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<p>In recent years, the growing amount of event-log data collected from websites and mobile applications has provided an opportunity to analyoc2vec to cluster physical ze user-behavior patterns in digital customer systems. However, a holistic data-driven approach for analyzing the digital customer journey is lacking. To address this gap, we propose a method for analyzing large amounts of digital event logs to derive actionable insights related to customers’ digital journeys, in order to improve user experience, satisfaction, and loyalty, particularly in high-stakes domains like healthcare. The proposed method identifies the most relevant types of customer journeys via the latent Dirichlet allocation (LDA) algorithm and its hierarchical version (HLDA). The proposed method is executed by generating topics from sequences of events that are transformed into session clusters. We then capture sequential behavioral patterns among and within the clusters and further model them as Markov chains. The results of this method for various applications are presented. We demonstrate the most representative paths and detect customers’ actions with a high probability of potential undesirable outcomes. We utilize a unique real-world dataset of event logs extracted from a mobile application of a large health maintenance organization (HMO). This dataset includes over 120,000 active customers who generated more than 5.5&#xa0;million events per day. This research makes three main contributions: )i) we propose a novel methodology that integrates established techniques (LDA/hLDA, Markov chain analysis, and leakage/irregularity detection) for analyzing digital customer journeys at various granularity levels to simplify the variability of the customer journey; (ii) we show how topic-based session summarization reduces complexity and supports customer journey’s sequential diagnostics, for example by detecting irregularities of unique behavioral patterns that would otherwise be difficult to detect; and (iii) we demonstrate the framework on a unique real-world HMO dataset that enables the implementation of the method to extract actionable and updated insights important for the digital healthcare domain. Finally, we discuss the emerging role of Artificial Intelligence and Large Language Models in digital customer journey analysis.</p>

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Analyzing the digital customer journey: a novel framework for sequential behavior modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月23日

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  • Lihi Bar-El,
  • Hila Chalutz-BenGal,
  • Sivan Gazit,
  • Tal Patalon,
  • Irad Ben-Gal

摘要

In recent years, the growing amount of event-log data collected from websites and mobile applications has provided an opportunity to analyoc2vec to cluster physical ze user-behavior patterns in digital customer systems. However, a holistic data-driven approach for analyzing the digital customer journey is lacking. To address this gap, we propose a method for analyzing large amounts of digital event logs to derive actionable insights related to customers’ digital journeys, in order to improve user experience, satisfaction, and loyalty, particularly in high-stakes domains like healthcare. The proposed method identifies the most relevant types of customer journeys via the latent Dirichlet allocation (LDA) algorithm and its hierarchical version (HLDA). The proposed method is executed by generating topics from sequences of events that are transformed into session clusters. We then capture sequential behavioral patterns among and within the clusters and further model them as Markov chains. The results of this method for various applications are presented. We demonstrate the most representative paths and detect customers’ actions with a high probability of potential undesirable outcomes. We utilize a unique real-world dataset of event logs extracted from a mobile application of a large health maintenance organization (HMO). This dataset includes over 120,000 active customers who generated more than 5.5 million events per day. This research makes three main contributions: )i) we propose a novel methodology that integrates established techniques (LDA/hLDA, Markov chain analysis, and leakage/irregularity detection) for analyzing digital customer journeys at various granularity levels to simplify the variability of the customer journey; (ii) we show how topic-based session summarization reduces complexity and supports customer journey’s sequential diagnostics, for example by detecting irregularities of unique behavioral patterns that would otherwise be difficult to detect; and (iii) we demonstrate the framework on a unique real-world HMO dataset that enables the implementation of the method to extract actionable and updated insights important for the digital healthcare domain. Finally, we discuss the emerging role of Artificial Intelligence and Large Language Models in digital customer journey analysis.