Customer Analytics and A/B Testing in Python

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

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4 Hours16 Videos49 Exercises14,797 Learners
3750 XP

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Course Description

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Customer analytics and in particular A/B Testing are crucial parts of leveraging quantitative know-how to help make business decisions that generate value. This course covers the ins and outs of how to use Python to analyze customer behavior and business trends as well as how to create, run, and analyze A/B tests to make proactive, data-driven business decisions.

  1. 1

    Key Performance Indicators: Measuring Business Success

    Free

    This chapter provides a brief introduction to the content that will be covered throughout the course before transitioning into a discussion of Key Performance Indicators or KPIs. You'll learn how to identify and define meaningful KPIs through a combination of critical thinking and leveraging Python tools. These techniques are all presented in a highly practical and generalizable way. Ultimately these topics serve as the core foundation for the A/B testing discussion that follows.

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    Course introduction and overview
    50 xp
    Understanding the key components of an A/B test
    50 xp
    Defining meaningful KPIs
    50 xp
    Identifying and understanding KPIs
    50 xp
    Loading & examining our data
    100 xp
    Merging on different sets of fields
    100 xp
    Exploratory analysis of KPIs
    50 xp
    Practicing aggregations
    100 xp
    Grouping & aggregating
    100 xp
    Calculating KPIs - a practical example
    50 xp
    Calculating KPIs
    100 xp
    Average purchase price by cohort
    100 xp
  2. 2

    Exploring and Visualizing Customer Behavior

    This chapter teaches you how to visualize, manipulate, and explore KPIs as they change over time. Through a variety of examples, you'll learn how to work with datetime objects to calculate metrics per unit time. Then we move to the techniques for how to graph different segments of data, and apply various smoothing functions to reveal hidden trends. Finally we walk through a complete example of how to pinpoint issues through exploratory data analysis of customer data. Throughout this chapter various functions are introduced and explained in a highly generalizable way.

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  3. 3

    The Design and Application of A/B Testing

    In this chapter you will dive fully into A/B testing. You will learn the mathematics and knowledge needed to design and successfully plan an A/B test from determining an experimental unit to finding how large a sample size is needed. Accompanying this will be an introduction to the functions and code needed to calculate the various quantities associated with a statistical test of this type.

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  4. 4

    Analyzing A/B Testing Results

    After running an A/B test, you must analyze the data and then effectively communicate the results. This chapter begins by interleaving the theory of statistical significance and confidence intervals with the tools you need to calculate them yourself from the data. Next we discuss how to effectively visualize and communicate these results. This chapter is the culmination of all the knowledge built over the entire course.

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In the following tracks

Marketing Analytics

Collaborators

Lore DirickYashas RoyEunkyung Park
Ryan Grossman Headshot

Ryan Grossman

Data Scientist at EDO Inc.

Ryan is a Data Scientist at EDO Inc, a Data Science Software company. Prior to that he worked designing and analyzing A/B tests and customer data as a member of the Business Analytics team at Tinder. He received his bachelor's degree in Statistics from Harvard University and is passionate about leverage data analytics to improve customer experiences and help companies run more efficiently.
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