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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

Building an A/B test segmenting function

In the previous lesson, you observed that your personalization experiment is highly statistically significant. However, when running experiments, it is important to check how new features are affecting specific demographics. Sometimes features that are highly appealing to one group are less appealing to others.

Since you want to segment our data multiple times, you will build a function ab_segmentation() that analyzes the impact of your A/B tests on segments of data that you can reuse each time you want to conduct this kind of analysis.

Your function will take in a column name and run through each unique value in that column calculating lift and statistical significance.

Instructions

100 XP
  • Build a for loop within our ab_segmentation() function that runs through each unique value in the user-inputted column name segment.
  • Isolate the rows in marketing where the marketing channel is 'Email' and the user-inputted column segment equals subsegment.
  • Print the results of the lift() and stats.ttest_ind() functions.