Work2018

Optimizing the prediction of telemarketing target calls by a classification technique

Stéphane Cédric Koumetio Tekouabou · Walid Cherif · Hassan Silkan

20 citations0/0 files storedInternational Conference on Wireless Networks and Mobile Communications (WINCOM)

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Customer churn and segmentation Data Mining and Machine Learning Applications Financial Distress and Bankruptcy Prediction

Abstract

This paper presents a new classification technique to optimize the prediction of telemarketing target calls for selling bank long-term deposits. A Portuguese retail bank addressed, from 2008 until 2013, data on its clients, products and social-economic attributes including the effects of the financial crisis. An original set of 150 features has been explored and 21 features are retained for the proposed approach. This paper introduces a new technique that implicitly fosters most significant features and predicts the classes of clients according to the types of these features. Moreover, experiments showed that either these features are normalized or not, the proposed technique proved stable and accurate. To evaluate the obtained results, this paper compares them to those of most known machine learning models: Naive Bayes (NB), Decision Trees (DT), Artificial Neural Network (ANN) and Support Vector Machines (SVM). Thus, the proposed approach yielded the best performance in terms of f-measure, and it allowed reaching 60.12% of the subscribers.

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References

2008Application of data mining techniques in customer relationship management: A literature review and classification 2014A data-driven approach to predict the success of bank telemarketing 2011Using data mining for bank direct marketing: an application of the CRISP-DM methodology 2005Linking innovative product development with customer knowledge: a data-mining approach 2015An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour 2014Bank Direct Marketing Analysis of Data Mining Techniques 2014Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns 2014Feature selection with data balancing for prediction of bank telemarketing

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2021Identifying Long-Term Deposit Customers: A Machine Learning Approach

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