Understand data better and extract insights from it to provide insight for decision-makers to improve company marketing and increase sales.
Also, showcase how can adding a recommender engine help to increase the company sales.
- COUNTRY - How many customers from different country , dose profit change?
- QUANTITY - How dose quantity trend change based on date?
- PROFIT - How was the profit for this year based on months and days?
- PRODUCTS - What is most sold products in the store?
This analysis is on Online_Retail_II dataset provided by UCI Link.
The dataset contain transactions occurring for a UK-based and registered online shop , The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.
The dataset contains the following variables:
| Variable | Description |
|---|---|
| InvoiceNo | 6-digit integral number uniquely assigned to each transaction. |
| If this code starts with the letter 'c', it indicates a cancellation | |
| StockCode | Product (item) code |
| Description | Product (item) name |
| Quantity | quantities of each product (item) per transaction |
| InvoiceDate | Invice date and time |
| UnitPrice | Product price per unit in sterling (£) |
| CustomerID | Customer number/Id |
| Country | country where a customer resides |
You can access the dataset directly from data folder in this repository.
- Online Retail R Markdown.
- Online Retail html.
- Online Retail ppt slide for presentation.