This notebook aims at analyzing the content of a real E-Commerce database that lists purchases made by 25,500 customers over a period from November 2018 to April 2019. Based on this analysis, I develop a RFM model that allows to anticipate the purchases that will be made by a new customer, during the following year and this, from its first purchase. RFM (Recency Frequency Monetary) analysis or RFM segmentation is an effective marketing technique to identify your best customers. Instead of reaching out to 100% of your audience, target only specific customer segments that can prove beneficial for your business in future. Deduct most recent purchase date from today to calculate the recency value.

Olist is the largest eCommerce website in Brazil. It connects small retailers from all over the country to sell directly to customers. The business has generously shared a large dataset containing 110k orders on its site from 2016 to 2018. The SQL-style relational database includes customers and their orders in the site, which contains around 100k unique orders and 73 categories. It also includes item prices, timestamps, reviews, and gelocation associated with the order.

Traditionally, the clustering of customers and the choice of the most relevant promotions are based on "RFM" parameters. Where R stands for Recency, i.e. how recent the last purchase was, F for Frequency, the number of purchases per month/year (or the average days between one and another) and M for Monetary lastly indicates the average value of each purchase. With traditional information systems, it was important to describe purchasing behavior with few significant data, in order to make it easy to write computer programs able to make the best decisions for each customer. It is now possible to do much more by way of Machine Learning (ML), a subset of Artificial Intelligence (AI). By including additional data such as, for example, the detailed purchase history of each customer, the reaction to previous promotions and, where available, data such as age, sex, profession, ... recommendation lists can be created (who bought product A, often also buys product B) used by the most advanced e-commerce sites.