Automatic Forecasting: Sales Driven Models Meet Linear Regression

Business people use forecasting models regularly, especially for creating pro-forma financial statements and operating budgets. Two common types are sales based models and linear regression forecasting with independent line-item projection. Each has distinct benefits and limitations that make it suitable for specific situations.

Sales-Driven Forecasting Models

To use a sales based model you first forecast the total revenue and then project all other line items. The items usually include cost of goods sold (COGS) and fixed costs, all expressed as a percentage of that revenue.

The percentages used are either based on a company’s own historical averages or on industry sector comparable figures. This makes it quick to apply and great for benchmarking your company’s performance against peers.

But there is a catch. The approach assumes all key expenses and profits scale directly with sales. This can miss anomalies in costs, such as fixed expenses not rising at the same rate as revenue, or COGS fluctuating due to supply chain changes rather than sales alone.

What does it mean? That sales-driven forecasting models may miss irregularities and company-specific patterns that deviate from industry norms.

Linear Regression Forecasting with Independent Line-Item Forecasts

Here you use linear regression to forecast each line item independently. For example, cost of goods sold (COGS), fixed expenses, and other major accounts are forecasted based on their own individual historical trends, not as a percent of revenue.

By analyzing each line item separately you can spot unusual trends more readily. Significant deviations in, say, utility costs or raw material expenses become evident, as they aren’t masked by a revenue-driven formula that uses an assumed percentage.

Regression models can also help you identify causal relationships unique to the particular company. And this supports more granular, data-driven decision making.

Note though that the method requires reliable historical data in order to produce realistic and potentially more accurate forecasts.

Best Practice: Use Both Approaches

What model should you choose? Actually, combining both models is often the best practice. Sales-driven models that base forecasts on industry averages are great for comparisons. On the other hand, line-item regression projections focus on actual trends that can reveal unique risks or value drivers within the business itself.

Running both in parallel allows you to cross-validate results, spot historic trends and unexpected shifts, and ensure the forecasts you get are both consistent and supportable.

You will find this dual-modelling approach especially valuable for business valuation. For the best results, consider including both models in your forecasting toolkit.

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