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Potential Customer Classification in Customer Relationship Management Using Fuzzy Logic

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Innovative Data Communication Technologies and Application (ICIDCA 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 46))

Abstract

Customer Relationship Management systems are one of the most significant determinants to maximize sales in any business domain. In the process of CRM, the important step after targeting a lead is to convert these leads into actual customers. In the current CRM system, depending on the lead score, the lead is projected as a potential customer. The paper proposes the application of fuzzy logic in the Customer Relationship Management systems to get the prospects of how potential a lead is to become a customer based on some of the factors that involves the interaction between the lead and the business domain. Fuzzy logic approach is mainly used to identify the important leads, who have the potential to increase the future sales of the business.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Sardar Patel Institute of Technology, Mumbai, India

    Tanay Kulkarni, Purnima Mokadam, Jnanesh Bhat & Kailas Devadkar

Authors
  1. Tanay Kulkarni
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  2. Purnima Mokadam
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  3. Jnanesh Bhat
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  4. Kailas Devadkar
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Corresponding author

Correspondence to Tanay Kulkarni .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, India

    Jennifer S. Raj

  2. College of Computer Engineering and Sciences, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi Arabia

    Abul Bashar

  3. Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, USA

    S. R. Jino Ramson

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Kulkarni, T., Mokadam, P., Bhat, J., Devadkar, K. (2020). Potential Customer Classification in Customer Relationship Management Using Fuzzy Logic. In: Raj, J., Bashar, A., Ramson, S. (eds) Innovative Data Communication Technologies and Application. ICIDCA 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-030-38040-3_7

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