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Iranian Churn Dataset Data Set
Download: Data Folder, Data Set Description
Abstract: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months.
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Data Set Characteristics: |
Multivariate |
Number of Instances: |
3150 |
Area: |
Business |
Attribute Characteristics: |
Integer |
Number of Attributes: |
13 |
Date Donated |
2020-04-09 |
Associated Tasks: |
Classification, Regression |
Missing Values? |
N/A |
Number of Web Hits: |
64269 |
Source:
Ruholla Jafari-Marandi <rojafari '@' calpoly.edu>
Industrial and Manufacturing Engineering Department, Cal Poly, San Luis Obispo, CA 93407, USA
Data Set Information:
This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months. A total of 3150 rows of data, each representing a customer, bear information for 13 columns. The attributes that are in this dataset
are call failures, frequency of SMS, number of complaints, number of distinct calls, subscription length, age group, the charge amount, type of service, seconds of use, status, frequency of use, and Customer Value.
All of the attributes except for attribute churn is the aggregated data of the first 9 months. The churn labels are the state of the customers at the end of 12 months. The three months is the designated planning gap.
Attribute Information:
Anonymous Customer ID
Call Failures: number of call failures
Complains: binary (0: No complaint, 1: complaint)
Subscription Length: total months of subscription
Charge Amount: Ordinal attribute (0: lowest amount, 9: highest amount)
Seconds of Use: total seconds of calls
Frequency of use: total number of calls
Frequency of SMS: total number of text messages
Distinct Called Numbers: total number of distinct phone calls
Age Group: ordinal attribute (1: younger age, 5: older age)
Tariff Plan: binary (1: Pay as you go, 2: contractual)
Status: binary (1: active, 2: non-active)
Churn: binary (1: churn, 0: non-churn) - Class label
Customer Value: The calculated value of customer
Relevant Papers:
- Jafari-Marandi, R., Denton, J., Idris, A., Smith, B. K., & Keramati, A. (2020). Optimum Profit-Driven Churn Decision Making: Innovative Artificial Neural Networks in Telecom Industry. Neural Computing and Applications.
- Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014). Improved churn prediction in telecommunication industry using data mining techniques. Applied Soft Computing, 24, 994-1012.
- Keramati, A., & Ardabili, S. M. (2011). Churn analysis for an Iranian mobile operator. Telecommunications Policy, 35(4), 344-356.
Citation Request:
Cite the latest publication: Jafari-Marandi, R., Denton, J., Idris, A., Smith, B. K., & Keramati, A. (2020). Optimum Profit-Driven Churn Decision Making: Innovative Artificial Neural Networks in Telecom Industry. Neural Computing and Applications.
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