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Book: Lean Analytics Book: The Effective Engineer
  • Good metrics are important

    • They should be comparable across time
    • Ideally, they should be rates (i.e. have a time dimension)
  • Metrics should be actionable

    • They help you make better decision faster
    • Ex: Percentage of active users (Your actions can cause it to change)
    • Another Ex: Customers acquired over a certain time period
      • Basically we want to feel the direction as much as the velocity.
    • Negative Example: Total number of users (It will always be growing so hard to get a feel for the direction)
  • For the same reason, it’s always preferable to have lead indicators vs lag indicators

  • Ensure that metrics measure the right thing.

    • Ex: time spent on app might be because people can’t find what they’re looking for/ stuck in support etc
  • Make sure you set up metrics for your known knowns.

    • This includes things you know you want to measure.
  • To tackle the unknown unknowns, it’s important to routinely dive through metrics and explore

  • In metrics, correlation is good but causation is better

  • Metrics are a line in the sand

    • It’s important to reflect and update our metrics.
    • Exploring the data helps with this.
  • Questions to keep in mind about metrics.

  • To prevent regressing metrics, one technique is to use metric ratcheting

Threshold vs Metric, Book: Lean Analytics, Legibility, Deliberate Practice, Google, Tying to Reality, Engineering Effectiveness, Instrumentation,