CUSTOMER SEGMENTATION USING MACHINE LEARNING & HYBRID MODEL
Prof Rajendra Arakh
, Raja Shoaib , Prof. Sweta Kriplani , Vanshika Yadav , Swechchha Agrawal
predictive features, data analysis, machine learning models, artificial intelligence, K-means clustering, marketing effectiveness, hierarchical cluster, DBSCABN.
Customer segmentation is a fundamental strategy for businesses seeking to tailor their offerings effectively to diverse consumer needs. Utilizing machine learning techniques alongside a hybrid model approach presents a promising avenue to enhance the precision and efficiency of segmentation processes. This abstract outlines a comprehensive framework for customer segmentation that integrates traditional clustering algorithms with advanced machine learning methods. The hybrid model merges both supervised and unsupervised learning approaches to optimize segmentation results, leveraging both labeled and unlabeled data to improve accuracy while accommodating the dynamic nature of customer preferences. By combining unsupervised techniques such as k-means clustering with supervised algorithms like decision trees or support vector machines, the model can identify natural groupings within the data and refine segment definitions based on domain knowledge and business goals. This adaptive capability ensures that segmentation remains relevant and actionable, enabling businesses to develop targeted marketing strategies, personalized product recommendations, and enhanced customer experiences. Ultimately, customer segmentation using a hybrid model approach empowers businesses to gain deeper insights into their customer base, foster long-term loyalty, and drive sustainable growth in competitive markets.
"CUSTOMER SEGMENTATION USING MACHINE LEARNING & HYBRID MODEL", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 6, page no.956 - 965, June-2024, Available :https://ijsdr.org/papers/IJSDR2406110.pdf
Volume 9
Issue 6,
June-2024
Pages : 956 - 965
Paper Reg. ID: IJSDR_211798
Published Paper Id: IJSDR2406110
Downloads: 000347134
Research Area: Science & Technology
Country: Jabalpur , Madhya Pradesh , India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
