Demand Forecasting
Demand forecasting drives revenue projections by estimating how much demand exists for a product, and when that demand will occur. Let’s walk through the different demand forecasting methods as well as an example of this process in action.
What Is Demand Forecasting?
A demand forecast estimates how much demand will exist for a product within a certain time period. This could be over a day, week, month, quarter, year, or any combination. The best demand forecasts do this in a very accurate way.
A demand forecast needs to answer two main questions. First, how much demand exists (and in theory, at what price). Second, when will this demand exist.
Demand forecasts help the entire business plan resources to ship products, pay for marketing, hire employees, and so much more. Accurate demand forecasting creates a well-oiled machine that meets customer needs, both today and in the future.
Demand forecasts impact every department in a business. For example, the finance department uses demand forecasts to decide how to make annual and long-term investments. Product leaders use them to plan for new products. And the HR department uses forecasts to determine recruiting needs.
At some level, demand forecasting affects everyone in the company.
Forecasting Methods
While there are many qualitative demand 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 that demand 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 Generate Demand Forecast and 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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