Optimizing Customer Engagement and Business Growth in the Photography Industry Through Predictive AI-Powered Analytics

Liaqat, Raazia (2025) Optimizing Customer Engagement and Business Growth in the Photography Industry Through Predictive AI-Powered Analytics. Masters thesis, Dublin, National College of Ireland.

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Abstract

The photography industry is experiencing a rapid digital transformation, increasingly influenced by artificial intelligence (AI) technologies that shape user behaviour, enable personalization, and impact business performance. Despite these advancements, many creative professionals particularly photographers lack data-driven tools to predict customer engagement or tailor content effectively. This practicum proposes a modular AI framework designed to analyse user interaction with visual content, predict engagement behaviour, and generate personalized outputs to enhance business outcomes. The solution integrates OpenCV for analysing aesthetic and compositional features, Scikit-learn for behavioural prediction using machine learning models, and Power BI for visualizing performance and engagement metrics.

The methodology follows a Design Science Research (DSR) approach, ensuring iterative development and validation through a multi-layered evaluation strategy. This includes analysing engagement patterns, assessing prediction accuracy, and measuring visual relevance to user preferences. By bridging the gap between creative expression and predictive analytics, the project delivers a practical, adaptable tool for photographers, portfolio-based creatives, and visually driven businesses. It empowers users to make informed, data-supported decisions, improving customer experience and driving sustainable growth in the competitive digital marketplace.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Del Rosal, Victor UNSPECIFIED
Uncontrolled Keywords:	Artificial Intelligence (AI); Customer Engagement; Photography Industry; Predictive Analytics; Computer Vision; Machine Learning; Design Science Research (DSR)
Subjects:	T Technology > TR Photography Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HF Commerce > Marketing > Consumer Behaviour Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	24 Aug 2026 10:26
Last Modified:	24 Aug 2026 10:26
URI:	https://norma.ncirl.ie/id/eprint/9589

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