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A novel hybrid intelligent technique to enhance customer relationship management in online food delivery system

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Abstract

Customer Relationship Management (CRM) has gained more attention due to customer satisfaction based on management decisions. However, customer review maintenance is challenging in the management field because of the structured and unstructured data. This paper proposes a novel Generalized Savitzky-Golay Filter (GS-GF) and Hybrid Self Constructing Neural Fuzzy based African Buffalo Optimization (HSCFN-ABO) techniques for maintaining customer reviews; the customer’s reaction may be positive, negative, or neutral. This novel technique provides a solution for classifying customer comments based on the specified problem, including food quality, food delivery, and payment issues. Initially, pre-processing and feature extraction is performed using the novel GS-GF approach. Once the features are extracted from the dataset, they enter the classification layer to value customer reviews using the novel HSCFN-ABO replica. The execution of this research is done using the MATLAB R2018b platform. The proposed HSCFN-ABO classifier method in CRM is tested using the real dataset from swiggy. CRM performance for customer review data using the proposed technique is validated with different case studies. Furthermore, the projected HSCFN-ABO method is compared with various other traditional methods in terms of accuracy, precision, and F measure, proving the significance of the HSCFN-ABO classifier in CRM applications.

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  1. Department of Management, BIT Mesra Campus, Ranchi, Jharkhand, 835215, India

    Rohini Jha

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  1. Rohini Jha
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Correspondence to Rohini Jha.

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Jha, R. A novel hybrid intelligent technique to enhance customer relationship management in online food delivery system. Multimed Tools Appl 81, 28583–28606 (2022). https://doi.org/10.1007/s11042-022-12877-1

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