Leveraging Deep Learning for Customer Segmentation: Patterns and Preferences Unveiled

Authors

  • Chitralekha Navneet Kumar Assistant Professor- Research , Prin. L. N. Welingkar Institute of Management Development and Research Matunga, Mumbai, Maharashtra
  • Ramchandra Vasant Mahadik Associate Professor, Bharati Vidyapeeth(Deemed to be University) Institute of Management and Entrepreneurship Development,Pune-411038
  • Sangeeta Paliwal University Librarian, Department- Central Library University and department – Symbiosis International University
  • Pallavi Sajanapwar Professor and Deputy Director, Indira institute of Management, Pune
  • Chaitali B. Kasar Assistant Professor, Department of Electronics and Telecommunication Engineering Dr. D. Y. Patil Institute of Technology, Pune. 411018
  • Manisha Tejas Chordiya Shingvi Founder , CMD UBT TECHNOLOGY PVT LTD

Keywords:

Deep Learning, Customer Segmentation, Neural Networks, Customer Behaviour, Personalization, Marketing Strategy

Abstract

It is crucial to comprehend and accommodate consumers' varied preferences and behaviours in the dynamic world of modern business. The revolutionary effects of deep learning on consumer segmentation are examined in this study, which also provides a thorough overview of the approaches, techniques, results, constraints, and potential future applications of this developing area.In the past, client segmentation depended on simple clustering methods and fundamental demographic characteristics. However, the development of deep learning has ushered in a new era by allowing businesses to explore complex patterns and preferences concealed within sizable and unstructured datasets. In order to put the deep learning revolution in historical perspective, our inquiry starts with an examination of conventional and machine learning-based segmentation techniques. We explore the possibilities of neural embeddings, RNNs, and unsupervised learning, emphasising their efficiency in simulating consumer preferences and behaviour. In the context of consumer segmentation, we also look at the possibility of deep reinforcement learning, hybrid methods, and transfer learning.Even while deep learning has a lot of potential, it is not without difficulties, such as computing demands and sensitivity to data noise. However, its scope is broad, encompassing anything from dynamic behaviour modelling to image-based segmentation. This paper gives businesses the knowledge they need to use deep learning to uncover complex patterns and preferences within their customer base, ultimately fostering more individualised and successful marketing strategies. Businesses are increasingly looking for data-driven insights to gain a competitive edge.

Downloads

Download data is not yet available.

References

P. Greenspan et al., "Nile Red: A selective fluorescent stain for intracellular lipid droplets", J. Cell Biol., vol. 100, no. 3, pp. 965-73, 1985.

P. Walker et al., "Practice guidelines for the renal biopsy", Mod Pathol., vol. 17, no. 12, pp. 1555-1563, 2004.

K. Zou et al., "Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index", Acad. Radiol, vol. 11, no. 2, pp. 178-189, 2004.

SM Sheehan et al., "Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning", Am. J. Physiol. Renal Physiol., vol. 315, pp. 1644-1651, 2018.

S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.

Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.

Potnurwar, A. V. ., Bongirwar, V. K. ., Ajani, S. ., Shelke, N. ., Dhone, M. ., & Parati, N. . (2023). Deep Learning-Based Rule-Based Feature Selection for Intrusion Detection in Industrial Internet of Things Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 23–35.

K. Agnihotri, P. Chilbule, S. Prashant, P. Jain and P. Khobragade, "Generating Image Description Using Machine Learning Algorithms," 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP), Nagpur, India, 2023, pp. 1-6, doi: 10.1109/ICETET-SIP58143.2023.10151472.

C. Jayapandian et al., "Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains", Kidney International, vol. 99, no. 1, pp. 86-101, 2021.

M. Bende, M. Khandelwal, D. Borgaonkar and P. Khobragade, "VISMA: A Machine Learning Approach to Image Manipulation," 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1-5, doi: 10.1109/ISCON57294.2023.10112168.

P. Khobragade, P. Ghutke, V. P. Kalbande and N. Purohit, "Advancement in Internet of Things (IoT) Based Solar Collector for Thermal Energy Storage System Devices: A Review," 2022 2nd International Conference on Power Electronics &IoT Applications in Renewable Energy and its Control (PARC), Mathura, India, 2022, pp. 1-5, doi: 10.1109/PARC52418.2022.9726651.

M. Hermsenet. al, "Deep Learning–Based Histopathologic Assessment of Kidney Tissue", J. Am. Soc. Nephrol., vol. 30, no. 10, pp. 1968-1979, 2019.

D. Muruve, "The biobank for the molecular classification of kidney disease: research translation and precision medicine in nephrology", BMC Nephrol., vol. 18, no. 1, pp. 252, 2017.

P. K. Khobragade and L. G. Malik, "Data Generation and Analysis for Digital Forensic Application Using Data Mining," 2014 Fourth International Conference on Communication Systems and Network Technologies, Bhopal, India, 2014, pp. 458-462, doi: 10.1109/CSNT.2014.97.

L. C. Chen, G. Papandreou, F. Schroff and H. Adam, "Rethinking atrous convolution for semantic image segmentation", arxiv preprint, 2017.

B. R. Kiran, I. Sobh, V. Talpaert et al., "Deep reinforcement learning for autonomous driving: A survey", IEEE Transactions on Intelligent Transportation Systems, 2021.

ChanduVaidya, PrashantKhobragade and AshishGolghate, "Data Leakage Detection and Security in Cloud Computing", GRD JournalsGlobal Research Development Journal for Engineering, vol. 1, no. 12, November 2016.

L. Barisoni et al., "Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology", Clinical Kidney Journal, vol. 10, no. 2, pp. 176-187, 2017.

L. Oni et al., "Inter-observer variability of the histological classification of lupus glomerulonephritis in children", Lupus, vol. 26, no. 11, pp. 1205-11, 2017.

W. Kang et al., "The Comparative Research on Image Segmentation Algorithms", First International Workshop on Education Technology and Computer Science, pp. 703-707, 2009.

R. T. Hadke and P. Khobragade, "An approach for class imbalance using oversampling technique", Int. J. Innov. Res. Comput. Commun. Eng., vol. 3, no. 11, pp. 11451-11455, 2015.

L. Chen, G. Papandreou, I. Kokkinos, K. Murphy and A. L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs", IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 4, pp. 834-848, 2017.

C. Yu, J. Liu, S. Nemati and G. Yin, "Reinforcement learning in healthcare: A survey", ACM Computing Surveys (CSUR), vol. 55, no. 1, pp. 1-36, 2021.

MrunalGirhepunje, Simran Jain, TriveniRamteke, Nikhil P. Wyawahare, PrashantKhobragade and SampadaWazalwar, "Proposed Crowd Counting system and Social Distance Analyzer for Pandemic Situation", International Conference on Computational Intelligence - ICCI 2021, 27-28 December 2021, ISSN 2524-7573.

K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, "Deep reinforcement learning: A brief survey", IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26-38, 2017.

Z. Q. Tian et al., "Multi-step medical image segmentation based on reinforcement learning", Journal of Ambient Intelligence and Humanized Computing, pp. 1-12, 2020.

PrashantKhobragade, Latesh G. Malik,“A Review on Data Generation for Digital Forensic Investigation using Datamining”, IJCAT International Journal of Computing and Technology, Volume 1, Issue 3, April 2014.

Downloads

Published

02.02.2024

How to Cite

Navneet Kumar, C. ., Vasant Mahadik, R. ., Paliwal, S. ., Sajanapwar, P. ., B. Kasar, C. ., & Chordiya Shingvi, M. T. . (2024). Leveraging Deep Learning for Customer Segmentation: Patterns and Preferences Unveiled. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 408–417. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4677

Issue

Vol. 12 No. 14s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)