Methods to Conduct Sales Forecasting in Financial Models

 I've specialized in revenue forecasting for many clients. This skill takes a lot of experience to perfect as many different businesses have completely different ways in which revenue is generated. In order to have a usable forecast, here are some of the techniques to use:

All of these financial model templates use unique sales forecasting methodologies depending on the industry.

There are two main schools of thought: 1) Use historical data and simply apply a growth rate. It could be a monthly growth rate or a year-over-year growth rate that is applied to each month (accounting for seasonality). 2) Use bottom up assumptions to derive expected sales. This is more in-depth and involves digging deeper down to how revenue is generated. Think about projecting the count of items sold and the sales price.

Sales forecasting is a crucial aspect of business planning, helping organizations predict future sales and make informed decisions. Here are some common methods used for sales forecasting:

  • Historical Analysis: This method involves analyzing past sales data to predict future sales. It assumes that future trends will follow historical patterns. This method works well for established products or services with a consistent sales history.
  • Trend Analysis: Similar to historical analysis, this method looks at the overall trend of the market, industry, or specific product category to forecast sales. It often involves the use of time series analysis.
  • Econometric Modeling: This method uses statistical techniques and economic theory to model the relationship between sales and various independent variables like GDP, market trends, consumer spending habits, etc.
  • Pipeline Analysis: Common in B2B sales, this method forecasts sales based on the analysis of the sales pipeline. It considers the number of leads, conversion rates at different stages, and average deal sizes to predict future sales.
  • Market Test Method: This involves testing a product in a specific market area to forecast its overall sales. The data gathered from this test market is then extrapolated to predict sales in larger markets.
  • Expert Opinion: This method relies on the insights and predictions of experts in the industry. These could be internal (like sales managers) or external (like market analysts).
  • Customer Intention Survey: Directly gathering information from current or potential customers about their future purchasing plans can also help in forecasting sales.
  • Delphi Method: A structured communication technique where a panel of experts answers questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts' forecasts and reasons. The experts are then encouraged to revise their earlier answers based on the replies of other members of their panel, aiming to converge towards the correct answer.
  • Regression Analysis: This statistical method uses the relationship between one or more independent variables and the sales as the dependent variable to forecast future sales.
  • Machine Learning Techniques: Advanced techniques like machine learning and artificial intelligence are increasingly being used for sales forecasting. These methods can analyze large and complex datasets to identify patterns and predict future sales.

Each method has its strengths and weaknesses, and often a combination of several methods is used for more accurate and reliable forecasting. The choice of method depends on the nature of the business, the availability of data, and the specific market conditions.

The method I use most is setting up base assumptions about customers or products and pricing. This gives a concrete way for anyone looking at the financial model to see what must be true for the numbers to make sense. For example, if you forecast $2,000,000 in revenue for the year, using this forecast method would allow one to see how many items must be sold and if that is feasible given any capacity constraints or likeliness to happen. It is much easier to sanity check something that uses bottom-up assumptions as opposed to high level growth rates of a raw historical sales forecast figure. This is also very good when trying to justify why revenue may be going up.

In SaaS or subscription models, you may be simply improving customer retention rate or increasing contract values as your means of growth. This is all demonstrable with a good financial model template.