Analyzing Marketing Campaigns with Pandas

The analysis of fake marketing dataset based on the data of an online subscription business is documented in Analyzing_Marketing_Campaigns_with_pandas.ipynb

Business questions like "How did this campaign perform?", "Which channel is referring the most subscribers?", "Why is a particular channel underperforming?" are answered using data from the analysis. This project is build on Python and pandas fundamentals, such as merging/slicing datasets, groupby(), correcting data types and visualizing results using matplotlib.

Analysis consisted of following activities (in sequence)

1. Visualizing daily marketing reach
2. Calculating conversion rate and retention rate
3. Visualizing daily conversion rate
4. Understanding marketing performance across various channels for cohorts of age groups
5. Analyzing retention rates for the campaign
6. Building functions to automate analysis
7. Identifying and resolving inconsistencies
8. Assessing bug impact
9. Personalisation of A/B test

• Test allocation
• Calculating list and significance testing
• Evaluating using t-test using 'stats.ttest_ind' from the scipy library
• Building and testing of A/B test segmenting function