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AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond

Krishnamurty Raju Mudunuru1 , Rajesh Remala2 , Sevinthi Kali Sankar Nagarajan3

  1. Independent Researcher, San Antonio, Texas, USA.
  2. Independent Researcher, San Antonio, Texas, USA.
  3. Independent Researcher, San Antonio, Texas, USA.

Section:Research Paper, Product Type: Journal Paper
Volume-12 , Issue-5 , Page no. 11-18, May-2024

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v12i5.1118

Online published on May 31, 2024

Copyright © Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan, “AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.5, pp.11-18, 2024.

MLA Style Citation: Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan "AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond." International Journal of Computer Sciences and Engineering 12.5 (2024): 11-18.

APA Style Citation: Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan, (2024). AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond. International Journal of Computer Sciences and Engineering, 12(5), 11-18.

BibTex Style Citation:
@article{Mudunuru_2024,
author = {Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan},
title = {AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2024},
volume = {12},
Issue = {5},
month = {5},
year = {2024},
issn = {2347-2693},
pages = {11-18},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5686},
doi = {https://doi.org/10.26438/ijcse/v12i5.1118}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i5.1118}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5686
TI - AI-Driven Data Analytics Unveiling Sales Insights from Demographics and Beyond
T2 - International Journal of Computer Sciences and Engineering
AU - Krishnamurty Raju Mudunuru, Rajesh Remala, Sevinthi Kali Sankar Nagarajan
PY - 2024
DA - 2024/05/31
PB - IJCSE, Indore, INDIA
SP - 11-18
IS - 5
VL - 12
SN - 2347-2693
ER -

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Abstract

Leveraging a synthesis of literature review and case studies, it illuminates how AI empowers organizations to discern intricate patterns and correlations within vast datasets. Through sophisticated algorithms and machine learning techniques, AI facilitates the nuanced understanding of the interplay between consumer demographics and purchasing behaviors, enabling targeted marketing strategies. Moreover, the study extends beyond demographics, encompassing psychographic, geographic, and behavioral factors through the amalgamation of diverse data sources. By employing predictive modeling, AI enables businesses to forecast market trends, optimize product positioning, and deliver personalized customer capabilities. Ethics around artificial intelligence-driven data analytics, incorporating consumer discretion and algorithmic fairness, are also addressed. Transparent methodologies and regulatory compliance are emphasized as crucial elements in fostering trust and mitigating risks. This paper explores the utilization of AI-driven data analytics in uncovering profound sales insights derived not only from demographics but also from diverse sources beyond traditional parameters. Machine learning deeper into consumer behavior patterns, market trends, and socio-economic indicators to gain a comprehensive understanding of their target audience. The paper discusses various methodologies employed in AI-driven data analytics, including predictive modeling, clustering techniques, and sentiment analysis, to extract valuable sales insights. Furthermore, it shows how crucial it is to incorporate data from various sources, including social media, geospatial information, and transactional records, to enrich the analytical process and enhance the accuracy of predictive models. Through real-world case studies and examples, this paper demonstrates how AI-driven data analytics can empower businesses to optimize their sales strategies, personalize marketing campaigns, and identify untapped market opportunities. By leveraging the capabilities of AI, organizations can move beyond traditional demographic segmentation and uncover nuanced insights that drive competitive advantage and foster sustainable growth.

Key-Words / Index Term

AI-driven data analytics, sales insights, demographics, machine learning, predictive modeling, consumer behavior, market trends, socio-economic indicators, personalized marketing

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