What Really Counts?
(Editors note: This is a two-part post. Part 1, below, introduces the One Metric That Matters approach to metric measurement. For more on this and taking a lean approach to analytics, we recommend you read Lean Analytics, by Alistair Croll and Benjamin Yoskovitz. Part 2, to come, will take this approach and overlay it with enterprise transformation, particularly that of the Agile HR variety. Enjoy!)

We live in a data-rich world, filled with trackable, measurable interactions that can help us iterate on products and processes in nearly real-time. We have a wealth of information at our fingertips about user behavior, and we have a number of ways to validate just how successful we’ve been in a given initiative. This is, of course, good news: Generations before us couldn’t imagine the user-level data we have today, nor the tools we have available to churn it all into meaningful insight.

So we have a lot of data. But do we know what matters in this mess of numbers and metrics? What actually counts when we talk about whether or not we’re see increased user interaction with a given product or service? Are click-through rates or downloads really indicative of how likely it is your service will exist next year? Let’s assume you have a mature product: How do you know what customers want in the next iteration? What measurements will you use to help your solution move to the next level (whatever that may be – it’s all relative.)

Data is dangerous when we use it incorrectly – and that’s very easy to do: Metrics that mattered when a product was in beta, probably aren’t relevant for a years-old solution with a solid customer base. Indeed, we need to identify which metrics will matter when, and among that short list, what will be the single metrics that defines a solution’s success in the wild.

What is that One Metric That Matters (OMTM)?
The OMTM is less a concrete thing, and more of a guideline that helps you, the solution owner, identify how a product is doing. The concept comes from Ben Yoskovitz’s “Lean Analytics”, a book primarily for startups, focusing on deriving meaning from data. When it comers to OMTM, different stages demand different metrics, and of those metrics, there should really only be one that you rely on to tell you how things are going. This sole focus helps cut noise from the signal, and allows you to make very specific decisions for very specific reasons.

Steve Glaveski, entrepreneur and founder, put together a nice table of metrics highlighting which metrics might be used at which stage of hypothetical Innovation Program at a corporation. Of the many listed in each stage, the OMTM framework demands you select just one to track obsessively, and make decisions on.

What happens once you’ve identified your One Metric? You measure it! You’re free to track other metrics too, but the One Metric becomes your North Star. As things mature (or reverse course), change what that metric should be for the various stage. Glaveski summarizes, and suggests themes for each stage of growth (again, for that hypothetical Innovation Program):

Because your initiative or product may be different, the above tables may not apply to you. Still, the concepts remain the same: Cutting data noise from signal, and investing in that sensing and responding to that signal, will empower you to make impactful decisions for product growth. You’ll chase non-important numbers less, and see true progress where it counts.

Author: Christopher Goscinski