{
    "data_id": "42774",
    "name": "KDDCup09-Appetency",
    "exact_name": "KDDCup09-Appetency",
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    "description": "This is the full version of the KDD Cup 2009 dataset\n\n**This Year's Challenge**\n\nCustomer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offers the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling).\n\nThe most practical way, in a CRM system, to build knowledge on customer is to produce scores. A score (the output of a model) is an evaluation for all instances of a target variable to explain (i.e. churn, appetency or up-selling). Tools which produce scores allow to project, on a given population, quantifiable information. The score is computed using input variables which describe instances. Scores are then used by the information system (IS), for example, to personalize the customer relationship. An industrial customer analysis platform able to build prediction models with a very large number of input variables has been developed by Orange Labs. This platform implements several processing methods for instances and variables selection, prediction and indexation based on an efficient model combined with variable selection regularization and model averaging method. The main characteristic of this platform is its ability to scale on very large datasets with hundreds of thousands of instances and thousands of variables. The rapid and robust detection of the variables that have most contributed to the output prediction can be a key factor in a marketing application.\n\nThe challenge is to beat the in-house system developed by Orange Labs. It is an opportunity to prove that you can deal with a very large database, including heterogeneous noisy data (numerical and categorical variables), and unbalanced class distributions. Time efficiency is often a crucial point. Therefore part of the competition will be time-constrained to test the ability of the participants to deliver solutions quickly.\n\n**Task Description**\n\nThe task is to estimate the churn, appetency and up-selling probability of customers, hence there are three target values to be predicted. The challenge is staged in phases to test the rapidity with which each team is able to produce results. A large number of variables (15,000) is made available for prediction. However, to engage participants having access to less computing power, a smaller version of the dataset with only 230 variables will be made available in the second part of the challenge.\n\n* Churn (wikipedia definition): 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.\n\n* Appetency: In our context, the appetency is the propensity to buy a service or a product.\n\n* Up-selling (wikipedia definition): 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.\n\nThe training set contains 50,000 examples. The first predictive 14,740 variables are numerical and the last 260 predictive variables are categorical. The last target variable is binary (-1,1).",
    "format": "arff",
    "uploader": "Marcos de Paula Bueno",
    "uploader_id": 11601,
    "visibility": "public",
    "creator": "\"Orange Labs\"",
    "contributor": null,
    "date": "2020-12-18 17:40:23",
    "update_comment": null,
    "last_update": "2020-12-18 17:40:23",
    "licence": "Public",
    "status": "active",
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    "url": "https:\/\/www.openml.org\/data\/download\/22044882\/dataset",
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