Customer journey analytics: A model for creating diagnostic insights with process mining

Daan  Weijs; Emiel Caron

doi:10.5220/0011263900003266

Customer journey analytics: A model for creating diagnostic insights with process mining

Daan Weijs
, Emiel Caron

Information Systems & Oper. Mgt.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

The customer journey is becoming more complex due to digitization of business processes, broadening the gap between the proposed journey and the journey that is actually experienced by customers. Customer Journey Analytics (CJA) aims to detect and analyse pain points in the journey in order to improve the customer experience. This study proposes an extended version of the Customer Journey Mapping (CJM) model, to measure the impact of different types of touchpoints along the customer journey on customer experience, and to apply process mining to gain more insight in the gap between proposed and actual journeys. Moreover, this model is used to develop dedicated CJA based on process mining techniques. A case study on e-commerce applies the CJM-model in practice and shows how the combination of process mining techniques can answer the analysis questions that arise in customer journey management.

Original language	English
Title of host publication	Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022)
Editors	Hans-Georg Fill, Marten van Sinderen , Leszek Maciaszek
Place of Publication	Lisbon
Publisher	SciTePress
Pages	418-424
Volume	1
ISBN (Electronic)	978-989-758-588-3
DOIs	https://doi.org/10.5220/0011263900003266
Publication status	Published - Jul 2022
Event	International Conference on Software Technologies - Portugal, Lisboa, Portugal Duration: 11 Jul 2022 → 13 Jul 2022 Conference number: 17 https://icsoft.scitevents.org/

Publication series

Name
Volume	1
ISSN (Electronic)	2184-2833

Conference

Conference	International Conference on Software Technologies
Abbreviated title	ICSoft
Country/Territory	Portugal
City	Lisboa
Period	11/07/22 → 13/07/22
Internet address	https://icsoft.scitevents.org/

Keywords

Customer journey
Touchpoints
Customer journey analytics
Data models
Process mining

Access to Document

10.5220/0011263900003266

Best paper award
Caron, E. (Recipient), 13 Jul 2022
Prize

Cite this

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title = "Customer journey analytics: A model for creating diagnostic insights with process mining",

abstract = "The customer journey is becoming more complex due to digitization of business processes, broadening the gap between the proposed journey and the journey that is actually experienced by customers. Customer Journey Analytics (CJA) aims to detect and analyse pain points in the journey in order to improve the customer experience. This study proposes an extended version of the Customer Journey Mapping (CJM) model, to measure the impact of different types of touchpoints along the customer journey on customer experience, and to apply process mining to gain more insight in the gap between proposed and actual journeys. Moreover, this model is used to develop dedicated CJA based on process mining techniques. A case study on e-commerce applies the CJM-model in practice and shows how the combination of process mining techniques can answer the analysis questions that arise in customer journey management.",

keywords = "Customer journey, Touchpoints, Customer journey analytics, Data models, Process mining",

author = "Daan Weijs and Emiel Caron",

year = "2022",

month = jul,

doi = "10.5220/0011263900003266",

language = "English",

volume = "1",

publisher = "SciTePress",

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editor = "\{ Fill\}, Hans-Georg and \{van Sinderen \}, \{Marten \} and Leszek Maciaszek",

booktitle = "Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022)",

note = "International Conference on Software Technologies, ICSoft ; Conference date: 11-07-2022 Through 13-07-2022",

url = "https://icsoft.scitevents.org/",

}

Weijs, D & Caron, E 2022, Customer journey analytics: A model for creating diagnostic insights with process mining. in H-G Fill, M van Sinderen & L Maciaszek (eds), Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022). vol. 1, SciTePress, Lisbon, pp. 418-424, International Conference on Software Technologies, Lisboa, Portugal, 11/07/22. https://doi.org/10.5220/0011263900003266

Customer journey analytics: A model for creating diagnostic insights with process mining. / Weijs, Daan; Caron, Emiel.
Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022). ed. / Hans-Georg Fill; Marten van Sinderen ; Leszek Maciaszek. Vol. 1 Lisbon: SciTePress, 2022. p. 418-424.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

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AB - The customer journey is becoming more complex due to digitization of business processes, broadening the gap between the proposed journey and the journey that is actually experienced by customers. Customer Journey Analytics (CJA) aims to detect and analyse pain points in the journey in order to improve the customer experience. This study proposes an extended version of the Customer Journey Mapping (CJM) model, to measure the impact of different types of touchpoints along the customer journey on customer experience, and to apply process mining to gain more insight in the gap between proposed and actual journeys. Moreover, this model is used to develop dedicated CJA based on process mining techniques. A case study on e-commerce applies the CJM-model in practice and shows how the combination of process mining techniques can answer the analysis questions that arise in customer journey management.

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KW - Touchpoints

KW - Customer journey analytics

KW - Data models

KW - Process mining

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M3 - Conference contribution

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EP - 424

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A2 - van Sinderen , Marten

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Y2 - 11 July 2022 through 13 July 2022

ER -

Customer journey analytics: A model for creating diagnostic insights with process mining

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Prizes

Best paper award

Cite this