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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

Creating a DataFrame for house ads

The house ads team is concerned because they've seen their conversion rate drop suddenly in the past few weeks. In the previous exercises, you confirmed that conversion is down because you noticed a pattern around language preferences.

As a data scientist, it is your job to provide your marketing stakeholders with as specific feedback as possible as to what went wrong to maximize their ability to correct the problem. It is vital that you not only say "looks like there's a language problem," but instead identify what the problem is specifically so that the team doesn't repeat their mistake.

Instructions

100 XP
  • Use np.where() to create a new column in house_ads called 'is_correct_lang' whose values are 'Yes' if 'language_displayed' is equal to 'language_preferred' and 'No' otherwise.
  • Group by date_served and is_correct_lang to get a daily count of the ads served.