KDDCup09_upselling
OpenML dataset with id 44158
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22103283/KDDCup09_upselling.arff
Upload date: 10 July 2022
Dataset Characteristics
Number of classes: 2
Number of features: 46 (numeric: 34, symbolic: 12 and in total binary: 4 )
Number of instances: 5,032
Number of instances with missing values: 0
Number of missing values: 0
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark,
transformed in the same way. This dataset belongs to the "classification on categorical and
numerical features" benchmark. Original description:
Author: Source: Unknown - Date unknown Please cite:
Datasets from ACM KDD Cup (http://www.sigkdd.org/kddcup/index.php)
KDD Cup 2009 http://www.kddcup-orange.com
Converted to ARFF format by TunedIT
Customer 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).
The 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.
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.
The training set contains 50,000 examples.
The first predictive 190 variables are numerical and the last 40 predictive variables are categorical.
The last target variable is binary {-1,1}.
This page was built for dataset: KDDCup09_upselling
