Sensing Distress – Towards a Blended Method for Detecting and Responding to Problematic Customer Experience Events
Excellent Customer Experience (CE) is a strategic priority for many large service organisations in a competitive marketplace. CE should be seamless, and in most cases it is, with customers ordering, paying for and receiving services that align with their expectations. However, in rare cases, an exceptional process event leads to service delivery delay or failure, and both the customer and organisation end up in complex recovery situations as a result. Unless this recovery is handled effectively inefficiency, avoidable costs and brand damage can result. So how can organisations sense when these problems are occurring and how can they respond to avoid these negative consequences? Our paper proposes a blended methodology where process mining and qualitative user research combine to give a holistic picture of customer experience issues, derived from a particular customer case study. We propose a theoretical model for detecting and responding to customer issues, and discuss the challenges and opportunities of such a model when applied in practice in large service organisations.
KeywordsCustomer experience Process mining HCI
Thanks to Florian Allwein of LSE for additional data from advisor interviews and observations. Thanks also to William Harmer of BT for his Process Mining expertise.
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