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.
- Visualizing daily marketing reach
- Calculating conversion rate and retention rate
- Visualizing daily conversion rate
- Understanding marketing performance across various channels for cohorts of age groups
- Analyzing retention rates for the campaign
- Building functions to automate analysis
- Identifying and resolving inconsistencies
- Assessing bug impact
- 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