ItemBook Chapter

Enhancing customer satisfaction through artificial neural networks (ANNS): Principles, architectures, and practical applications

Title
Enhancing customer satisfaction through artificial neural networks (ANNS): Principles, architectures, and practical applications
Creator
Grewal, Diva Kaur; Senthilkumaran, Vanishree; Hridhya, P.K.
Description
Artificial neural networks (ANNs) are a powerful paradigm in AI, inspired by the complex structure of the human brain. Their design mirrors biological neural networks that govern the human nervous system, allowing ANNs to excel in tasks from pattern recognition to decision-making. This chapter explores the foundational principles of ANNs, highlighting the interplay between neuroscience and computer science, where digital systems replicate or sometimes surpass human cognitive abilities. The study examines ANNs in various customer-focussed applications, where businesses can leverage ANNs to increase customer satisfaction by predicting and influencing consumer behaviour. This chapter provides an overview of the core principles, architectures, and broad applications of ANNs. It seeks to offer historical insights, understanding the evolution of ANNs and their mathematical foundations. The research explores the building blocks of neural networks, including neurons, layers, and activation functions, and their importance in pattern recognition and information processing. Special emphasis is given to popular architectures like feedforward, recurrent, and convolutional neural networks. ANNs' versatility is demonstrated through surveys of their applications in finance, robotics, and healthcare. This chapter addresses real-world challenges and potential solutions in these domains. This chapter serves as a valuable resource for researchers and practitioners interested in understanding ANNs, how they work, their development, and future advancements in the field. It discusses ANNs' impacts in finance, healthcare, and robotics, alongside ethical considerations. This original contribution provides fresh insights into ANNs, making it valuable for those exploring this emerging topic. 2025 Diva Kaur Grewal, Vanishree Senthilkumaran and Hridhya P. K.. Published under exclusive licence by Emerald Publishing Limited. All rights reserved.
Source
Marketing Intelligence, Part A: Understanding Customers in the Era of Digitalization;pp.103-119
Date
01-01-2025
Publisher
Emerald Publishing
Subject
Architecture; Building blocks; Diverse domains; Field; Neural network; Neuroscience
Coverage
Grewal D.K., Christ University, India; Senthilkumaran V., Christ University, India; Hridhya P.K., Christ University, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-183549418-9; 978-183549419-6;
Format
online
Language
English
Type
Book chapter

Citation

Grewal, Diva Kaur; Senthilkumaran, Vanishree; Hridhya, P.K., “Enhancing customer satisfaction through artificial neural networks (ANNS): Principles, architectures, and practical applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24270.

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