Analyzing Marketing Campaigns with pandas
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Learn to measure marketing campaigns in pandas: conversion and retention rates, channel attribution, and A/B test lift, using a mock subscription dataset.
Marketing campaign data raises practical questions: how did a campaign perform, which channel brings in the most subscribers, and why does one channel fall short? You work through them in Python on a mock dataset modeled on an online subscription business. Each question becomes a measurable figure such as a conversion or retention rate. You also trace a drop in conversions to its cause and read A/B test results with segmentation in mind.
What You Will Achieve
- Calculate conversion and retention rates for a campaign and compare them across user segments such as language
- Prepare raw marketing records for analysis by fixing column types, merging tables and deriving date fields
- Write reusable Python functions for conversion analysis and use them to explain why a channel’s conversions dropped
- Measure lift and statistical significance for an A/B test, then check whether the result holds within each segment
Program Overview
pandas Basics and Marketing Metrics
Reviews loading and reshaping the campaign data in pandas, then calculates core marketing metrics and plots them for different user segments.
- Importing, merging and retyping the dataset
- Daily marketing reach by channel
- Conversion and retention rates
- Segmentation by language and age group
Conversion Attribution and Automated Analysis
Builds functions that automate conversion analysis, then uses them to explain why some channels converted below normal in late January.
- Conversion and plotting functions
- House ads conversion inconsistencies
- Conversion indexes and user preferences
- Assessing the impact of a bug
Personalization A/B Testing
Analyzes a personalization A/B test and applies segmentation when interpreting its results.
- Key metrics and test allocation
- Lift and significance testing
- Segmenting A/B test results
Certificate Awarded (Paid)
Job Outlook
These skills are used in marketing analytics work, where analysts measure how campaigns and channels perform, check campaign data for errors, and evaluate experiments such as A/B tests.
Good for: Learners who want to see how data analysis in Python answers everyday marketing questions about campaigns, channels and experiments.
Keep in mind: Every exercise uses one mock dataset modeled on a subscription business, so the worked answers fit that setting rather than your own campaign data.
Bottom line: A case-based pandas course that follows one mock campaign through its metrics, a conversion bug investigation and a segmented A/B test.
Frequently Asked Questions
Is the campaign data in the course real?
No. The lessons use a mock marketing dataset modeled on an online subscription business. It records users by marketing channel, language and age group, and includes house ads, which become the focus of the conversion investigation.
What problem does the house ads investigation uncover?
Some channels converted below their usual rate in late January. Comparing house ads conversion by language, you identify inconsistencies, confirm a house ads error, resolve the inconsistencies with conversion indexes and assess the bug's impact.
Which library is used for the charts?
The plots are made with matplotlib. They show daily marketing reach by channel, conversion rate by language, daily conversion rates and campaign results, and a later lesson turns the plotting into a reusable function.
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