JK — Data Scientist at Google, author of two books, patent holder. I write AI First Data Scientist: doing real data science in the age of AI agents. Warm, first-principles, honest about the messy middle.
Retention & LTV
Estimating the Lifetime value (LTV) is indispensable in understanding the health of the product as well as in evaluating if the Cost of Acquiring Customer (CAC) or Cost of Retaining Customer (CRC) is justified. But many product organizations never calculate this value since this is viewed as an exercise in
Experimentation & A/B Testing
Is 10 and 11 significantly different from each other? What about 10,000 & 11,000. Just comparing counts is unusual in the world of experimentation, since we are often focused on comparing differences in means or differences in proportions. In many cases, this nudges us to create metrics that
The AI-First Data Scientist
Since Alan Turing's fascination behind the question, "Can machines think?" (from his novel paper on Computing Machinery and Intelligence in 1950) to teaching machines long term and short-term attention (from "Attention is all you need" paper in 2017), the best brains and the biggest
Experimentation & A/B Testing
Note : This is the third part of a series of articles discussing variance reduction techniques in A/B testing. Written in collaboration with Athulya Ganapathi Kandy The previous parts can be found here: * PART I : All strata are useful, but some strata are more useful than the others | LinkedIn * PART
Experimentation & A/B Testing
Note: This is the second part of a series of articles discussing variance reduction techniques in A/B testing. The first part can be found here: All strata are useful, but some strata are more useful than the others | LinkedIn When considering variance reduction techniques, there is a very simple
Experimentation & A/B Testing
Written in collaboration with Athulya Ganapathi Kandy NOTE : This is the first part of a series of posts discussing variance reduction techniques in A/B testing Introduction The biggest adversary an analytics professional encounters in their experimentation journey is (statistical) insignificance, which is the antithesis of what A/B tests
Experimentation & A/B Testing
When it comes to multiple test corrections in A/B tests, two methods stand out - Bonferroni Correction and Benjamini-Hochberg (B-H) Corrections. In this blog post, we will explore: * Commonly overlooked multiple testing scenarios * The difference between Bonferroni Correction and B-H Correction * Python Implementation for both methods Commonly Overlooked Multiple
Personal
If you want to convince a million homes to install solar panels on their rooftops to power their homes, how would you do it? Informing the household, of their solar potential would be the first thing in my hitlist. But first of all, why would anyone want to go solar.