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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

Analyzing House ads conversion rate

Now that you have confirmed that house ads conversion has been down since January 11, you will try to identify potential causes for the decrease.

As a data scientist supporting a marketing team, you will run into fluctuating metrics all the time. It's vital to identify if the fluctuations are due to expected shifts in user behavior (i.e., differences across the day of the week) versus a larger problem in technical implementation or marketing strategy.

In this exercise, we will begin by checking whether users are more likely to convert on weekends compared with weekdays and determine if that could be the cause for the changing house ads conversion rate.

Instructions

100 XP
  • Add a day of week column to the marketing DataFrame using dt.dayofweek based on the 'date_served' column.
  • Use conversion_rate to calculate conversion by the day of week and marketing channel and store the results in DoW_conversion.
  • Create a line plot of the results, set the y-axis to begin at 0 and display the plot.