If you want to know what to anticipate in the future, a good first step is to look at the past. Businesses use this historical information in forecasting to plan their budgets and anticipate upcoming expenses.
What is Forecasting?
A technique for making informed estimates, forecasting applies historical data inputs to model future outcomes. Forecasting makes some basic assumptions around market and business conditions that can be disrupted. Due to those unknowable conditions, forecasting must be conducted often for accuracy. For example, a forecast may not anticipate a disrupter on the market or the acquisition of a new strategic account. Accuracy in forecasting is vital, as it influences purchasing fixed assets, staffing plans, funding and significant expenses. Forecasting is also used in valuation, which is focused on cash flow.
Forecasting vs. Budgeting
Although budgeting and forecasting often go hand-in-hand, they are unique financial practices, according to Accounting Tools.
Budgeting allocates revenue, outlining company expectations for an annual period. This typically includes:
- A detailed plan for expenses, revenue and cash flow
- Estimated debt reduction
- Anticipated cash flow
- Comparison of actual versus budgeted as the year progresses
- An impact on employee compensation based on results against budget
Forecasting estimates future financial results based on historical data. This typically includes:
- Significant revenue and expenses
- Updates at regular intervals when changes in the business occur
- Short and long term estimates. For example, revenue forecasts may be quarterly.
Forecasting typically does not include:
- Comparison of actual versus forecast
- An impact on employee compensation based on results against forecast
Depending on an organization’s industry and focus, forecasting methods can vary. Generally, this begins with a base assumption, selection of the appropriate dataset and a plan for analyzing the dataset. The analyst may be comparing revenue and economic indicators or observing changes in financial or statistical data to determine the relationship between variables.
The scope of forecasting can determine whether a qualitative or quantitative approach is used. Short-term forecasts may benefit from a qualitative approach, which is much more reliant on expert opinion and limited statistical data. Qualitative approaches are based on polls, surveys or market research. Quantitative methods apply more analytics, using time series methods, analysis of leading or lagging indicators, or economic modeling.
Some typical forecasting methods include:
The straight-line method uses historical trends to predict future growth. First, a sales growth rate is established looking at historical performance. Then, assuming growth continues at the same trajectory, the growth rate is applied for a future timeframe.
The moving average method looks at patterns in a dataset to determine patterns that establish future estimates. Generally, this is a three or five-month moving average. Using a three-month moving average, the analyst will calculate the average revenue of the current and past two months. This average is then applied to a future timeframe.
Simple linear regression, or regression analysis, analyzes the relationship between variables. For example, a company may correlate its advertising spend to revenue and then apply that correlation to predict future revenue based on advertising spend.
Multiple linear regression is regression analysis applied when multiple variables impact the projection. Using the previous example, a company may note that it’s not only advertising spend, but the cost of promotions, that influence revenue.
Hybrid forecasting blends analytics and general knowledge based on a feel or expert assessment of a situation.
Considerations in Forecasting
For a forecast to be valuable, it must be generally reliable. Forecasters should be mindful of several areas to ensure they are delivering a sound forecast:
- Anticipate any potential political or legal influence.
- Understand and use the appropriate business cycle.
- Examine demographic trends that could change the landscape. For example, a surge of retiring baby boomers could increase demand for retirement services.
- Review outliers or historical anomalies. For example, a successful product launch may see a rise in growth for the first year, but the business may not anticipate sustaining that growth rate.
- Understand relationships between variables.
- Establish the assumptions made in the forecast.
- Provide a forecast with a range of outcomes, and across both short and long timeframes.
- Root the forecast in the industry context, bearing in mind the addressable market, as sales cannot logically exceed the addressable market.
- Consider the competition to ensure competitor share and disruption is also accounted for.
- Amend and adjust forecasts as needed, and view the forecast as a dynamic tool.
- Assess past forecasts against actual revenue and understand how to improve forecasting approaches in the future.
In many cases, issues in forecasts come down to simple human error, according to the Harvard Business Review. Forecasters should avoid these errors:
- Don’t keep bad news quiet. One key weakness can be win rates, as salespeople worry about lower win rates. Unfortunately, a false win rate leads to over-inflated estimates.
- Don’t keep separate “real” records and report something else in the Customer Relationship Management (CRM) system.
- Don’t let false hope inflate predictions. If a deal is probably dead, assume it is to avoid unrealistic floating deals.
- Don’t avoid asking questions. If variables, data or relationships don’t make sense – ask.
By rewarding accuracy and vetting forecasts, over time businesses can improve their accuracy, which results in better budgets and a more realistic understanding of the business. And, better control is likely to lead to better outcomes.