Fail fast—fail cheap. That mantra is becoming popular among business people as a way to promote Dynamic Customer Strategy because the idea is to accelerate learning through better experimentation.
But how do you know when something went wrong? How quickly can you get the right data that tells you what has happened or is happening?
In this chapter, we'll explore the application of Big Data to metrics, because Big Data can make metrics more complicated. At the same time, however, visualization techniques are simplifying Big Data–based metrics to ease decision making. So first we'll discuss metrics in the Big Data world and conclude with a primer on visualization.
One of the themes of this book is that strategy is about making choices, and the power of Big Data is in how you can use it for making informed choices. Metrics are designed to establish two factors: the effectiveness of your marketing activity and the efficiency of your marketing spend, or to put it another way, how much you are earning and how fast.
Efficiency is also a standardized measure, meaning that you are able to compare across settings. The most common efficiency measure is return on investment, or ROI. By converting earnings into a percentage of investment, we can compare across investments.
Other efficiency measures include such things as average sales per catalog or per square foot (if you're Cabela's), average sales per salesperson (if ...