AI-Augmented Big Data Analytics for Smart Retail Customer Behavior Prediction

Dileep Valiki

doi:10.15662/IJRAI.2023.0606031

Authors

Dileep Valiki Independent Researcher, India Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606031

Keywords:

Smart Retail Analytics, Real-Time Customer Prediction, AI-Driven Retail Systems, Streaming Data Architectures, Event-Based Processing, Customer Behavior Modeling, Retail Personalization Systems, Predictive Shopper Analytics, Wi-Fi Tracking Analytics, Sensor Data Processing, Social Media Analytics, Point-of-Sale Data, Real-Time Recommendation Systems, Retail Data Pipelines, AI-Augmented Analytics, Stream Processing Engines, Customer Journey Prediction, Omnichannel Analytics, Shopper Marketing Automation, Scalable Retail AI

Abstract

Smart retail relies on the efficient processing, storage, and analysis of vast data volumes to inform real-time business decisions. Despite substantial data generation capabilities within the smart retail domain, fast and effective predictive models remain scarce. Addressing this gap is of considerable economic importance due to the wealth of knowledge hidden in data. Real-time predictions of future customer behavior —such as intention to return to a store retailer, product choice, and channel of choice for the next visit— are enabled using time-stamped data from Wi-Fi tracking, infrared sensor, social media, and point-of-sale sources. The communication of real-time predictions is designed to seamlessly integrate back into store-systems that manage personalized advertising and recommendations.

An end-to-end, AI-augmented big-data-analytics pipeline is presented, powered by the capabilities of stream-processing architectures for event-based communication, and for processing velocity in the order of seconds for the behavior-prediction tasks. The proposed solution is validated in a wide variety of business contexts, including the personalization of advertising and recommendations at the store level, and the automation of shopper marketing. The core predictions —intention to return, preferred product, and preferred channel— exhibit tight integration with personal-store communication systems, and allow for non-obtrusive personalization at scale. As a whole, the solution leverages AI for the enhancement of big-data-analytics systems built upon event-based streaming architectures and open-source processing engines, and maps to the complex requirements of modern smart-retail environments.

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