In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain.
Description
Predicting customer's gender and age depending on mobile phone data
%0 Journal Article
%1 alzuabi2019predicting
%A AlZuabi, Ibrahim Mousa
%A Jafar, Assef
%A Aljoumaa, Kadan
%D 2019
%J J. Big Data
%K Age_prediction Big_data CDR Classification Customer_behavior Gender_prediction Machine_learning
%P 18
%R 10.1186/s40537-019-0180-9
%T Predicting customer's gender and age depending on mobile phone data
%U http://arxiv.org/abs/1903.06756
%V 6
%X In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain.
@article{alzuabi2019predicting,
abstract = {In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain.},
added-at = {2019-03-24T22:34:44.000+0100},
author = {AlZuabi, Ibrahim Mousa and Jafar, Assef and Aljoumaa, Kadan},
biburl = {https://www.bibsonomy.org/bibtex/2abd572fc0abc5aa3370d3881a0578565/ibrahimz},
description = {Predicting customer's gender and age depending on mobile phone data},
doi = {10.1186/s40537-019-0180-9},
interhash = {a529752476fe15670289f0afff358e3c},
intrahash = {abd572fc0abc5aa3370d3881a0578565},
journal = {J. Big Data},
keywords = {Age_prediction Big_data CDR Classification Customer_behavior Gender_prediction Machine_learning},
note = {cite arxiv:1903.06756},
pages = 18,
timestamp = {2019-03-24T22:34:44.000+0100},
title = {Predicting customer's gender and age depending on mobile phone data},
url = {http://arxiv.org/abs/1903.06756},
volume = 6,
year = 2019
}