AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights

October 2024
Vol-10, Issue-5
Paper ID: 25086
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Commerce and Management
Keywords
AI-Driven Predictive Analytics Market Strategy Consumer Insights Machine Learning Data-Driven Decision-Making Real-Time Customer Experience
Abstract
The research paper examines how the predictive analytics added by artificial intelligence (AI) changes commerce for better market strategies and deeper consumer insights as newly AI enabled algorithms can now almost instantaneously provide businesses with an analysis of massive and highly peculiar data sets, identify constructive patterns and more accurately forecast consumer behaviour to individually personalize marketing campaigns, optimize inventory management, or deliver client tailored product recommendations – these referred business approaches are identified to significantly skyrocket customer engagement levels through a more granular unbeatable solutions since AI such as machine learning or natural language processing (NLP) methods help to better capture evolving snapshots on changing trends, sentiments or consumption habits enabling boards with richer yet timely analytical descriptions that support executive-based decisions about pricing tactics, promotional activities or goods aligned design choices - thereby realizing improved customer experience frameworks, where firms obtain immediate feedbacks allowing them to adapt fast on-demand services especially productive within electronic retailing conditions where competitive advantages depend greatly on if managers foresee consumer requests well ahead & can contend immediately; notwithstanding all this potential benefit the research also discusses ethical reflections including privacy loss prevention requirements over extensive automation use in learning models making us mode aware that proper AI deployment transparently should be maintained proposing accounting models that skillfully leverage new findings into operational profit while common good practices ensuring accuracy & equity stand against discrimination obstacles guaranteed postures needed for continuous gains producing crucial layman results however academic community for vista factual improvements.

Author Information

# Name Institute / Affiliation
1 Chetana Guest Lecture in Commerce Government First Grade College, Mudgal Affiliated to Raichur University

How to Cite

Use the following formats to cite this article in your research.

APA Style
Chetana (2024). AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights. International Journal of Advance Research and Innovative Ideas In Education, 10(5), 1334-1343.
MLA Style
Chetana. "AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, 2024, pp. 1334-1343.
IEEE Style
Chetana, "AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, pp. 1334-1343, 2024.
Vancouver Style
Chetana. AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(5):1334-1343.
Harvard Style
Chetana (2024) 'AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights', International Journal of Advance Research and Innovative Ideas In Education, 10(5), pp. 1334-1343.
Chicago Style
Chetana. "AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 1334-1343.
Turabian Style
Chetana. "AI-Driven Predictive Analytics in Commerce: Enhancing Market Strategy and Consumer Insights." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 1334-1343.

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