A Machine Learning Case Study (KDD Cup 2009)

The KDD Cup 2009 challenge was to predict, from customer data provided by the French Telecom company Orange, the urge of customers to switch providers (churn), buy new products or services (appetency), or buy upgrades or add-ons (up-selling).

Definitions (from KDD Cup webpage):

● Churn : Churn rate is also sometimes called attrition rate. It is one of two primary factors that determine the steady-state level of customers a business will support. In its broadest sense, churn rate is a measure of the number of individuals or items moving into or out of a collection over a specific period of time. The term is used in many contexts, but is most widely applied in business with respect to a contractual customer base. For instance, it is an important factor for any business with a subscriber-based service model, including mobile telephone networks and pay TV operators. The term is also used to refer to participant turnover in peer-to-peer networks.

● Appetency: In our context, the appetency is the propensity to buy a service or a product.

● Up-selling: Up-selling is a sales technique whereby a salesman attempts to have the customer purchase more expensive items, upgrades, or other add-ons in an attempt to make a more profitable sale. Up-selling usually involves marketing more profitable services or products, but up-selling can also be simply exposing the customer to other options he or she may not have considered previously. Up-selling can imply selling something additional, or selling something that is more profitable or otherwise preferable for the seller instead of the original sale.

It is a classification based problem containing two classes +1 and -1 for each of the tasks i.e churn, appetency and up-selling.

The performances are evaluated according to the arithmetic mean of the AUC for the three tasks (churn, appetency. and up-selling). This is what we call "Score".

Goal: to get the best possible Score.