Configurable Process Mining for Context-Aware Customer Journey Analysis: A Simulation-Based Comparative Study
Abstract
Customer journeys frequently differ depending on the context. Customer experiences with a digital service can be influenced by factors such as location, delivery limitations, or payment methods. Process mining can reconstruct customer journeys from event logs. However, classical process discovery usually integrates all observed behavior into a single model, which may obscure context-specific variations. This study examines whether such variations can be represented more explicitly within a shared process structure using configurable process mining. A synthetic e-commerce event log was generated for two regional contexts with distinct shipping and payment policies, comprising 1,600 customer sessions, approximately 7,500 events, and 13–14 activity types. Using ProM, the Inductive Miner algorithm was applied to discover a global process model and region-specific process trees, which were then merged and configured into regional variants. Fitness and precision were used as the main evaluation metrics. In this controlled setting, the classical model achieved higher fitness (0.9438) but lower precision (0.7129), while the configured regional models achieved higher precision (0.996) with lower fitness (0.902). These findings suggest that configurable process mining may be more informative when the objective is to analyze contextual differences, whereas classical discovery remains useful for a broad understanding of the process. Real-world validation remains necessary to assess the robustness of the approach on operational customer journey logs.References
[1] K. N. Lemon and P. C. Verhoef, “Understanding Customer Experience Throughout the Customer Journey,” Journal of Marketing, vol. 80, no. 6, pp. 69–96, Nov. 2016, doi: 10.1509/jm.15.0420.
[2] M. S. Rosenbaum, M. L. Otalora, and G. C. Ramírez, “How to create a realistic customer journey map,” Business Horizons, vol. 60, no. 1, pp. 143–150, Jan. 2017, doi: 10.1016/j.bushor.2016.09.010.
[3] W. Van Der Aalst, “Process mining,” Commun. ACM, vol. 55, no. 8, pp. 76–83, Aug. 2012, doi: 10.1145/2240236.2240257.
[4] S. J. J. Leemans, D. Fahland, and W. M. P. Van Der Aalst, “Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour,” in Business Process Management Workshops, vol. 171, N. Lohmann, M. Song, and P. Wohed, Eds., in Lecture Notes in Business Information Processing, vol. 171. , Cham: Springer International Publishing, 2014, pp. 66–78. doi: 10.1007/978-3-319-06257-0_6.
[5] I. Mandic, “ENHANCING CUSTOMER JOURNEY THROUGH PROCESS MINING: A LITERATURE REVIEW AND FUTURE RESEARCH DIRECTIONS,” in Proceedings–The First International Conference FUTURE-BME 2024 (Forging the Future: Pioneering Approaches in Business, Management and, p. 987. Accessed: Dec. 02, 2025. [Online]. Available: https://www.researchgate.net/profile/Tamara-Gajic-2/publication/388626197_Permanent_Involvement_of_Digitization_and_Modern_Technology_in_the_Direction_of_Sustainable_Growth_in_the_Tourism_and_Hotel_Industry_of_Serbia/links/679f4e408311ce680c4b7f7f/Permanent-Involvement-of-Digitization-and-Modern-Technology-in-the-Direction-of-Sustainable-Growth-in-the-Tourism-and-Hotel-Industry-of-Serbia.pdf#page=1014
[6] R. Halvorsrud, F. Mannhardt, O. Prillard, and C. Boletsis, “Customer journeys and process mining–challenges and opportunities,” in ITM Web of Conferences, EDP Sciences, 2024, p. 05002. Accessed: Dec. 02, 2025. [Online]. Available: https://research.tue.nl/en/publications/customer-journeys-and-process-mining-challenges-and-opportunities
[7] B. Topaloglu, B. Oztaysi, and O. Dogan, “Cart-state-aware discovery of e-commerce visitor journeys with process mining,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 19, no. 4, pp. 2851–2879, 2024.
[8] A. Terragni and M. Hassani, “Analyzing Customer Journey with Process Mining: From Discovery to Recommendations,” in 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud), Aug. 2018, pp. 224–229. doi: 10.1109/FiCloud.2018.00040.
[9] A. Terragni and M. Hassani, “Optimizing customer journey using process mining and sequence-aware recommendation,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Limassol Cyprus: ACM, Apr. 2019, pp. 57–65. doi: 10.1145/3297280.3297288.
[10] I. Shafei, J. Karnon, and M. Crotty, “Process mining and customer journey mapping in healthcare: Enhancing patient-centred care in stroke rehabilitation,” DIGITAL HEALTH, vol. 10, p. 20552076241249264, Jan. 2024, doi: 10.1177/20552076241249264.
[11] G. Bernard and P. Andritsos, “A process mining based model for customer journey mapping,” in Forum and doctoral consortium papers presented at the 29th international conference on advanced information systems engineering (CAiSE 2017), CEUR Workshop Proceedings, 2017, pp. 49–56. Accessed: Dec. 02, 2025. [Online]. Available: https://serval.unil.ch/resource/serval:BIB_6FD456C4AB58.P001/REF.pdf
[12] M. La Rosa, M. Dumas, R. Uba, and R. Dijkman, “Business Process Model Merging: An Approach to Business Process Consolidation,” ACM Trans. Softw. Eng. Methodol., vol. 22, no. 2, pp. 1–42, Mar. 2013, doi: 10.1145/2430545.2430547.
[13] F. Gottschalk, W. M. P. Van Der Aalst, M. H. Jansen-Vullers, and M. La Rosa, “CONFIGURABLE WORKFLOW MODELS,” Int. J. Coop. Info. Syst., vol. 17, no. 02, pp. 177–221, Jun. 2008, doi: 10.1142/S0218843008001798.
[14] M. L. Rosa, W. M. P. V. D. Aalst, M. Dumas, and F. P. Milani, “Business Process Variability Modeling: A Survey,” ACM Comput. Surv., vol. 50, no. 1, p. 2:1-2:45, Mar. 2017, doi: 10.1145/3041957.
[15] N. Assy, W. Gaaloul, and B. Defude, “Mining Configurable Process Fragments for Business Process Design,” in Advancing the Impact of Design Science: Moving from Theory to Practice, vol. 8463, M. C. Tremblay, D. VanderMeer, M. Rothenberger, A. Gupta, and V. Yoon, Eds., in Lecture Notes in Computer Science, vol. 8463. , Cham: Springer International Publishing, 2014, pp. 209–224. doi: 10.1007/978-3-319-06701-8_14.
[16] E. B. Varga and A. Baksa, “Evaluating Process Discovery From Loop Structures,” Production Systems and Information Engineering, vol. 12, no. 2, pp. 23–39, Aug. 2024, doi: 10.32968/psaie.2024.2.2.
[17] A. Bogarín, R. Cerezo, and C. Romero, “Discovering learning processes using Inductive Miner: A case study with Learning Management Systems (LMSs),” Psicothema, vol. 3, no. 30, pp. 322–329, Aug. 2018, doi: 10.7334/psicothema2018.116.
[18] A. Augusto et al., “Automated Discovery of Process Models from Event Logs: Review and Benchmark,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 4, pp. 686–705, Apr. 2019, doi: 10.1109/TKDE.2018.2841877.
[19] A. J. Weijters, W. M. van Der Aalst, and A. A. De Medeiros, “Process mining with the HeuristicsMiner algorithm,” 2006, Accessed: Dec. 02, 2025. [Online]. Available: https://research.tue.nl/files/2388011/615595.pdf
[20] J. C. A. M. Buijs, B. F. Van Dongen, and W. M. P. Van Der Aalst, “On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery,” in On the Move to Meaningful Internet Systems: OTM 2012, vol. 7565, R. Meersman, H. Panetto, T. Dillon, S. Rinderle-Ma, P. Dadam, X. Zhou, S. Pearson, A. Ferscha, S. Bergamaschi, and I. F. Cruz, Eds., in Lecture Notes in Computer Science, vol. 7565. , Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 305–322. doi: 10.1007/978-3-642-33606-5_19.
[21] A. Augusto, A. Armas-Cervantes, R. Conforti, M. Dumas, and M. La Rosa, “Measuring fitness and precision of automatically discovered process models: A principled and scalable approach,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1870–1888, 2020.
[22] J. C. A. M. Buijs, B. F. Van Dongen, and W. M. P. Van Der Aalst, “Quality Dimensions in Process Discovery: The Importance of Fitness, Precision, Generalization and Simplicity,” Int. J. Coop. Info. Syst., vol. 23, no. 01, p. 1440001, Mar. 2014, doi: 10.1142/S0218843014400012.
[23] A. Augusto, R. Conforti, M. Dumas, and M. La Rosa, “Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs,” in 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA: IEEE, Nov. 2017, pp. 1–10. doi: 10.1109/ICDM.2017.9.
[24] E. Chlebus and J. Brazier, “Nonstationary poisson modeling of web browsing session arrivals,” Information Processing Letters, vol. 102, no. 5, pp. 187–190, 2007.
[25] N. Blenn and P. V. Mieghem, “Are human interactivity times lognormal?,” Jul. 11, 2016, arXiv: arXiv:1607.02952. doi: 10.48550/arXiv.1607.02952.
[26] S. Suriadi, R. Andrews, A. H. ter Hofstede, and M. T. Wynn, “Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs,” Information systems, vol. 64, pp. 132–150, 2017.
[27] H. M. Marin-Castro and E. Tello-Leal, “Event log preprocessing for process mining: a review,” Applied Sciences, vol. 11, no. 22, p. 10556, 2021.
[28] R. Conforti, M. La Rosa, and A. H. Ter Hofstede, “Filtering out infrequent behavior from business process event logs,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 2, pp. 300–314, 2016.
DOI:
https://doi.org/10.31449/inf.v50i14.14899Keywords:
process mining, customer journey map, configurable process mining, variability modeling, context-aware analyticsDownloads
Published
Issue
Section
License
Authors retain copyright in their work. By submitting to and publishing with Informatica, authors grant the publisher (Slovene Society Informatika) the non-exclusive right to publish, reproduce, and distribute the article and to identify itself as the original publisher.
All articles are published under the Creative Commons Attribution license CC BY 3.0. Under this license, others may share and adapt the work for any purpose, provided appropriate credit is given and changes (if any) are indicated.
Authors may deposit and share the submitted version, accepted manuscript, and published version, provided the original publication in Informatica is properly cited.
