AI for Real-Time Personalization in Digital Marketing Campaigns

Abstract

Real-time personalisation of digital marketing efforts presents a significant barrier that necessitates the utilisation of sophisticated AI methodologies to assess varied and evolving customer data. This research presents a platform that integrates Encoding Categorical Features, Deep Learning Feature Extraction, and Neural Networks to provide actionable, real-time personalisation. Encoding categorical characteristics, including demographics, preferences, and behavioural patterns with one-hot and target encoding guarantees data integrity and significant representation for machine learning models. Deep learning feature extraction improves the process by recognising high-level patterns in unstructured data, such as textual reviews and visual content, that older algorithms may neglect. The framework's foundation lies in neural networks that forecast personalised recommendations by identifying non-linear relationships and individual user preferences, hence guaranteeing precise and pertinent results. The proposed method, assessed using actual marketing datasets, shows substantial enhancements in customer engagement, click-through rates, and return on investment (ROI). This research emphasises the revolutionary capacity of AI in enhancing digital marketing campaigns via real-time, data-driven personalisation.