Turning Observations into Action with Revenue Driver Trees
By: The CDPa Team
We’ve covered how organizations can identify and activate their most valuable customers using a wide range of data and analytics tools — now we’re focusing on how to turn data into useable insights.
Today’s customer-facing organizations often face the challenge of what to do with an overwhelming amount of data. With millions of data points arriving in real-time, how can a marketing or sales team understand which data is relevant and which isn’t? The process of collecting and organizing data can help categorize and prioritize different customer groups, but the challenge arises when it comes to figuring out what to do next after customers have been broken down into segments. In other words, how can tables of information be used to inform real-world strategies?
The first step in turning information into action is putting in place a simple analysis framework. When beginning the journey of making data-driven changes to improve customer-centric metrics like customer lifetime value (CLV), one of the most effective solutions is a revenue driver tree.
Revenue driver trees make it possible for organizations to put hypotheses to the test and steadily improve their performance.
Understanding segment performance
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.
Conducting tests and analysis
By continually breaking down high-level metrics into more granular information, revenue driver trees make it possible for organizations to put hypotheses to the test and steadily improve their performance.
In the case of our sporting goods store, they have a few options: experiment with offering sales prices on athleisure clothing, or offer special prices on merchandise from nearby professional sports teams. They could also test out minimum order thresholds for free shipping on online orders, or even try a new product line to bring customers into brick-and-mortar stores more frequently.
These tests should always begin with a question and a hypothetical answer. Why are valuable customers completing orders less frequently? Maybe this season’s product assortment doesn’t carry the same appeal as in previous years. Maybe a previous promotional campaign was highly effective but was discontinued this year. Once a hypothesis has been tested and proven, that problem can be directly addressed with data-driven adjustments. What’s more, the revenue driver tree provides a framework to track changes in performance.
Applying best practices
Revenue driver trees are valuable for all businesses. This approach to data analytics can offer immediate insights across a wide range of industries, companies, and customer segments — a hotel chain could use a revenue driver tree to understand the impact of changes to its loyalty points program, while a regional restaurant group can discover why some locations are significantly outperforming others among an important segment.
To uncover more best practices on how to find and activate high-value customers, check out our most recent playbook. Be on the lookout for more insights from the CDPa in the weeks to come, including downloadable resources to help any organization put our recommendations into practice.
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