ADPList Product-led Growth: How to measure product-led growth?

This masterclass talked about how to understand data, what to analyse run test. The speaker talked about how analysing becomes widely used once one progresses towards product maturity. In POC stage, one typically has limited data to analyse. Then this data becomes more available when one moves towards MVP, product market fit, growth-stage and finally the staedy state.

Growth equations (once analytics are in place):
Quick ratio = How we are growing
Gross retention = company gets judged on. How much revenue we keep vs overall revenue

Interesting example of Facebook marketplace, they talked to users realised that it is an acquisition problem, since the marketplace is hidden in the app. The team brainstormed for ideas

Some people stop the test once they see success but that is biased and risky. Decide from before hand on the number of tests, the duration

Experiment when you want to learn something. Make sure there are enough users for the test, it will change a decision. Failure in a test can be good since you will learn what is not working and make improvements accordingly.  Worse than failure is a false results, invalid tests (impossible to fail and easy win, which renders it pointless), flailed tests (impossible to succeed - the test is pointless). You spent time setting it up but the results do not help.

Key takeaways:

1) Measurement helps us make the right decisions

2) Experiments help us learn, change and prove incremental impact