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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

House ads conversion by language

Now that you've ruled out natural fluctuations across the day of the week a user saw our marketing assets as they cause for decreasing house ads conversion, you will take a look at conversion by language over time. Perhaps the new marketing campaign does not apply broadly across different cultures.

Ideally, the marketing team will consider cultural differences prior to launching a campaign, but sometimes mistakes are made, and it will be your job to identify the cause. Often data scientists are the first line of defense to determine what went wrong with a marketing campaign. It's your job to think creatively to identify the cause.

Instructions

100 XP
  • Create a new DataFrame house_ads by isolating the rows from marketing where marketing_channel equals 'House Ads'.
  • Run your conversion_rate() function on the house_ads DataFrame grouping by date_served and language_displayed.
  • Use the plotting_conv() function on conv_lang_df to display your results.