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Explainable Model Fusion for Customer Journey Mapping.

Kotaro Okazaki1,2, Katsumi Inoue1,3

  • 1Department of Informatics, School of Multidisciplinary Sciences, The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan.

Frontiers in Artificial Intelligence
|June 1, 2022
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Summary

This study introduces explainable alignment to automate customer journey mapping (CJM) for AI-driven marketing. It enhances strategic decision-making by bridging human and AI perspectives for better problem formulation.

Keywords:
Boolean networkLFITXAIcustomer journey mappinggenerative modelmarketingover-the-top media servicesprocess mining

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Area of Science:

  • Artificial Intelligence
  • Marketing Science
  • Human-Computer Interaction

Background:

  • The adoption of AI in industry is hindered by its 'black box' nature, impacting trustworthiness and fairness in decision-making.
  • Strategic decision-making in global marketing faces challenges due to diverse environments and the difficulty of AI in problem formulation.
  • High-stakes marketing decisions require robust methods for problem definition, which current AI struggles to provide.

Purpose of the Study:

  • To address the challenge of AI in problem formulation for marketing strategy.
  • To propose a novel method for automating customer journey mapping (CJM) through explainable alignment.
  • To enhance strategic decision-making in marketing by integrating human and AI perspectives.

Main Methods:

  • Developed a customer journey mapping (CJM) automation system using model-level data fusion.
  • Employed latent Dirichlet allocation and variational autoencoder for transparent latent variable analysis.
  • Integrated hidden Markov models, interpretation transition learning, and long short-term memory for sequential data analysis and attitude rule extraction.

Main Results:

  • Successfully automated CJM generation using domain-specific inputs and observations.
  • Achieved explainable alignment, reconciling human and AI viewpoints in problem formulation.
  • Demonstrated the application of the human-AI system on real-world data from over-the-top media services.

Conclusions:

  • Explainable alignment offers a practical solution for AI-driven problem formulation in marketing.
  • Automated CJM enhances strategic decision-making by providing transparent insights into customer dynamics.
  • The proposed human-AI system effectively extracts dynamic customer behaviors for improved marketing strategies.