aKite - From RFM (Recency, Frequency, Monetary) to Machine Learning

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.