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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

Grouping and counting by multiple columns

Stakeholders have begun competing to see whose channel had the best retention rate from the campaign. You must first determine how many subscribers came from the campaign and how many of those subscribers have stayed on the service.

It's important to identify how each marketing channel is performing because this will affect company strategy going forward. If one channel is outperforming others, it might signal the channel merits further investment.

You will build on what we have learned about .groupby() in previous exercises, this time grouping by multiple columns.

Instructions 1/2

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  • 1
    • Use .groupby() to calculate subscribers by subscribing_channel and date_subscribed.
  • 2
    • Use .groupby() to calculate retained subscribers by subscribing_channel and date_subscribed.