Lesson tag: Metrics

We have the privilege of meeting with thousands of entrepreneurs every year, and in the course of those discussions are presented with all kinds of numbers, measures, and metrics that illustrate the promise and health of a particular company. Sometimes, however, the metrics may not be the best gauge of what’s actually happening in the business, or people may use different definitions of the same metric in a way that makes it hard to understand the health of the business.

A few weeks ago, we shared some key startup metrics (16 of them, to be exact) that help investors gauge the health of a business when investing in it.

Active customers churn. And when they do we’re shocked and confused.

How can this happen? Your customer was very active, logging-in several times in the last month.

I think it’s fair to say that if Active customers churn, then “active” – as a customer “state” – clearly doesn’t equate to success.

Outlier detection can be a pain point for all data driven companies, especially as data volumes grow. At Netflix we have multiple datasets growing by 10B+ record/day and so there’s a need for automated anomaly detection tools ensuring data quality and identifying suspicious anomalies. Today we are open-sourcing our outlier detection function, called Robust Anomaly Detection (RAD), as part of our Surus project.

A user is someone that uses your SaaS product, right? Or is it someone that signed-up? Or someone that’s active? Or someone that logged-in a few times? Hmm.

Okay, so maybe defining a user is hard, but defining a customer is easy, right?