Implementing a Process Mining Framework to Improve Customer Experience
Main Article Content
Keywords
Process Mining, Customer Experience, Decision Making, Performance Metrics, Process Discovery
Abstract
Improving Customer Experience (CX) is a strategic priority for organisations. This study develops and evaluates a Process Mining framework that extracts operational performance metrics and interpretable insights from event logs to support evidence-based decisions for optimizing CX. The framework encompasses the discovery of visual process models, cycle time measurement, analysis of operational flows and frequencies, identification of recurring bottlenecks, and evaluation of model accuracy. Applied to three datasets (Sepsis Events, BPI Challenge, and Hospital Billing), the results showed strong model accuracy, ranging from 88% for BPI Challenge to 100% for Hospital Billing. Additionally, the analysis uncovered efficiency variations, with cycle times spanning 19.4 hours to 149.2 days, and flagged notable delays, such as over 700 hours in administrative duties. These findings affirm the framework’s ability to offer in-depth insights, helping organisations identify key obstacles and take purposeful steps to elevate service quality and customer contentment.
References
Ashrafi, N., Abdollahi, A., Placencia, G., & Pishgar, M. (2024). Effect of a process-mining-based pre-processing step in prediction of the critical health outcomes [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2407.02821
Bakhshi, A., Hassannayebi, E., & Sadeghi, A. H. (2023). Optimizing sepsis care through heuristics methods in process mining: A trajectory analysis [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2303.14328
Brons, D., Scheepens, R., & Fahland, D. (2021). Striking a new balance in accuracy and simplicity with the Probabilistic Inductive Miner. In C. Di Ciccio, C. Di Francesco-marino & P. Soffer (Eds.), Proceedings of the 3rd International Conference on Process Mining (ICPM 2021) (pp. 32–39). IEEE. https://doi.org/10.1109/ICPM53251.2021.9576864
Dioses, J., & Córdova, L. (2025). A survey of process mining for customer management. Engineering Proceedings, 83(1), 7. https://doi.org/10.3390/engproc2025083007
Elkhovskaya, L. O., Kshenin, A. D., Balakhontceva, M. A., Ionov, M. V., & Kovalchuk, S. V. (2023). Extending process discovery with model complexity optimization and cyclic states identification: Application to healthcare processes. Algorithms, 16(1), 57. https://doi.org/10.3390/a16010057
Emamjome, F., Andrews, R., & ter Hofstede, A. H. M. (2019). A case study lens on process mining in practice. In J. vom Brocke, J. Mendling, & M. Rosemann (Eds), Business process management workshops: BPM 2019 international workshops (pp. 127–139). Springer. https://doi.org/10.1007/978-3-030-37453-2_11
Fleig, C., Augenstein, D., & Maedche, A. (2018). Designing a Process Mining-Enabled Decision Support System for Business Process Standardization in ERP Implementation Projects. In M. Weske (Ed.), Business Process Management Forum: BPM Forum 2018 (Lecture Notes in Business Information Processing, vol. 329, pp. 228–244). Springer Nature. https://doi.org/10.1007/978-3-319-98651-7_14
Gal, A., & Senderovich, A. (2020). Process Minding: Closing the Big Data Gap. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (Eds), Business Process Management. BPM 2020. Lecture Notes in Computer Science, vol. 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_1
Goretti, V., Basile, D., Barbaro, L., & Di Ciccio, C. (2024). Trusted execution environment for decentralized process mining. In G. Guizzardi, S. S. Kim & L. H. Stickel (Eds), Advanced Information Systems Engineering – CAiSE 2024 (LNCS 14663, pp. 509–527). Springer. https://doi.org/10.1007/978-3-031-61057-8_30
Halvorsrud, R., Mannhardt, F., Prillard, O., & Boletsis, C. (2024). Customer journeys and process mining – challenges and opportunities. ITM Web of Conferences, 62, Article 05002. https://doi.org/10.1051/itmconf/20246205002
Jlidi, A., & Kovács, L. (2024). Navigating process mining: A case study using pm4py [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2409.11294
Kerpedzhiev, G. D., König, U. M., Röglinger, M., & Rosemann, M. (2021). An Exploration into Future Business Process Management Capabilities in View of Digitalization. Business & Information Systems Engineering, 63, 83–96. https://doi.org/10.1007/s12599-020-00637-0
Krajčovič, M., Bastiuchenko, V., Furmannová, B., Botka, M., & Komačka, D. (2024). New Approach to the Analysis of Manufacturing Processes with the Support of Data Science. Processes 2024, 12(3), 449. https://doi.org/10.3390/pr12030449
Norouzifar, A., Dees, M., & van der Aalst, W. M. P. (2024). Imposing rules in process discovery: An inductive mining approach. In J. Araújo, J. L. de la Vara, M. Y. Santos & S. Assar (Eds), Research Challenges in Information Science – RCIS 2024 (Lecture Notes in Business Information Processing, vol. 513, pp. 220–236). Springer. https://doi.org/10.1007/978-3-031-59465-6_14
Polyvyanyy, A., Moffat, A., & García-Bañuelos, L. (2020). An entropic relevance measure for stochastic conformance checking in process mining. In Proceedings of the 2nd International Conference on Process Mining (ICPM 2020) (pp. 97–104). IEEE. https://doi.org/10.1109/ICPM49681.2020.00024
Sánchez-Obando, J. W., Duque-Méndez, N. D., & Bedoya Herrera, O. M. (2024). Construction of the invoicing process through process mining and business intelligence in the Colombian pharmaceutical sector. Computers, 13(10), 245. https://doi.org/10.3390/computers13100245
Turdibayeva, K. (2024). Process mining algorithms simply explained. ProcessMaker. https://www.processmaker.com/blog/process-mining-algorithms-simply-explained/
Twilio Segment. (2023). The state of personalization report 2023. Retrieved from https://segment.com/pdfs/State-of-Personalization-Report-Twilio-Segment-2023.pdf
Van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-49851-4
Van der Aalst, W. M. P., & Carmona, J. (Eds). (2022). Process mining handbook (Lecture Notes in Business Information Processing, vol. 448). Springer. https://doi.org/10.1007/978-3-031-08848-3
Van der Aalst, W. M. P. (2019). Everything You Always Wanted to Know About Petri Nets, but Were Afraid to Ask. In Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (Eds), Business Process Management. BPM 2019. Lecture Notes in Computer Science, vol. 11675. Springer, Cham. https://doi.org/10.1007/978-3-030-26619-6_1
Winter, M., Langguth, B., Schlee, W., & Pryss, R. (2024). Process mining in mHealth data analysis. npj Digital Medicine, 7, Article 299. https://doi.org/10.1038/s41746-024-01297-0
Zerbino, P., Stefanini, A., & Aloini, D. (2021). Process mining for customer journey analysis: A framework for omnichannel retail. Technological Forecasting and Social Change, 172, 121021. https://doi.org/10.1016/j.techfore.2021.121021
Bakhshi, A., Hassannayebi, E., & Sadeghi, A. H. (2023). Optimizing sepsis care through heuristics methods in process mining: A trajectory analysis [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2303.14328
Brons, D., Scheepens, R., & Fahland, D. (2021). Striking a new balance in accuracy and simplicity with the Probabilistic Inductive Miner. In C. Di Ciccio, C. Di Francesco-marino & P. Soffer (Eds.), Proceedings of the 3rd International Conference on Process Mining (ICPM 2021) (pp. 32–39). IEEE. https://doi.org/10.1109/ICPM53251.2021.9576864
Dioses, J., & Córdova, L. (2025). A survey of process mining for customer management. Engineering Proceedings, 83(1), 7. https://doi.org/10.3390/engproc2025083007
Elkhovskaya, L. O., Kshenin, A. D., Balakhontceva, M. A., Ionov, M. V., & Kovalchuk, S. V. (2023). Extending process discovery with model complexity optimization and cyclic states identification: Application to healthcare processes. Algorithms, 16(1), 57. https://doi.org/10.3390/a16010057
Emamjome, F., Andrews, R., & ter Hofstede, A. H. M. (2019). A case study lens on process mining in practice. In J. vom Brocke, J. Mendling, & M. Rosemann (Eds), Business process management workshops: BPM 2019 international workshops (pp. 127–139). Springer. https://doi.org/10.1007/978-3-030-37453-2_11
Fleig, C., Augenstein, D., & Maedche, A. (2018). Designing a Process Mining-Enabled Decision Support System for Business Process Standardization in ERP Implementation Projects. In M. Weske (Ed.), Business Process Management Forum: BPM Forum 2018 (Lecture Notes in Business Information Processing, vol. 329, pp. 228–244). Springer Nature. https://doi.org/10.1007/978-3-319-98651-7_14
Gal, A., & Senderovich, A. (2020). Process Minding: Closing the Big Data Gap. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (Eds), Business Process Management. BPM 2020. Lecture Notes in Computer Science, vol. 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_1
Goretti, V., Basile, D., Barbaro, L., & Di Ciccio, C. (2024). Trusted execution environment for decentralized process mining. In G. Guizzardi, S. S. Kim & L. H. Stickel (Eds), Advanced Information Systems Engineering – CAiSE 2024 (LNCS 14663, pp. 509–527). Springer. https://doi.org/10.1007/978-3-031-61057-8_30
Halvorsrud, R., Mannhardt, F., Prillard, O., & Boletsis, C. (2024). Customer journeys and process mining – challenges and opportunities. ITM Web of Conferences, 62, Article 05002. https://doi.org/10.1051/itmconf/20246205002
Jlidi, A., & Kovács, L. (2024). Navigating process mining: A case study using pm4py [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2409.11294
Kerpedzhiev, G. D., König, U. M., Röglinger, M., & Rosemann, M. (2021). An Exploration into Future Business Process Management Capabilities in View of Digitalization. Business & Information Systems Engineering, 63, 83–96. https://doi.org/10.1007/s12599-020-00637-0
Krajčovič, M., Bastiuchenko, V., Furmannová, B., Botka, M., & Komačka, D. (2024). New Approach to the Analysis of Manufacturing Processes with the Support of Data Science. Processes 2024, 12(3), 449. https://doi.org/10.3390/pr12030449
Norouzifar, A., Dees, M., & van der Aalst, W. M. P. (2024). Imposing rules in process discovery: An inductive mining approach. In J. Araújo, J. L. de la Vara, M. Y. Santos & S. Assar (Eds), Research Challenges in Information Science – RCIS 2024 (Lecture Notes in Business Information Processing, vol. 513, pp. 220–236). Springer. https://doi.org/10.1007/978-3-031-59465-6_14
Polyvyanyy, A., Moffat, A., & García-Bañuelos, L. (2020). An entropic relevance measure for stochastic conformance checking in process mining. In Proceedings of the 2nd International Conference on Process Mining (ICPM 2020) (pp. 97–104). IEEE. https://doi.org/10.1109/ICPM49681.2020.00024
Sánchez-Obando, J. W., Duque-Méndez, N. D., & Bedoya Herrera, O. M. (2024). Construction of the invoicing process through process mining and business intelligence in the Colombian pharmaceutical sector. Computers, 13(10), 245. https://doi.org/10.3390/computers13100245
Turdibayeva, K. (2024). Process mining algorithms simply explained. ProcessMaker. https://www.processmaker.com/blog/process-mining-algorithms-simply-explained/
Twilio Segment. (2023). The state of personalization report 2023. Retrieved from https://segment.com/pdfs/State-of-Personalization-Report-Twilio-Segment-2023.pdf
Van der Aalst, W. M. P. (2016). Process mining: Data science in action (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-49851-4
Van der Aalst, W. M. P., & Carmona, J. (Eds). (2022). Process mining handbook (Lecture Notes in Business Information Processing, vol. 448). Springer. https://doi.org/10.1007/978-3-031-08848-3
Van der Aalst, W. M. P. (2019). Everything You Always Wanted to Know About Petri Nets, but Were Afraid to Ask. In Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (Eds), Business Process Management. BPM 2019. Lecture Notes in Computer Science, vol. 11675. Springer, Cham. https://doi.org/10.1007/978-3-030-26619-6_1
Winter, M., Langguth, B., Schlee, W., & Pryss, R. (2024). Process mining in mHealth data analysis. npj Digital Medicine, 7, Article 299. https://doi.org/10.1038/s41746-024-01297-0
Zerbino, P., Stefanini, A., & Aloini, D. (2021). Process mining for customer journey analysis: A framework for omnichannel retail. Technological Forecasting and Social Change, 172, 121021. https://doi.org/10.1016/j.techfore.2021.121021
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Published
2025-12-29
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How to Cite
Ramirez, C., Dioses, J., & Jara, G. (2025). Implementing a Process Mining Framework to Improve Customer Experience. Journal of Telecommunications and the Digital Economy, 13(4), 127-147. https://doi.org/10.18080/jtde.v13n4.1281
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How to Cite
Ramirez, C., Dioses, J., & Jara, G. (2025). Implementing a Process Mining Framework to Improve Customer Experience. Journal of Telecommunications and the Digital Economy, 13(4), 127-147. https://doi.org/10.18080/jtde.v13n4.1281
