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  3. Analyzing Marketing Campaigns with pandas
  • 1

    Pandas

    Free

    In this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting.

  • 2

    Exploratory Analysis & Summary Statistics

    In this chapter, you will learn about common marketing metrics and how to calculate them using pandas. You will also visualize your results and practice user segmentation.

  • 3

    Conversion Attribution

    In this chapter, you will build functions to automate common marketing analysis and determine why certain marketing channels saw lower than usual conversion rates during late January.

  • 4

    Personalization A/B Test

    In this chapter, you will analyze an A/B test and learn about the importance of segmentation when interpreting the results of a test.

Initializing

Exercise

Comparing conversion rates

Now that we know allocation is relatively even let's look at the conversion rate for the control and personalization. Since we chose conversion rate as our key metrics for this test, it is highly important that we evaluate whether or not conversion was higher in the personalization treatment compared with the control. While we will dive in deeper in subsequent exercises, measuring the difference between the key metric in the control and the treatment is the most important part of evaluating the success of an A/B test.

The DataFrame email has been loaded in your workspace which contains only rows from the marketing DataFrame where marketing_channel is 'Email'.

Instructions

100 XP
  • Group the email DataFrame by user_id and variant while selecting the maximum value of the converted column and store the results in subscribers.
  • Drop missing values from the control column of subscribers_df.
  • Drop missing values from the personalization column of subscribers_df.
  • Calculate the conversion rate for both personalization and control using the appropriate function for each.