasting is like looking at the health of a complex system, like a vehicle, by looking first at its most basic parts, like its engine components.
Why bottom-up forecasting is important
If the bottom-up forecasting process is like analyzing the health of a car, then the ultimate output (i.e., the sales forecast) is like a roadmap for the business. It helps inform companies on how to navigate their path forward and adjust if they start to veer off track.
When forecast predictions are off in either direction, it’s like using a roadmap without clear road signs. Too high, teams suffer a miss in expectations with a downstream effect. Not only do company leaders lose faith over time in a team’s ability to meet targets, but it also puts the company in a precarious position with hitting cash flow and profitability targets. Not to mention, teams that repeatedly miss forecast targets suffer productivity losses and are more likely to start searching for jobs elsewhere. Forecast predictions that are too low lead to surprises that also hurt credibility and make it difficult for business leaders to plan predictably.
TLDR; sales forecasting is essential to get right.
How revenue teams use bottom-up forecasting
Let's take a closer look at how teams put this forecasting method into practice. For example, if we want to create a sales forecast template, we'll typically begin by defining the number of orders expected from each business channel. If we wanted to go deeper, we could even start further down with advertising conversion rates or productivity metrics within a specific team.
Next, we estimate how much will be charged for those sales and what the business nets from those sales. Once we identify the value of low-level transactions after refunds, exchanges, returns, charge-backs, production costs, and other pertinent factors, we have the metric that we can use to estimate revenue in broader terms.
Another example would be to take the performance of an average sales rep. If the entire company is performing at the rate of this rep, we can extrapolate what revenue would look like across the business.
Bottom-up forecasting formula
A simplified approach to bottom-up forecasting formula follows a simple calculation:
Estimate of goods/services expected to sell x average price = total sales
In lay terms, you estimate how much of each good and service you expect to sell and multiply that by the average price. Of course, you must subtract all costs to get a true picture of profit or loss for a specific period.
Ultimately, the bottom-up forecasting formula is a way of calculating potential revenue for a specific period (i.e., a sales cycle, quarter, etc.). That said, different companies take different approaches.
Bottom-up forecasting example
Every organization is unique and requires the right inputs for an accurate forecast. Companies just setting out may use the simplified bottom-up forecast formula approach as a starting point. However, enterprise businesses will likely take a more intricate approach that factors in market complexities.
Of course, it also depends on the type of business and industry. Take an eCommerce company as an example. They must look at all their sales channels to analyze the number of expected orders coming from each. With products or services at different price points, they’ll need to determine the average cost, considering things like discounts or promotions. They can then arrive at a number for total sales. However, they may add other variables into their model, like returns, refunds, and exchanges.
Another example is SaaS business models, where subscription services are common. Here, companies will still consider sales channels but look at variables like the number of active subscriptions, churn rate, and pipeline coverage to forecast revenue.
Bottom-up vs. top-down analysis
The key difference between the top-down and bottom-up approaches is the perspective taken to perform your analysis. Bottom-up forecasting is ideal for estimating how specific performance metrics impact revenue. But to understand the true health of a complex business, we should look at it in more than one way.
In a top-down analysis, we estimate demand at an aggregate level. This type of assessment weighs historical outcomes to predict future performance.
If we're considering purchasing a company's stock, for example, the information we're using will be the product of a top-down analysis. In this case, we're looking at the business as a unit. So, we'll look at total revenue and stock performance over a given time.
Top-down methods are helpful when reporting to groups like agencies, investors, partners, and other external stakeholders. In short, a top-down analysis is relevant when looking at the company from an outside perspective.
There are some similarities, however. When working from a top-down perspective, we create a system-wide analysis that gives us a holistic representation of total performance. Bottom-up forecasting does this, too, but it relies on the health and functionality of the organization's specific internal components, which are then extrapolated to the aggregate level.
In other words, a top-down approach looks at the business as a complete unit, whereas a bottom-up helps assess individual parts for optimization. That said, forecasting is imprecise by nature. Whether we look at a company from a bottom-up or top-down perspective, we're bound to tap into some critical inputs while missing out on others.
Top-down vs. bottom-up sales forecasting: What's better?
If we think of a company as an automobile, we can compare the top-down approach to looking at the car from the outside. Likewise, the bottom-up approach would be like inspecting the vehicle's internal components. From the outside, we would look at the condition of the exterior, the speed, performance, and other aggregate factors. By looking under the hood, we can diagnose specific problems and assess the value of certain systems.
Similarly, a bottom-up approach helps leaders examine various aspects of their organization compared to their competitors. However, a top-down approach becomes critical as a business scales, especially if you can leverage consumer data and buying trends accurately.
Neither approach is solely right or wrong, and both are key components of revenue intelligence software, which connects, analyzes, and actively monitors every data point across your revenue team so sales leaders can examine the health of their pipeline — from both a high-level and individual deal-level view.
Top-down Forecasting Pros and Cons
- Fast and straightforward: Unlike bottom-up forecasting, a top-down approach offers a simple analysis of the organization viewed as a single unit. Here, our results will be systemic in nature.
- Lack of detail: With a top-down approach, we lose a level of detail. While top-down is fast and broad, we miss the context that a bottom-up view can offer.
Bottom-up forecasting pros and cons
- Team engagement: By analyzing teams' impact and extrapolating them upward, we can decide whether or not a given individual's experience is good, bad, or neutral, then provide support to encourage or prevent it.
- Flexibility: Using the bottom-up approach makes it easier for teams to see where they fall short, so they can quickly pivot and fix problems.
- Limited visibility: What we see and know about our immediate circumstances may not paint an accurate picture of reality. If our forecasts only index on a select group of metrics, we miss other contributing factors that could just as easily derail our projections.
- Potential conflict: Any time we look at and consider the functions and perspectives of individual units, we run the risk of creating or exacerbating conflicts.
Commit your forecast with confidence
The traditional approach to sales forecasting is filled with gaps, particularly for teams who use disparate systems and processes to manage the revenue cycle. Without a consolidated view of pipeline health and buyer insights, revenue leaders must guess their forecast, so they are perpetually at risk of surprise outcomes. They have dozens of dashboards, but they’re not sure they can trust the data. Instead, they are forced to rely on the gut intuitions of their whole team to inform their forecasting models.
But with a single, unified platform for support, forecasting can shift from a critical gap to a seamless, highly-valuable component of your business. delivers real-time pipeline data and buyer engagement signals to bring science to the art of forecasting, enabling revenue leaders to go from guessing the future to changing it with recommended actions.
More forecasting resources for sales and RevOps teams
Today’s shifting economy means revenue leaders must do more with fewer resources.
So, how do you deliver on lofty revenue targets while also reducing costs?
It starts with more efficient forecasting processes. Instead of spending anxious hours on manual forecasts, modern revenue leaders are embracing ways to save time and refocus their energy on growing revenue. For Outreach's top resources on forecasting efficiency, download the free content bundle: Your Road to Forecasting Efficiency.
Commit your forecast with confidence
The traditional approach to sales forecasting is filled with gaps, particularly for teams that use disparate systems and processes to manage the revenue cycle. Without a consolidated view of pipeline health and buyer insights, revenue leaders must guess their forecast, so they are perpetually at risk of surprise outcomes. They have dozens of dashboards, but they’re unsure they can trust the data. Instead, they are forced to rely on the gut intuitions of their whole team to inform their forecasting models.
But with a single, unified platform for support, forecasting can shift from a critical gap to a seamless, highly valuable component of your business. Outreach's forecasting delivers real-time pipeline data and buyer engagement signals to bring science to the art of forecasting, enabling revenue leaders to go from guessing the future to changing it with recommended actions.
