When it comes to financial modeling, there’s more than one way to go about achieving results. Depending on factors such as the company’s industry and business model, there are a variety of models that can be used to project revenue and sales growth.
To help you choose which approach is best suited for your company, let’s take a detailed look at 9 of the most commonly-used revenue forecasting models.
Top-down forecasting uses factors such as your company’s historical data and market conditions to predict future growth rates. With top-down forecasting, companies start by estimating the total size of their market. They then calculate their potential share of that market to estimate future revenue.
Top-down forecasting requires components such as market size analysis, industry growth rates, and market share calculations. This revenue forecasting model is best suited for large enterprises with a significant market presence, well-defined market data, and a broad customer base.
Bottom-up forecasting entails a granular approach to revenue projections. With bottom-up forecasting, a company will analyze the historical performance of individual products or services in order to forecast revenue on a product-by-product and service-by-service basis. Usually, this means working closely with your sales teams and performing a detailed analysis of your sales pipeline in order to gather the necessary data.
Once you have calculated revenue projections for each individual project or service that your company offers, these sums can then be added together to calculate a total revenue projection for your company.
Bottom-up forecasting entails processes such as unit sales projections, average price per unit calculations, and product-level forecasts. It is best suited for companies with diverse product or service offerings, startups launching new products, and businesses with accurate sales data.
Backlog forecasting focuses on orders or commitments from existing customers that have yet to be fulfilled. This revenue forecasting model is only useful for companies with long sales cycles or companies with a backlog of orders. However, if you know how many customers have placed orders or commitments with your company over a specific time period, estimating your company’s future revenue over that same period is a relatively straightforward process.
Backlog forecasting requires companies to analyze open orders, contract values, and anticipated delivery schedules. It is a revenue forecasting model that is well-suited for manufacturing or service companies with a backlog of open orders as well as any other company with a long sales cycle.
Pipeline forecasting is a revenue forecasting method that allows companies to estimate future revenue based on a detailed analysis of their sales pipelines. By tracking active opportunities and estimating the revenue potential of deals at various stages of the sales process, companies can predict how many sales they are likely to close over a period of time and thus predict their revenue over that period.
Forecasting accuracy is one key benefit of pipeline forecasting; when you are able to develop an in-depth understanding of your company’s sales pipeline, making accurate revenue projections becomes much easier.
Of course, performing this type of detailed analysis isn’t always a quick and simple process itself, and you will need to work closely with your sales team to gather data on sales opportunities, sales cycle stages, and the probability of closing deals.
Pipeline forecasting is best suited for sales-driven organizations, businesses with complex sales cycles, and those aiming to optimize sales strategies.
Historical performance forecasting
As its name suggests, historical performance forecasting is a model that uses a company’s historical data and past performance data to estimate future revenue. By analyzing data points and metrics such as past growth rates, recurring revenue, and customer retention rates, companies can accurately predict what their future revenue will be.
Along with analyzing past sales data, historical performance forecasting also requires companies to consider internal and external factors that may influence future revenue. This includes factors such as seasonality, market conditions, and competition.
Historical performance forecasting entails considerations such as historical revenue data, growth rate analysis, and seasonal adjustments. This revenue forecasting model is best suited for companies with consistent revenue growth patterns and industries with predictable market cycles.
Moving average forecasting
Historical data is a great place to turn to if your company is looking to estimate revenue growth. However, these data points often aren’t granular or focused enough to provide the kind of accurate projections that companies need.
Moving average forecasting is a revenue model that seeks to smooth out fluctuations in historical data by breaking time periods up into smaller chunks or time series and calculating the average revenue for each one. For example, a company might choose to analyze its historical sales data on a month-by-month basis, looking closely at each one before moving on to the next month in order to gain a more granular understanding of its past performance. This allows for companies to better account for seasonal changes and the ebbs and flows of their sales cycles.
Moving average forecasting is best suited for businesses with volatile revenue patterns and irregular fluctuations in demand.
Straight-line forecasting assumes a linear growth or decline in revenue over a set time series. It provides a simplified approach to projecting revenue by assuming constant rates of change.
Straight-line forecasting involves analyzing data points from historical data to determine the company’s revenue growth trends. However, rather than also accounting for factors such as changes to market conditions and seasonality (like historical performance forecasting requires), straight-line forecasting assumes that past trends will continue at a linear rate.
This method is not as accurate at predicting future revenue as many other revenue forecasting models, but it is beneficial for companies that want to perform a quick and simple forecast. Straight-line forecasting is also best suited for companies with steady, predictable growth patterns and industries that are not rapidly evolving.
Simple linear regression forecasting
Simple linear regression forecasting is more of an analysis technique than it is a revenue forecasting model. However, it can still be used to unearth valuable insights regarding your company’s revenue trends.
Simple linear regression forecasting involves looking at your historical data to analyze the relationship between two data points. Sales and profitability, for example, are two variables that companies often compare using simple linear regression forecasting.
This analysis is used to shed light on key revenue trends and provide the data your company needs to enable more accurate predictions from its other revenue forecasting models. Simple linear regression forecasting is a great method for any company that wants to better understand the relationship between various variables that impact revenue and situations where historical patterns may not be linear.
Multiple linear regression forecasting
Multiple linear regression forecasting takes the simple linear regression model a step further by incorporating multiple independent variables. It considers the impact of several factors on revenue, allowing for a more comprehensive analysis.
Like its two-variable counterpart, multiple linear regression forecasting is able to provide companies with the insights they need to create better revenue forecasting models.