Whatever the nature of your forecast, there are a few principles that, if applied in the beginning of your forecasting exercise, will give you a step ahead of the complexity. These principles, which I call the forecasting framework, are composed of three dimensions: factorial analysis, past versus future, and cost versus accuracy.

Factorial analysis

The first step in any good forecasting practice is to split your target variable. Reduce its complexity. Define the lower variables, which actually influence the one you want to forecast. Another simple example: profit (revenues — cost) and revenues (price x sales volume). Assuming price in the short term might be fixed, to predict your revenues you just need to predict your volume. So what are the factors that influence the variability of your volume? New launches of competitors, customer demands?

Once you have identified all the potential influencing factors, assess them in terms of impact and magnitude. Through the assessment you will be able to score each of the factors in terms of their contribution to movement in your target variable.

Depending on the nature of your key factors, you can use different techniques to assess their impact. Each testing technique depends on data availability and the nature of each single factor. Once again, precision is welcome but not necessarily mandatory. As you will have the chance to test and improve your forecasting, the success factor here is to identify the eligible key influencing factors to focus on.

Past versus future

Once you have identified key factors, you need to analyze them. A first step is to split them into categories. Are they existing factors or are they new?

Do you have past history to rely on? If you’re a startup, and want to assess the credit risk of your client, how do you do that? You don’t have a time series; they are all new to your business.

Conversely, if you have a long-standing relationship with your key clients, and your demand is composed of cyclical order, analyzing a time series might provide interesting insight.

Some key factors have a long time series (i.e., pharma sales volume of a branded product launched 20 years ago, IMS data in some markets). Some do not (i.e., the reaction of your target customers to the launch of a new digitalized strategy on Facebook). If you are lucky enough to have a time series, use that. For instance, as said before, you can use regression analysis and see the effect of your competitors’ ADV on your top line. But, if you are missing that, here is what could be done.

If your key factors do not show any usable time series, you may find using a proxy to be worthwhile. In this trick, you find a proxy to your key factor. A proxy is a variable that can explain the movement of your key factor. But unlike your key factor, it is measurable. For instance, if you want to target the affluent and growing middle class in the emerging markets, you may find it difficult to measure that. What is the percentage of salary they will be spent on acquiring brand-name drugs? Use a proxy. For instance: you have data from the French pharma market that shows the average percentage of salary spent by the middle class on medicines. You can adapt this proxy, applying a multiple that simulates the lag in achieving a similar percentage. Be creative, you are forecasting; this is a learning exercise.

Cost versus accuracy

Now, you have identified your key factors. You understand the different ways to measure them. The last question then is about forecasting economics.

Measurability is partially influenced by the very first two points, but it is also a separate variable to be addressed. Indeed, measurability is all about a trade-off—if you spend more, will your accuracy be any better?

Clearly, if you have to address a multimillion-dollar manufacturing business, pursuing higher levels of accuracy in your forecast will allow you to save a huge amount of money. Conversely, when exploring the adoption of new and challenging strategies, the most important thing might be the possibility to learn and the consequences of your decision.

This is the most important lesson in forecasting: It’s a process, and most of the time it’s not perfect. Mistakes and inaccuracy are part of the game. You have to refine your forecasting practice, and you have to work to identify areas of improvement. But the value of the forecasting process is clear in that it will teach you a lot.

Luca De Angeli, FP&A, is finance and strategy director at Finox Biotech in Kirchberg, Switzerland.

Read the expanded version of this article at www.AFPonline.org/Exchange.