At XYZ we deal daily with a great amount of data from different sources (from web, shop, inventory, purchasing, logistics, finance, etc.), produced by the customer journey. In this challenge, we will focus on the sales data, produced by customers visiting and purchasing items from our web shop
- Describe the dataset and eventual anomalies you find.
- Which patterns do you find in the purchasing behavior of the customers?
- What are the categories and genres which customers are mostly interested in?
- Split customers in different groups based on their purchasing behavior. a. Justify your choice for your adopted method(s) and model(s). b. Describe the defined customer groups. What are the features which are driving the differentiation amongst the different groups? c. Give suggestions on how the business should treat these clusters differently.
- (optional) Assuming that the ‘Category_Reporting’ tells you the category of all the items in that order, predict: a. The number of items per category which will be ordered on a monthly basis for the rest of May 2021. b. The number of returns for the rest of May 2021.
- (optional) As, at this point in your analysis, you are the dataset expert, suggest any ideas (initiatives, further analyses) you might have in mind which can be helpful for the business.