TY - GEN
T1 - Process Mining-Driven Optimization of Digital Customer Journeys Based on Audience Intervention Using the AI-DATA Model
AU - Mühle, Heidrun
AU - Danter, Daniel
AU - Sandler, Simone
AU - Krauss, Oliver
AU - Stöckl, Andreas
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/11/6
Y1 - 2024/11/6
N2 - In a case study, we aimed to explore AI-based e-commerce optimization based on process mining by introducing the AI-DATA model, a human-centered approach to customer journey optimization in the context of trustworthy AI. The AI-DATA model, which stands for Awareness, Interest, Desire, Action, Trust, and Again, is a phase model. It is an extension of the traditional AIDA model, emphasizing a human-centered customer journey, that includes post-transaction activities and a cycle of repeat patronage. In our case study, the AI-DATA model was implemented at an e-commerce online shop specializing in nutrition supplements. The objective of employing this model through process mining was to increase conversion rates, such as clicks and purchases, through phase interventions.
AB - In a case study, we aimed to explore AI-based e-commerce optimization based on process mining by introducing the AI-DATA model, a human-centered approach to customer journey optimization in the context of trustworthy AI. The AI-DATA model, which stands for Awareness, Interest, Desire, Action, Trust, and Again, is a phase model. It is an extension of the traditional AIDA model, emphasizing a human-centered customer journey, that includes post-transaction activities and a cycle of repeat patronage. In our case study, the AI-DATA model was implemented at an e-commerce online shop specializing in nutrition supplements. The objective of employing this model through process mining was to increase conversion rates, such as clicks and purchases, through phase interventions.
KW - Process mining
KW - Mechatronics
KW - Computational modeling
KW - Electronic commerce
KW - Artificial intelligence
KW - Optimization
KW - Context modeling
KW - Artificial Intelligence
KW - Big Data
KW - Customer Journey
KW - Data Mining
KW - Digital communication systems
KW - Large Language Models
KW - Process Mining
KW - Trustworthy AI
UR - http://www.scopus.com/inward/record.url?scp=85215974001&partnerID=8YFLogxK
U2 - 10.1109/ICECCME62383.2024.10796188
DO - 10.1109/ICECCME62383.2024.10796188
M3 - Conference contribution
SN - 979-8-3503-9119-0
T3 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024
SP - 1
EP - 6
BT - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024
PB - IEEE
T2 - 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
Y2 - 4 November 2024 through 6 November 2024
ER -