Forecasting Sales and Demand in a Financial Model

Forecasting Sales and Demand in a Financial Model

Sales are the lifeblood of any company, and getting a reasonable estimate of sales revenue scale and growth is highly critical in any ensuring business planning exercise, such as capital investment decisions, hiring of staff, expansion of business operations and allocation of operating budgets, etc.

Hence, forecasting demand for a company’s products and services, and the resulting revenues accrued is probably the most critical step a financial analyst needs to undertake when building a financial model.

In order to arrive at a realistic and reasonable revenue forecast for a business, a good financial analyst should conduct a detailed revenue modeling / demand analysis of a company’s products and services, by examining its usage potential and a customer’s willingness and ability to pay.

A demand analysis would entail determining current demand and using assumptions for demand build up to predict future demand over the time period of the financial model. There are a number of qualitative and quantitative methods that can be used to conduct a demand analysis.

Sales Forecasting Using Qualitative Methods

These methods rely essentially on the qualitative judgment and information of highly experienced practitioners or experts in a specific area, and translate their opinions into quantitative estimates of the revenue model of a specific business, product or service.

  • Jury of Executive Opinion Method: Very popular in practice, this method calls for a group of experienced executives and experts to get together in a structured discussion forum, and in which a moderator would work towards pooling the contrasting views of these executives on expected future sales and demand and combines them into a revenue and demand estimate that they can all agree on.
  • Delphi Method: The Delphi method also relies on the views of a pool of experts, but they do not interact face to face, and the demand forecast and revenue model is constructed through an iterative process. The advantage of this method is that it avoids “group-think”, which may sometimes creep into the Jury of Executive Opinion method, when the pool of executives start agreeing with one another without through independent and objective thinking.

Sales Forecasting Using Time Series Projection Methods

Demand projection methods based on time series generate sales and revenue forecasts on the basis of historical data and trends. The important time series projection methods include:

  • Trend Projection Method: The trend project method involves the direct extrapolating historical sales and revenue trends in the future, primarily those of growth and customer conversion rates. This method works well for stable businesses that have not experienced significant change in their financial profile in the past years, and expect to continue on a similar track going forward.
  • Exponential Smoothing Method: In exponential smoothing, sales and revenue forecasts are modified by examining potential bumps or errors in observed historical demand data trends, to ensure that historical demand rates that are exceptionally high or exceptionally low due to a one-off event are not carried into future revenue projections. This method is useful for discounting the impact of exceptional events (such as a sudden spike in sales due to an unsustainable trend) on the historical sales performance of a business.
  • Moving Average Method: In the moving average method, a simple arithmetic average or a weighted arithmetic average of a reasonable historical sales data window are used to forecast future demand. This method works well for businesses which periodically experience adjustments in their revenue profile or structure, but bounce back to similar historical levels after a certain time period.

Sales Forecasting Using Casual Methods

Even more analytical then either of the qualitative or quantitative methods alone, casual methods take a statistical correlation approach to develop sales and demand forecasts on the basis of cause-effect relationships in an explicit, quantitative manner. Some of the more important casual methods used in financial modeling and forecasting include:

  • Chain Ratio Method: This method applies a series of factors for developing sales and demand forecasts, in which the quantitative impact of each factor is layered upon another in a structured, analytical approach.
  • Consumption Level Method: Useful for a product that is directly sold and consumed, as such fast moving consumer goods or telecom services, this method estimates demand / consumption levels on the basis of elasticity coefficients, such as the income elasticity of demand and the price elasticity of demand.
  • End Use Method: The end use method develops sales and demand forecasts on the basis of consumption coefficients of the product or service for various uses, and is most suitable for intermediate products / services.
  • Leading Indicator Method: Observed changes in the leading demand indicators for a product or service are used to predict the changes in lagging demand variables, most suited for products / services with predictable (or seasonal) demand cycles that are predicated on the occurrence of certain related events or customer behavior.

There is also an excellent demand forecasting template available in the Finance 3.0 forums, which takes monthly historic data to calculate a new-year monthly forecast with consideration for seasonality, secular trending, and the current business cycle.

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