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Analyzing Marketing Campaigns with pandas

Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!

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4 Hours14 Videos53 Exercises17,647 Learners4500 XPMarketing Analytics with Python Track

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Course Description

One of the biggest challenges when studying data science technical skills is understanding how those skills and concepts translate into real jobs. Whether you're looking to level up in your marketing job by incorporating Python and pandas or you're trying to get a handle on what kinds of work a data scientist in a marketing organization might do, this course is a great match for you. We'll practice translating common business questions into measurable outcomes, including "How did this campaign perform?", "Which channel is referring the most subscribers?", "Why is a particular channel underperforming?" and more using a fake marketing dataset based on the data of an online subscription business. This course will build on Python and pandas fundamentals, such as merging/slicing datasets, groupby(), correcting data types and visualizing results using matplotlib.
  1. 1

    Pandas

    Free

    In this chapter, you will review pandas basics including importing datasets, exploratory analysis, and basic plotting.

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    Introduction to pandas for marketing
    50 xp
    Importing the dataset
    100 xp
    Examining the data
    100 xp
    Data types and data merging
    50 xp
    Updating the data type of a column
    100 xp
    Adding new columns
    100 xp
    Date columns
    100 xp
    Initial exploratory analysis
    50 xp
    Daily marketing reach by channel
    100 xp
    Visualizing daily marketing reach
    100 xp
  2. 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.

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    Introduction to common marketing metrics
    50 xp
    Calculating conversion rate
    100 xp
    Calculating retention rate
    100 xp
    Customer segmentation
    50 xp
    Comparing language conversion rate (I)
    100 xp
    Comparing language conversion rate (II)
    100 xp
    Aggregating by date
    100 xp
    Plotting campaign results (I)
    50 xp
    Visualize conversion rate by language
    100 xp
    Creating daily conversion rate DataFrame
    100 xp
    Setting up our data to visualize daily conversion
    100 xp
    Visualize daily conversion rate
    100 xp
    Plotting campaign results (II)
    50 xp
    Marketing channels across age groups
    100 xp
    Grouping and counting by multiple columns
    100 xp
    Analyzing retention rates for the campaign
    100 xp
  3. 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.

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    Building functions to automate analysis
    50 xp
    Building a conversion function
    100 xp
    Test and visualize conversion function
    100 xp
    Plotting function
    100 xp
    Putting it all together
    100 xp
    Identifying inconsistencies
    50 xp
    House ads conversion rate
    100 xp
    Analyzing House ads conversion rate
    100 xp
    House ads conversion by language
    100 xp
    Creating a DataFrame for house ads
    100 xp
    Confirming house ads error
    100 xp
    Resolving inconsistencies
    50 xp
    Setting up conversion indexes
    100 xp
    Analyzing user preferences
    100 xp
    Creating a DataFrame based on indexes
    100 xp
    Assessing bug impact
    100 xp
  4. 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.

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    A/B testing for marketing
    50 xp
    Determining key metrics
    50 xp
    Test allocation
    100 xp
    Comparing conversion rates
    100 xp
    Calculating lift & significance testing
    50 xp
    Creating a lift function
    100 xp
    Evaluating statistical significance
    50 xp
    A/B testing & segmentation
    50 xp
    Building an A/B test segmenting function
    100 xp
    Using your segmentation function
    100 xp
    Wrap-up
    50 xp

In the following tracks

Marketing Analytics with Python

Datasets

Marketing dataset 1Marketing dataset 2

Collaborators

Mona Khalil
Sumedh Panchadhar

Prerequisites

Data Manipulation with pandas
Jill Rosok Headshot

Jill Rosok

Data Scientist at Spotify

Jill is a Data Scientist at Spotify.
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