## Metadata - Author: [[Ergest xheblati|Ergest Xheblati]] - Full Title:: Using Data to Find Growth Levers - Category:: #🗞️Articles - Document Tags:: [[Data analysis|Data analysis]], - URL:: https://sqlpatterns.com/p/using-data-to-find-growth-levers - Finished date:: [[2024-01-14]] ## Highlights > Since the company is still a startup, sustained user growth (preferably exponential) is a key indicator of its success ([View Highlight](https://read.readwise.io/read/01hm2g2mqse35kve3kf569tte5)) > I’ve learned the hard way that only when you really **understand why** something happens can you not only solve the problem but also explain it in simple terms. ([View Highlight](https://read.readwise.io/read/01hm2g8fa0zksn7dbk6ns24abw)) > they built a model of how retention actually worked. They segmented users based on their engagement level into buckets and build a retention model. ([View Highlight](https://read.readwise.io/read/01hm2gec2a8mm8xpmhpy108rya)) > With the model created, we started taking daily snapshots of data to create a history of how all of these user buckets and retention rates had evolved on a day-by-day basis over the past several years. With this data, we could create a forward-looking model and then perform a sensitivity analysis to predict which levers would have the biggest impact on DAU growth. We ran a simulation for each rate, where we moved a single rate 2% every quarter for three years, holding all the other rates constant. ([View Highlight](https://read.readwise.io/read/01hm2gemksyxkayq0g5be68m7k)) > This model allowed them to narrow down their focus on the blue box above (Current Users) which conveniently has an arrow looping back into itself creating a compounding effect. Current Users thus became their goal metric. ([View Highlight](https://read.readwise.io/read/01hm2gexrfr7qc2hak4jqsz5gd))