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.

There is a lack of consensus on the usefulness of Human Resource (HR) analytics to achieve better business results. The authors suggest this is due to lack of empirical evidence demonstrating how the use of data in the HR field makes a positive impact on performance, due to the detachment of the HR function from accessible data, and due to the typically poor IT infrastructure in place. We provide an in-depth case study of Strategic Competence analytics, as an important part of HR analytics, in a large multinational company, labelled ABC, which potentially shows two important contributions. First, we contribute to HR analytics literature by providing a data-driven competency model to improve the recruitment and selection process. This is used by the organization to search more effectively for talents in their knowledge networks. Second, we further develop a model for data-driven competence analytics, thus also contributing to the information systems literature, in developing specialized analytics for HR, and by finding appropriate forms of computerized network analysis for identifying and analysing knowledge hubs. Overall, our approach, shows how internal and external data triangulation and better IT integration makes a difference for the recruitment and selection process. We conclude by discussing our model’s implications for future research and practical implications.