The Hierarchy of Metrics

Michael Karpov
Dec 10, 2019 · 8 min read

What is the purpose of the hierarchy of metrics?

Compilation algorithm

It can be helpful to visualize the algorithm using MindMap (e.g. MindMeister, Mind42, Miro) or on a sheet / flipchart

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Nuances and details

· As a simple option to start — I would suggest that you use “Revenue” as the key product metric (if you don’t have any other input from the company’s leadership).

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What to do with all this next

· Take your backlog and decompose the features from it into metrics derived from the metric hierarchy — the closer to the top the metric is, the more likely the feature will be successful (of course, there is still a certain “tackle” coefficient when influencing a metric — that is, if you greatly influence a level-IV metric, then this can more useful than a small change in a level-II metric)

Frequent errors

1. Goal setting: confirm that the metric you have chosen for the top of the hierarchy is truly the most important (hint — check with the company’s leadership + you can take the “Product revenue” metric by default). Do you really want “Order quantity” to increase more than anything else, and not “Total revenue” or “Ratio of revenue per user to attraction costs”?

Answers to questions

On the slide that discusses the hierarchy of metrics, it was pointed out that features should be associated with a metric, and the closer this metric is to the primary one, the more important the feature is. Can you explain in more detail why this is so?

In this case, I would suggest the following:

· Key Product Metric — Revenue.

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