JK Vijayaraghavan - AI First Data Scientist (Page 2)

Experimentation & A/B Testing

You Don't Need a Crystal Ball or Calculus to Do Sizing

In my experience with data professionals, sizing is often one of the most misunderstood or misapplied efforts. This is a bold accusation, but I come from a place of atonement rather than audacity. I have done sizing the wrong way for years until I learnt the simple recipe behind the

Retention & LTV

The Vital Role of Retention in Unlocking Lifetime Value (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

When You Have Counts, Expected Value Is All You Need

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

We Need Universal Teaching Machines as Much as We Do Universal Learning Machines

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

Experimentation & A/B Testing

CUPED: What You Know Before the Experiment Matters as Much as What You Know After

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 * PART II:

Experimentation & A/B Testing

Capping Is the Low-Hanging Fruit You Should Pick, Unless the Whales Matter

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 When considering variance reduction techniques, there is a very simple yet

Experimentation & A/B Testing

All Strata Are Useful, But Some Strata Are More Useful Than Others

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

Multiple Test Corrections: Where False Positives Are Rigorously Trimmed

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

Personal

Empowering a Million Households to Go Solar

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.