On the Machine Learning Based Business Workflows Extracting Knowledge from Large Scale Graph Data

Musaoğlu, Mert; Bekler, Merve; Budak, Hüseyın; Akçelik, Celal; Aktaş, Mehmet S.

doi:10.1007/978-3-031-10548-7_34

Cited by 1 publication

(1 citation statement)

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“…If touchpoints can adapt to individual user behavior, conversion rates may increase. In practice, this usually entails generalizing across a population in order to infer individual behavior patterns [18]. Alfian et al [19] proposes a statistical model for extracting association rules from real-time online transactional data.…”

Section: B Conversionmentioning

confidence: 99%

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Ahlstrand¹,

Boldt²,

Borg³

et al. 2023

Linköping Electronic Conference Proceedings

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During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However, implementing and generating value from AI in the customer life cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both the model performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer life cycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regarding outcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating both policy outputs and outcomes to ensure reliable and trustworthy AI.

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Section: B Conversionmentioning

confidence: 99%