Enhancing Consumer Behavior and Experience Through AI-Driven Insights Optimization-Reference-Cited by-同舟云学术

Enhancing Consumer Behavior and Experience Through AI-Driven Insights Optimization

Published:2024-03-01 Issue: Volume: Page:1-35
ISSN:2327-5502
Container-title:Advances in Marketing, Customer Relationship Management, and E-Services
language:
Short-container-title:

Author:

Naveeenkumar N.¹,Rallapalli Sreekanth²^ORCID,Sasikala K.³,Priya P. Vidhya⁴^ORCID,Husain Jakeer⁵,Boopathi Sampath⁶^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Muthayammal Engineering College (Autonomous), Namakkal, India

2. Department of MCA, Nitte Meenakshi Institute of Technology, Bengaluru, India

3. Department of Information Technology, R.P. Sarathy Institute of Technology, Salem, India

4. Department of Management Studies, Kongu Engineering College, Perundurai, Erode, India

5. Department of Applied Sciences, Faculty of Engineering and Technology KBNU Kalaburagi, India

6. Mechanical Engineering, Muthayammal Engineering College (Autonomous), Namakkal, India

Abstract

This chapter delves into the role of AI-driven behavioral analytics in understanding, predicting, and enhancing consumer experiences. It highlights the integration of behavioral analytics, consumer experiences, and predictive modeling in reshaping market dynamics. The chapter explains the fundamental components of behavioral analytics, emphasizing its significance in understanding consumer preferences and decision-making processes. It also discusses the impact of AI-powered predictive analytics on consumer experiences, anticipating behaviors and fostering proactive strategies. It addresses ethical concerns like data privacy and algorithmic biases. The chapter provides a guide for practitioners, researchers, and businesses to harness AI's transformative potential in contemporary markets.

Publisher

IGI Global

Reference57 articles.

1. Agarwal, A., Singhal, C., & Thomas, R. (2021). AI-powered decision making for the bank of the future. Academic Press.

2. GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia

3. Linking sustainable product attributes and consumer decision-making: Insights from a systematic review

4. Experiential Marketing

5. Machine learning and AI in business intelligence: Trends and opportunities.;J. P.Bharadiya;International Journal of Computer,2023

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transforming Hospitality, Personalized Medicine, and Adaptive Learning;Advances in Medical Technologies and Clinical Practice;2024-09-27

2. Artificial Intelligence-Enhanced Nanomedicine Design and Deep Reinforcement Learning in Pharmacokinetics;Advances in Medical Technologies and Clinical Practice;2024-09-27

3. Study on AI-Powered Advanced Drug Discovery for Enhancing Privacy and Innovation in Healthcare;Advances in Medical Technologies and Clinical Practice;2024-09-27

4. IoT and Blockchain-Based Smart Grid Energy Management;Advances in Civil and Industrial Engineering;2024-08-30

5. Harnessing Biochar, Biofuels, and Solar Cells;Advances in Chemical and Materials Engineering;2024-08-16