Sales Forecasting

Sales forecasting drives revenue projections by estimating how many sales will be made and when they will occur. Let’s walk through the different sales forecasting methods as well as an example of this process in action.

What Is Sales Forecasting?

A sales forecast estimates how much your company plans to sell within a certain time period. This could be over a day, week, month, quarter, year, or any combination. The best sales forecasts do this in a very accurate way.

A sales forecast needs to answer two main questions. First, how many sales will be made (and in theory, at what price). Second, when will these sales come in.

Sales forecasts help the entire business plan resources to ship products, pay for marketing, hire employees, and so much more. Accurate sales forecasting creates a well-oiled machine that meets customer demand, both today and in the future.

Sales forecasts impact every department in a business. For example, the finance department uses sales forecasts to decide how to make annual and long-term investments. Product leaders use them to plan demand for new products. And the HR department uses forecasts to determine recruiting needs. 

At some level, sales forecasting affects everyone in the company.

Forecasting Methods

While there are many qualitative sales forecasting methods, those methods tend to be the domain of sales and marketing. We will focus on quantitative forecasting methods.

• Test Marketing – Sell products in one specific market and extrapolate those results to similar markets
• Time Series Analysis – Use historical activity, including seasonality, to predict future results
• Regression Analysis – Use a variable that you know to forecast a variable that you don’t know
• Econometric Models – Replacement demand + new owner demand = sales

Example – Using Time Series Analysis to Forecast Hotel Revenue:

In this example, we will use time series analysis to evaluate a hotel near the beach that has a high degree of seasonality. For purposes of this example, we will assume no changes in the underlying drivers such as room rate or rooms available.

Step 1 – Gather historical data

Step 2 – Analyze Trends

We will analyze both the secular trend and seasonality and the slope of the line. With a high degree of high seasonality, it is necessary to calculate the secular trend using regression analysis.

Step 3 – Calculate the Forecast

Keep in mind that seasonality can affect the slope of the line. I recommend taking two approaches to the forecast to care for the impact of seasonality. If you have significant changes between the two methods, further analysis may be needed.

In the first method, we take the full-year growth of $262,236 and add it to the full year of history. Then, we use the seasonality % to spread the full-year number. In the second method, we add the growth to the prior year’s number. Reviewing the two methods, they are close to each other and both methods can be relied upon.

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