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assignments:detecting_root_causes_of_complaints_and_investigate_the_continuation_within_the_customer_journey [2018/02/09 12:52]
mhassani [Detecting root causes of complaints and investigate the continuation within the customer journey]
assignments:detecting_root_causes_of_complaints_and_investigate_the_continuation_within_the_customer_journey [2018/02/09 12:54] (current)
mhassani [Research goal and questions:]
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 Underlined co-created customer journey mining algorithms, together with the TU/e (Bart Hompes and Joos Buijs from the Architecture of Information Systems group). This research showed that it is possible to distinguish the different journeys per customer. Moreover, in collaboration with the TiU econometrist department, a driver model has recently developed to distinguish relevant drivers and to predict the NPS-score. Underlined co-created customer journey mining algorithms, together with the TU/e (Bart Hompes and Joos Buijs from the Architecture of Information Systems group). This research showed that it is possible to distinguish the different journeys per customer. Moreover, in collaboration with the TiU econometrist department, a driver model has recently developed to distinguish relevant drivers and to predict the NPS-score.
 =====Research goal and questions:​===== =====Research goal and questions:​=====
-In further collaborative research between TU/e, CZ and Underlined, the research focus will be on developing a predictive method on complaints, based on process mining techniques. The scope of the current master project will be +In further collaborative research between TU/e, CZ and Underlined, the research focus will be on developing a predictive method on complaints, based on process mining techniques. The scope of the current master project will be 
-Developing ​a complaint driver model+developing ​a complaint driver model by taking into account the customer journey to detect root causes and follow up triggers.
-taking into account the customer journey to detect root causes and follow up triggers.+
 More precisely, after a proper preprocessing of the different types of data, the master project will start by addressing the following list of questions: More precisely, after a proper preprocessing of the different types of data, the master project will start by addressing the following list of questions:
   - **Can customers be segmented?​** Which distance measure is suitable for clustering customers such that similar customers can be grouped based on their behavior throughout their journey? How good is this clustering method when including the domain knowledge and/or internal quality evaluation measures?   - **Can customers be segmented?​** Which distance measure is suitable for clustering customers such that similar customers can be grouped based on their behavior throughout their journey? How good is this clustering method when including the domain knowledge and/or internal quality evaluation measures?