A revenue driver tree is an analytic framework that makes it possible to break down customer behaviors and understand the underlying causes of a given trend or spending driver. Revenue driver trees can be applied to any number of customer segments, and be used to analyze spending behavior on a year-over-year perspective, compare segments, identify areas of opportunity, and even measure campaign performance. They go beyond basic metrics like total revenue or revenue per customer to uncover the root causes of changes in top-line performance.
How does a revenue driver tree work in practice? Let’s consider an example.
Imagine a sporting goods chain with a national footprint. The company knows that parents with teenage children are an important customer segment, because they often have to purchase new equipment for their child’s athletic season. However, the company finds that its revenue numbers fell slightly over the past year in this segment, despite the fact that they increased their total number of customers.
A non-data-driven organization would recognize that they have a problem with one of their important customer groups, but they wouldn’t be able to pin down the reason why. However, a revenue driver tree allows data-driven teams to delve into why by discovering important signals. Breaking down the figures, the company finds that their average order value (AOV) has actually increased by 1.2%. However, it’s the number of orders per customer where they’re seeing problems: among mothers with teenage children, their average number of orders per year has fallen.