IN THIS CHAPTER
Finding trends in your data
Looking at a mess of data and finding relationships that make sense
Many baselines of sales history rise during the early stages of a product’s release, and fall as technology and fashions move on. During a product’s middle age, the sales flatten out. Your forecasts’ accuracy improves if you understand the nature of those trends and whether you should detrend the baseline. Excel offers methods of testing for trends, to help you decide whether you’re looking at something real or just random variation.
Relationships in your sales baselines are the key to any forecast, whether the relationship is between one month’s results and the next month’s, or between historical sales results and some other variable such as advertising costs. In this chapter, I show you how to use Excel to quantify those relationships using Excel’s worksheet functions and the Data Analysis add-in.
Unfortunately, many decision-makers have no faith in sales forecasting. Their image of a forecaster is a combination of the meteorologist on Channel 7 and someone gazing into a crystal ball, probably wearing a pointy hat decorated with moons and stars.
But quantitative forecasting works for reasons that are sound, mathematical, and logical, and you can find plenty of examples of forecasts working in practice. If someone looks at you suspiciously when you trot out your sales forecast, you’ll want ...