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Guest Article - Data-Driven Text Mining for CX Analytics in Services

last modified Nov 30, 2016 10:44 AM
Mohamed Zaki and Benjamin Lucas have written a guest article for the ServSig Group on 'Data-Driven Text Mining for CX Analytics in Services'

Mohamed Zaki and Benjamin Lucas have written a guest article for the ServSig Community, based on research recently funded by a Marketing Science Institute (MSI), Customer Experience Initiative grant. The article is based on their project titled: 'CX Analytics: A Data-Driven Measurement System for Customer Experience and Emotional Complexity'.

Extract:

"Customer Experience (CX) and Services

Unsatisfactory customer experiences result in around $83 billion in losses by US enterprises each year through abandoned purchases and defections (Forbes, 2013). As such, managing end-to-end customer experience and customer emotional needs has risen as a top managerial priority (Zorfas and Leemon, 2016). In basic terms, the more managers can understand about the experiences customers have with their product and service offerings, the more they can measure them again in the future to shape positive experiences. For example, a manager of a service organization may identify that a customer had a positive overall experience, which included a positive social component (e.g. positive interactions with staff, other customers, or their own family and friends during a service experience), but also that the customer engaged in needlessly complex cognition in response to a negatively evaluated attribute during the decision process (e.g. because of an overly complicated pricing system, menu or display of offerings). Knowing this, a manager would know in the future which specific aspect of the service experience to leverage further (social) and which needs improvement (facilitating more fluid customer cognition during the purchase decision process). This of course applies to both online and offline services." 

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