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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | AI-Driven Dynamic Pricing Models Using Machine Learning for Revenue Optimization |
| Authors Name: | Kanishk Singh |
| Download E-Certificate: | Download |
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IJRTI_210479
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| Published Paper Id: | IJRTI2605009 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | The increasing integration of artificial intelligence and machine learning into business operations has fundamentally redefined how organisations approach pricing strategy. This dissertation investigates the effectiveness of AI-driven dynamic pricing models in optimising revenue outcomes among Indian business practitioners, addressing a notable gap in primary empirical research within the Indian context. A quantitative, cross-sectional research design was employed, with structured survey data collected from 135 professionals spanning five industry sectors — e-commerce and retail, hospitality and travel, SaaS and technology, logistics, and related fields. Three core hypotheses were examined: that AI-powered dynamic pricing positively influences revenue optimisation, customer retention, and demand forecasting accuracy. Statistical analysis using Pearson correlation and simple linear regression yielded strong support for all three hypotheses. Gradient Boosting emerged as the most widely preferred algorithm among practitioners, followed by Random Forest and Deep Neural Networks, while Reinforcement Learning — despite its theoretical promise — remained comparatively underutilised. Descriptive analysis further revealed that while respondents broadly endorsed the operational benefits of AI pricing, perceptions of ethical transparency were notably more ambivalent, pointing to an important governance challenge for organisations scaling these systems. The findings collectively demonstrate that machine learning-driven dynamic pricing constitutes a strategically significant capability with measurable business impact across multiple performance dimensions. The study contributes original primary evidence to an underexplored domain and offers actionable guidance for practitioners, researchers, and policymakers navigating the expanding landscape of algorithmic pricing in India. |
| Keywords: | Dynamic Pricing, Artificial Intelligence, Machine Learning, Revenue Optimisation, Gradient Boosting, Demand Forecasting, Customer Retention, Algorithmic Pricing, Price Fairness, Indian E-commerce |
| Cite Article: | "AI-Driven Dynamic Pricing Models Using Machine Learning for Revenue Optimization", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a67-a74, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605009.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605009
Registration ID:210479
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a67-a74 Country: Gurugram, Haryana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605009 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605009 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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