I wrote a tweet a few days ago that got a bunch of responses. What does that even mean? Is that just one of those tweets that is meant to look smart to tech insiders but doesn’t actually mean much? Well, yes but I decided take it upon myself to expand a ambiguous “strategy” tweet into a longer, more ambiguous blog post with 500% more ambiguous “strategy” in it.
Any large system picks a metric to goal itself on. Entire books and way-too-long Medium posts have been written on the importance of said metric - it influences everything from people’s incentives to how quickly you can optimize your business. In an organizational equivalent of Schrödinger’s cat, picking the metric itself can cause weird cultural distortion (see Goodhart’s Law).
Since it is near impossible to perfectly measure human behavior, most large teams/products pick a proxy metric to measure underlying behavior. For example - ‘clicks’ are a proxy for “did I read this?” and “will I buy this product sometime in the future?”, ‘time spent’ is a proxy for “did I enjoy this content?” and NPS is often a substitute for “do I love this company?”. You convert a nebulous human emotion/behavior to a quantifiable metric you can align execution on and stick on a graph and measure teams on. Engineers and data scientists can’t do anything with “this makes people feel warm and fuzzy”. They can do a lot with “this feature improves metric X by 5% week-over-week”. Figuring out the connection between the two is often the art and science of product management.
#Startups
#Platforms
Any large system picks a metric to goal itself on. Entire books and way-too-long Medium posts have been written on the importance of said metric - it influences everything from people’s incentives to how quickly you can optimize your business. In an organizational equivalent of Schrödinger’s cat, picking the metric itself can cause weird cultural distortion (see Goodhart’s Law).
Since it is near impossible to perfectly measure human behavior, most large teams/products pick a proxy metric to measure underlying behavior. For example - ‘clicks’ are a proxy for “did I read this?” and “will I buy this product sometime in the future?”, ‘time spent’ is a proxy for “did I enjoy this content?” and NPS is often a substitute for “do I love this company?”. You convert a nebulous human emotion/behavior to a quantifiable metric you can align execution on and stick on a graph and measure teams on. Engineers and data scientists can’t do anything with “this makes people feel warm and fuzzy”. They can do a lot with “this feature improves metric X by 5% week-over-week”. Figuring out the connection between the two is often the art and science of product management.
#Startups
#Platforms
