Python Tutorial : Customer Analytics and A/B Testing in Python

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Hi, my name is Ryan and I will be your instructor for this course on customer analytics and A/B Testing. This is a fascinating subject, and I'm looking forward to the opportunity to explore it with you.

The primary focus of this course is A/B testing. A/B testing is a tool that allows you to test two or more different ideas against each other in the real world, and to see which one empirically performs better.

Because you are running this test in the real world, there is no guessing. You get to know which idea is better under the conditions that matter most. Beyond that, it has many other benefits. It can provide accurate answers quickly, allowing companies to rapidly iterate on ideas. At its core, it is one of the only statistically sound ways to establish causal relationships.
We will dive into all of this later in the course.

Simply speaking, A/B testing works by exposing unique randomly assigned groups of users to each of the ideas you want to test. Then you can observe these users, and by measuring how they behave, untangle the impact of each of your ideas ultimately determining which is best.

If you have users and ideas, then chances are you can run an A/B test. It is utilized in fields as diverse as pharmaceutical companies testing the impact of different drugs to mobile games trying to incentivize users to spend more, and subscription services working to drive user growth, as well as many more use cases beyond these.

Before you can perform an A/B test you must first understand what is worth testing and optimizing for. The first chapter will cover this topic in detail. Once we understand these metrics we discuss exploratory data analysis in chapter 2, which leads naturally to the bulk of the course, on how to design and analyzing A/B tests in practice. These topics will be covered thoroughly in the final two chapters of the course.

We will finish off this lesson by briefly discussing KPIs.

Typically, A/B tests are run to improve Key Performance Indicators or KPIs. These are the metrics that are most important to the business or organization you are a part of. For a drug company, these may be remission rates of cancer, or the likelihood of a particular side-effect. For a mobile game, it may be something like revenue or playtime per user.

Identifying the right KPIs requires a combination of experience, domain knowledge, and exploratory data analysis. Experience and knowledge will tell you what is likely to be an important driver of the business, and analysis allows you to uncover relationships that reveal which metrics truly measure these drivers.

Starting in the next video, we will dive headfirst into exploring a customer data set, and begin to work through an example to identify which metrics are meaningful and which are not. We will explore this in a way that interleaves the mechanics of doing the analysis with the art that makes up much of data exploration.

We will continue with similar examples throughout the course to show how the A/B testing process grows out of the ideas of KPIs & exploratory data analysis.

Now let’s work through some exercises on the high-level A/B testing material we just covered. From there we will dive into some live data and code. Good luck!

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