Abstract:
In the rapidly evolving digital landscape, understanding customer journeys is paramount for businesses seeking to improve customer satisfaction and retention. This paper presents a novel approach to customer journey analytics through the application of process mining techniques, enhanced by GENAI agents and sequence-aware recommendation. By integrating process mining with customer journey mapping, we can unveil intricate patterns and behaviors that traditional methods may overlook. GENAI agents further enrich this analysis by leveraging advanced generative AI to provide deeper insights and predictive capabilities. Additionally, incorporating sequence-aware recommendation allows our model to consider the chronological order of customer interactions, capturing temporal dynamics and user behavior patterns to make more accurate and contextually relevant suggestions.
This study demonstrates the effectiveness of this combined model in capturing and analyzing real-time customer interactions across various touchpoints. We apply our approach to a case study within the retail industry, showcasing how businesses can leverage these insights to optimize their customer engagement strategies, identify bottlenecks, and enhance overall customer experience. The findings underscore the potential of GENAI agents, sequence-aware recommendation, and process mining as powerful tools in the realm of customer journey analytics, providing a data-driven foundation for strategic decision-making.
https://www.researchgate.net/publication/316521600_A_Process_Mining_Based_Model_for_Customer_Journey_Mapping
https://dl.acm.org/doi/abs/10.1145/3297280.3297288
https://journals.sagepub.com/doi/abs/10.1016/j.intmar.2018.02.001
https://scholar.google.com/scholar?q=research+papers+on+customer+journey+analytics&hl=en&as_sdt=0&as_vis=1&oi=scholart
https://www.adobe.com/content/dam/www/us/en/analytics/pdf/AA-Customer-Journey-Analytics.pdf
https://pure.rug.nl/ws/files/81733365/Understanding_Customer_Experience_Throughout_the_Customer_Journey.pdf