Conversion rate sensitivities

To mix things up, we will spend the next few exercises working with the conversion rate metric we explored in Chapter One. Specifically you will work to examine what that value becomes under different percentage lifts and look at how many more conversions per day this change would result in. First you will find the average number of paywall views and purchases that were made per day in our observed sample. Good luck!

This exercise is part of the course

Customer Analytics and A/B Testing in Python

Exercise instructions

Merge the paywall_views with demographics_data tables using an 'inner' join. This will limit the result to only include users who appear in both and will remove everyone who did not view a paywall, which is what we want in this scenario.
Group purchase_data by 'date'. The result of this is then aggregated for you by summing over the purchase field to find the total number of purchases and counting over it to find the total number of paywall views.
Average each of the resulting sum and count fields to find the average number of purchases and paywall views per day.
The results reflect a sample of 0.1% of our overall population for ease of use. Multiply each of daily_purchases and daily_paywall_views by 1000 so our result reflects the magnitude change if we had been observing the entire population.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Merge and group the datasets
purchase_data = demographics_data.merge(paywall_views,  how='____', on=['uid'])
purchase_data.date = purchase_data.date.dt.floor('d')

# Group and aggregate our combined dataset 
daily_purchase_data = purchase_data.groupby(by=['____'], as_index=False)
daily_purchase_data = daily_purchase_data.agg({'purchase': ['sum', 'count']})

# Find the mean of each field and then multiply by 1000 to scale the result
daily_purchases = daily_purchase_data.purchase['sum'].____()
daily_paywall_views = daily_purchase_data.purchase['count'].____()
daily_purchases = daily_purchases * ____
daily_paywall_views = daily_paywall_views * ____

print(daily_purchases)
print(daily_paywall_views)

Edit and Run Code

This exercise is part of the course

Customer Analytics and A/B Testing in Python

IntermediateSkill Level

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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.

Exercise 1: Course introduction and overview Exercise 2: Understanding the key components of an A/B test Exercise 3: Defining meaningful KPIs Exercise 4: Identifying and understanding KPIs Exercise 5: Loading & examining our data Exercise 6: Merging on different sets of fields Exercise 7: Exploratory analysis of KPIs Exercise 8: Practicing aggregations Exercise 9: Grouping & aggregating Exercise 10: Calculating KPIs - a practical example Exercise 11: Calculating KPIs Exercise 12: Average purchase price by cohort

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.

Exercise 1: Working with time series data in pandas Exercise 2: Parsing dates Exercise 3: Creating time series graphs with Matplotlib Exercise 4: Plotting time series data Exercise 5: Pivoting our data Exercise 6: Examining the different cohorts Exercise 7: Understanding and visualizing trends Exercise 8: Seasonality and moving averages Exercise 9: Exponential rolling average & over/under smoothing Exercise 10: Events and releases Exercise 11: Visualizing user spending Exercise 12: Looking more closely at revenue

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.

Exercise 1: Introduction to A/B testing Exercise 2: Good applications of A/B testing Exercise 3: General properties of an A/B Test Exercise 4: A/B test generalizability Exercise 5: Initial A/B test design Exercise 6: Experimental units: Revenue per user day Exercise 7: Preparing to run an A/B test Exercise 8: Conversion rate sensitivities

Current Exercise

Exercise 9: Sensitivity Exercise 10: Standard error Exercise 11: Calculating sample size Exercise 12: Exploring the power calculation Exercise 13: Calculating the sample size

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.

Exercise 1: Analyzing the A/B test results Exercise 2: Confirming our test results Exercise 3: Thinking critically about p-values Exercise 4: Understanding statistical significance Exercise 5: Intuition behind statistical significance Exercise 6: Checking for statistical significance Exercise 7: Understanding confidence intervals Exercise 8: Calculating confidence intervals Exercise 9: Interpreting your test results Exercise 10: Plotting the distribution Exercise 11: Plotting the difference distribution Exercise 12: Finale