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A/B Testing

Algorithms

Analytics

Machine Learning

Probability

Product Metrics

Python

Statistics

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Let’s say we work at a SaaS company, and last month we had 10,000 users sign up for a free trial. For each user, we have data on company size, industry, number of logins in the first week, and whether they invited teammates.

How would you approach segmenting these trial users for a nurture campaign? How many segments would you create, and what data points would drive your choices?

How would you design user segments for a SaaS trial nurture campaign and decide how many to create?

Trial User Segmentation

Marketing

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

What is a key disadvantage of the F-test when compared to multiple t-tests?

What is a key disadvantage of the F-test when compared to multiple t-tests?

Hundreds of Hypotheses

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Question	Topic	Difficulty	Ask Chance
Trial User Segmentation	Marketing	Medium	Very High
Boarding Times Bias	Statistics	Medium	Low
Hundreds of Hypotheses	Statistics	Medium	Low

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