The dataset given
The data available contains the following information, including the details of a sample of campaigns and coupons used in previous campaigns - User Demographic Details Campaign and coupon Details Product details Previous transactions
On the available data, aggregation, joining the data, adding new columns to get much meaningful results and other operations were performed. To work on data aggregation, indexing the data for faster retrieval, etc, XSV, Trifacta and Pandas were used.
XSV:https://github.com/BurntSushi/xsv Using XSV, each of the files were indexed and based on the matching columns, joins were performed on the data.
Trifacta https://www.trifacta.com/
Pandas A package in python used for manipulating data and analyzing it. Pandas are used
We downloaded the Kaggle set given to us- https://www.kaggle.com/vasudeva009/predicting-coupon-redemption
Performed EDA- Mentioned in the Jupyter Notebook- Assignment2_CleaningDigital_Marketing.ipynb
Performed Statistical Analysis and suggested Marketing Techniques like
Respose Measurements - Response_Measurement.ipynb
RFM Score- RFM_Score.ipynb
Customer LTV - Life_Time_Value.ipynb