Online retail is a booming industry nowadays with every major player having an online storefront. Over the last decade, consumers have steadfastly embraced online shopping. To improve customer engagement and thereby revenue and profits, data-driven targeted marketing has proved to be the most effective way forward. Customer segmentation, Churn modeling, and Market Basket analysis are employed widely to help businesses thrive in the online retail domain. We wanted to explore the same and we have picked a dataset from the UK, where the first inklings of online shopping are said to have begun, back in the late 1970s.
This project consists of five Jupyter Notebooks.
- Data Preprocessing
- Data Visualization
- Clustering
- Association Rule Mining
- Customer Segmentation
Each one of the above techniques was applied to the Online Retail Data Set, which was obtained from the UCI Machine Learning repository. The data contains information about transnational transactions for a UK-based and registered non-store online retail. Link to the dataset - https://archive.ics.uci.edu/ml/datasets/online+retail. The dataset consists of 541909 records.