Advances in Consumer Research
Issue 2 : 873-878
Original Article
Machine Learning for Product Development: Predicting Consumer Preferences and Market Trends
 ,
 ,
 ,
1
Assistant Professor, Department of Management Studies, Middle East College, Muscat, Oman
2
Senior Lecturer, Department of Management Studies, Middle East College, Muscat, Oman
Abstract

Product administrators may now make better choices about costs, advertising, and development of products thanks to machine learning (ML), which is transforming analytical forecasting. They may search through enormous databases for unseen relationships or trends using this equipment, which gives them fresh perspectives on how decisions are made. A person pursuing a career in machine learning product management has to be well-versed in statistical analysis, mathematics, and the constraints of adaptive programming. To predict future trends and make better judgments about managing products, advanced machine learning algorithms can evaluate previous sales data. Additionally, they may use consumer preferences for items to tailor recommendations to make items and services stand out and inspire fresh, clever ideas for internet marketing. Managers of products may also benefit from using machine learning technologies to set goals for developing products and discover features that consumers find most useful.

Keywords
Recommended Articles
Research Article
Predictive Insights: Leveraging Artificial Intelligence for Strategic Business Decision-Making
...
Published: 21/10/2025
Research Article
Consumer Protection against Misleading Advertisements and Deceptive Branding: Interplay between Consumer Law and Intellectual Property Rights in India
...
Published: 21/10/2025
Research Article
Deep Learning-Based Consumer Preference Prediction System for Personalized Digital Campaigns
...
Published: 19/10/2025
Research Article
Competition Law and Market Regulation: Managerial Challenges and Strategic Opportunities
...
Published: 21/10/2025
Loading Image...
Volume 2, Issue 2
Citations
86 Views
115 Downloads
Share this article
© Copyright Advances in Consumer Research