International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Automated Feature Selection and Churn Prediction using Deep Learning Models V. Umayaparvathi1, K. Iyakutti2 Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India Professor-Emeritus, Department of Physics and Nanotechnology, SRM University, Chennai, Tamilnadu, India
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Abstract
– In this competitive world, mobile
Therefore, companies are going behind introducing new state of the art applications and technologies to offer their customers as much better services as possible so as to retain them intact. Before doing so, it is necessary to identify those customers who are likely to leave the company in the near future in advance because losing them would results in significant loss of profit for the company. This process is called Churn Prediction. Data mining techniques are found to be more effective in predicting customer churn from the researches carried out during the past few years. The construction of effective churn prediction model is a significant task which involves lots of research right from the identification of optimal predictor variables (features) from the large volume of available customer data to the selection of effective predictive data mining technique that is suitable for the feature set. Telecom Industries collect a voluminous amount of data regarding customers such as Customer Profiling, Calling pattern, Democratic data in addition to the network data that are generated by them. Based on the history of the customers calling pattern and the behavior, there is a possibility to identify their mindset of either they will leave or not. The efficiency of any churn prediction model depends highly on the selection of customer attributes (feature selection) from the dataset for its model construction. These traditional methods have two major problems: 1) With hundreds of customer attributes, existing manual feature engineering process is very tedious and time consuming and often performed by a domain expert: 2) Often it is tailored to specific dataset, hence we need to repeat the feature engineering process for different datasets. Since deep learning algorithms automatically comes up with good features and representation for the input data, we investigated their applications for customer churn prediction problem. We developed three deep neural network architectures and built the corresponding churn prediction model using two telecom dataset. Our experimental results show that deep-learning based models are performing as good as traditional classification models, without even using the hand-picked features. The rest of the paper is organized as follows. In Section 2, we review the existing predictive models proposed in the literature for churn prediction. In Section 3, we present the details about deep-learning networks. In section 4, we present the architecture of the proposed deep-
telecommunications market tends to reach a saturation state and faces a fierce competition. This situation forces the telecom companies to focus their attention on keeping the customers intact instead of building a large customer base. According to telecom market, the process of subscribers (either prepaid or postpaid) switching from a service provider is called customer churn. Several predictive models have been proposed in the literature for churn prediction. The efficiency of any churn prediction model depends highly on the selection of customer attributes (feature selection) from the dataset for its model construction. These traditional methods have two major problems: 1) With hundreds of customer attributes, existing manual feature engineering process is very tedious and time consuming and often performed by a domain expert: 2) Often it is tailored to specific dataset, hence we need to repeat the feature engineering process for different datasets. Since deep learning algorithms automatically comes up with good features and representation for the input data, we investigated their applications for customer churn prediction problem. We developed three deep neural network architectures and built the corresponding churn prediction model using two telecom dataset. Our experimental results show that deep-learning based models are performing as good as traditional classification models, without even using the hand-picked features. Key Words: Customer relationship management (CRM), Data mining, Churn prediction, Predictive models, and Deep learning.
1. INTRODUCTION Today is the competitive world of communication technologies. Customer Churn is the major issue that almost all the Telecommunication Industries in the world faces now. In telecommunication paradigm, Churn is defined to be the activity of customers leaving the company and discarding the services offered by it due to dissatisfaction of the services and/or due to better offering from other network providers within the affordable price tag of the customer. This leads to a potential loss of revenue/profit to the company. Also, it has become a challenging task to retain the customers.
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