A checklist for developing better product metrics & KPIs

Defining clear KPIs is one of the most important things for a Product team, and (unfortunately) it’s incredibly easy to do poorly. A good metric not only helps evaluate your product’s performance, but also guides your team in building the right things to progress you toward your goal.

The framework described below is one I’ve used for a few years while working with teams that develop software products, and has been very helpful for me, my Data Science teammates, and our Product partners in measuring the success or failure of the products we build.

Shared definitions for objectives, KPIs, & product metrics

Before diving in, let’s define a…

A tribute to useful, but less interesting research findings

A few years ago as a Data Scientist I was presenting to co-workers an analysis I’d been working on. The presentation went fine and the work was well-received, but I could tell the group was a little underwhelmed. Towards the end of the presentation, one co-worker asked, “Did you find anything that surprised you? Anything we didn’t already know?”

I had uncovered some new information, but most of what I’d found was well-aligned with what we already thought to be true. Still, I understood their sentiment. Any Data Scientist or Researcher will tell you that the most common thing we…

A guide to building a more meaningful metric

The Net Promoter Score is a widely-used survey question that companies use to measure customer satisfaction, loyalty, and growth.

Proponents of NPS are drawn to it because it’s a single number that appears — on the surface, at least — to be linked to some significant indicators of performance. NPS a bad measure of success, though. It uses a poorly phrased question, a response scale that’s entirely too big, and an absurd method of calculation.

There are other metrics you can use that will be more accurate, more interpretable, and much more predictive of satisfaction, loyalty, or growth.


The standard…

Brian Weinstein

Froyo enthusiast & Sr Data Science Manager @Slice; prev. froyo enthusiast & data science @WeWork / @1stdibs, applied math @Columbia, physics @AmericanU

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