AI-Powered Big Data Analytics for Smart Retail Customer Journey Optimization

Madhu Sathiri

doi:10.15662/IJRAI.2023.0606030

Authors

Madhu Sathiri Independent Researcher, India Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606030

Keywords:

Retail Analytics, Predictive Analytics, Customer Behavior, Personalization, Omnichannel Retail, Consumer Insights, Digital Retail Transformation, Data-Driven Decision Making, Customer Engagement, Retail Optimization, Experience Management, Intelligent Retail Systems, Consumer Journey Analytics, Retail Data Integration, AI-driven Marketing, Customer Lifetime Value, Retail Intelligence Platforms, Behavioral Analytics, Decision Support Systems, Retail Innovation

Abstract

Intensifying of competition among modern retail companies necessitates a transition from big data to enhanced intelligence through optimization of the still-unknown customer journey by means of artificial intelligence and business analytics. Such a transition calls for a rethinking of conceptual foundations and development of a data ecosystem. Data constitute a fundamental part of modern intelligent systems. The new-age retail environment produces tremendous amounts and varieties of data about customer experiences during their journey, both online and offline. A model-based customer-journey-mapping and touchpoint-optimization technique and its supporting architecture are presented. Other needed technologies encompass a wide spectrum of artificial intelligence techniques for machine learning, deep learning, and natural language processing. Regulatory and ethical aspects of the exploitation of such a data ecosystem support the smart retail business.

Customer journey mapping defines all stages of customer experience with a company or brand and includes its online and offline components. Every stage encompasses several touchpoints for customer experience assessment: awareness, consideration, intent, evaluation, purchase, consumption, and advocacy. For a comprehensive customer-journey model, advanced customer experience metrics are required in addition to those for each touchpoint: net promoter score, customer satisfaction score, customer lifetime value, customer engagement score, and customer effort score. All customer-journey metrics determine the optimization of touchpoint impact on profit.

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