While advanced mathematics and enormous computational power have improved forecasting potential significantly, few would argue that forecasting is an exact science. That's because at its core, forecasting is still mostly a human dynamic where accuracy is dependent upon...
· asking the right people the right questions;
· their willingness to answer truthfully and completely;
· the ability to separate the meaningful elements from the noise; and,
· the openness of the forecaster to suggestions of process improvement.
That last point is key: process improvement.
Consistently good forecasting isn't a mathematical exercise performed at regular intervals (e.g., quarterly) as much as it's an ongoing process of gathering and evaluating dozens or hundreds of points of information into a decision framework. Then, when called upon, this decision framework can output the best forward-looking view grounded in the insights of the contributors. While software can facilitate process structure by prompting for specific fields of information to be included, it cannot make judgments on the quality of the information being input. Garbage in; garbage out.
As marketers, our job is to consistently prepare forecasts that help our companies conceive, plan, test, build and ultimately sell successful products and services. Sound forecasting processes form the foundation of an "early warning" system to alert the rest of the organization to the need to rethink its market orientation. In essence, forecasting becomes the rudder that can help your company stay the course, change directions, or navigate uncharted waters with confidence. As such, marketing migrates from being a tactical player to a strategic resource for the CEO when forecasts become more accurate, timely, and reliable.
Five Keys to Better Forecasting
Here are a few things I've learned over the years that result in much better forecasts:
1. Be Specific. Define exactly what you are trying to forecast. If you say "sales,"what do you really mean? Revenue? Unit volume? Gross margins? Net profit? The differences are substantial and might cause you to take very different approaches to forecasting. Likewise, having some sense of how far out you need to forecast (e.g. 3 months, 12, 36, etc.) and how accurate you need to be will guide you to use forecasting methods and processes better suited to your objectives.
2. Be Structured. Being methodical in defining all of the dimensions, variables, facts, and assumptions will pay huge dividends in several ways, including explaining your forecast to skeptics and inspiring confidence that you've been comprehensive and credible in your approach.
3. Be Quantitative - With or Without Data. Regardless of how little data you have, there are scientifically developed and proven ways of making better decisions. You may not have the raw materials for statistical regression forecasting, but you surely can use Delphi techniques of other judgmental calculus tools to transform perceptions and intuition of managers into data sets which can be more fully examined and questioned. Often, the process of quantifying the fuzzier logic uncovers great insights that were previously overlooked.
4. Triangulate - Use multiple forecasting methods and see how the results differ. Chances are that the "reality" is somewhere within the triangle of results. That level of accuracy may be sufficient. But even if it isn't, the multiple-method approach highlights weaknesses in any single method that might otherwise be overlooked -- and that in itself leads to more accurate forecasts.
5. KISS - Keep it simple, stupid. As with most things in life, simplicity is a virtue in forecasting. Einstein said that "things should be made as simple as possible, but no simpler." In forecasting,we interpret that to mean that an accurate and reliable forecasting process should be comprehensive enough to identify the truly causal factors, but simple enough to explain to those who will need to make decisions upon it. There is no power in a forecast if those who need to trust it cannot understand or explain the logic and process behind it.
Recognizing forecasting to be a complex human decision process is the first step toward improving the accuracy and reliability of the forecasts coming out of your department.