Chapter 3

Understanding Baselines


Putting your forecast in context with qualitative data

Avoiding common errors in sales forecasting

Understanding the effects of seasons and trends

You build your sales forecast on something called a baseline — that is, data that describes your level of sales, usually in prior months, quarters, or years. But creating a numeric forecast without looking at the context isn’t a good idea. You need to make sure you have a handle on product management’s plans, marketing’s promotional budget, sales management’s intentions for hiring (or firing), and so on.

Even with a good context and a good baseline, several common errors can send your forecast reeling off course. Recognizing and avoiding these errors is easy if you know what they are, and in this chapter I point them out for you.

Your baseline will often reflect both an ongoing trend (sales have been heading generally up or down) and seasons (sales reliably spike or drop at certain times of the year). In this chapter, I call out some of the reasons that context, common errors, trends, and seasonality contribute to good forecasts — and bad ones.

Using Qualitative Data

Qualitative data is information that helps you understand the background for quantitative data. Of course, that begs the question: What’s quantitative data? I want to focus on this issue early, because it’s an important one, and one that makes a real difference to the value of your sales forecasts.

Quantitative data

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