Advancing Customer Experience Personalization with AI-Driven Data Engineering: Leveraging Deep Learning for Real-Time Customer Interaction

Abstract

In today’s customer-centric market, offering integrated and seamless customer experiences strengthens business performance. One of the essential components of this journey is to personalize these experiences using data-driven methodologies. The objective of our research focuses on leveraging AI-driven data engineering to enhance customer experience personalization. An illustrative approach focuses on integrating deep learning methodologies for more meaningful interactions in real time. Measurable results display the effectiveness of AI to learn, understand, and derive optimal experiences tailored to every customer’s needs based on event data recording human interactions.

In this digital world, a personalized experience makes an emotional connection and a competitive difference. However, with the large and multi-modal data channels and volume of data, data creation consumes excessive manual efforts and is scarce for an enterprise. Only a small percentage of customers’ data is used for personalization. Our objective is to leverage AI-driven data engineering to advance the personalization of the customer experience. We exemplify this by integrating deep learning into a big data environment to enhance and elevate the personalization of customer interactions. Measurable results show that our AI-driven approach can personalize the response.

By combining and leveraging big data engineering in the data frame for modeling and analysis, we showcase the power of AI in tailoring experiences that are fitted to every customer using subsequent procedures that take structured events to match a narrative experience determined by unsupervised learning of dynamic customer journeys and map these dynamic clusters to customer answer sentiments. We demonstrate the proposed illustrative proof of method omnichannel, AI-driven, and deep learning modular-driven framework to process structured logs and the propagation of AI-driven business value improvement in a global and growing company by using the deep learning modeling improvements in the architecture of the algorithm. Finally, we show the effectiveness of deep learning by monitoring and measuring the chatbot performance when the relevant audience size grows exponentially. Our PM sets out end-to-end data engineering bundles with deep learning-driven data prep tasks across the three multi-modal data channels. Data and tools can be tailored to a company’s application landscape and technologies.