Modeling and Personalising the Customer Journey: The Case for Case Management

Benzarti, Imen; Mili, Hafedh; Carvalho, Renata Medeiros de

doi:10.1109/edoc52215.2021.00019

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…On the other hand, the current CJM has some shortcomings that should be improved. Since the modeling of consumer behavior is always provisional, the essence of strategic decision-making in marketing is in the consensus between the decision-makers' beliefs and contexts (Benzarti et al, 2021 ). There is always a conflict between quantitative efficiency management and qualitative structural understanding.…”

Section: Introductionmentioning

confidence: 99%

Explainable Model Fusion for Customer Journey Mapping

Okazaki

Inoue

2022

Front. Artif. Intell.

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Due to advances in computing power and internet technology, various industrial sectors are adopting IT infrastructure and artificial intelligence (AI) technologies. Recently, data-driven predictions have attracted interest in high-stakes decision-making. Despite this, advanced AI methods are less often used for such tasks. This is because AI technology is a black box for the social systems it is meant to support; trustworthiness and fairness have not yet been established. Meanwhile in the field of marketing, strategic decision-making is a high-stakes problem that has a significant impact on business trends. For global marketing, with its diverse cultures and market environments, future decision-making is likely to focus on building consensus on the formulation of the problem itself rather than on solutions for achieving the goal. There are two important and conflicting facts: the fact that the core of domestic strategic decision-making comes down to the formulation of the problem itself, and the fact that it is difficult to realize AI technology that can achieve problem formulation. How can we resolve this difficulty with current technology? This is the main challenge for the realization of high-level human-AI systems in the marketing field. Thus, we propose customer journey mapping (CJM) automation through model-level data fusion, a process for the practical problem formulation known as explainable alignment. Using domain-specific requirements and observations as inputs, the system automatically outputs a CJM. Explainable alignment corresponds with both human and AI perspectives and in formulating the problem, thereby improving strategic decision-making in marketing. Following preprocessing to make latent variables and their dynamics transparent with latent Dirichlet allocation and a variational autoencoder, a post-hoc explanation is implemented in which a hidden Markov model and learning from an interpretation transition are combined with a long short-term memory architecture that learns sequential data between touchpoints for extracting attitude rules for CJM. Finally, we realize the application of human-AI systems to strategic decision-making in marketing with actual logs in over-the-top media services, in which the dynamic behavior of customers for CJM can be automatically extracted.

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Section: Introductionmentioning

confidence: 99%

Explainable Model Fusion for Customer Journey Mapping

Okazaki

Inoue

2022

Front. Artif. Intell.

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Involving users in the development of a modeling language for customer journeys

et al. 2023

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Although numerous methods for handling the technical aspects of developing domain-specific modeling languages (DSMLs) have been formalized, user needs and usability aspects are often addressed late in the development process and in an ad hoc manner. To this concern, this paper presents the development of the customer journey modeling language (CJML), a DSML for modeling service processes from the end-user’s perspective. Because CJML targets a wide and heterogeneous group of users, its usability can be challenging to plan and assess. This paper describes how an industry-relevant DSML was systematically improved by using a variety of user-centered design techniques in close collaboration with the target group, whose feedback was used to refine and evolve the syntax and semantics of CJML. We also suggest how a service-providing organization may benefit from adopting CJML as a unifying language for documentation purposes, compliance analysis, and service innovation. Finally, we distill what we learned into general lessons and methodological guidelines.

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Modeling and Personalising the Customer Journey: The Case for Case Management

Cited by 2 publications

References 19 publications

Explainable Model Fusion for Customer Journey Mapping

Explainable Model Fusion for Customer Journey Mapping

Involving users in the development of a modeling language for customer journeys

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