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Cambridge Service Alliance

developing new understanding and approaches to complex service systems

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Service Consumer's Exeperience Analytics

Creating a strong customer experience is a strategic priority for organisations. Companies are leveraging new technologies such as mobile applications, social media platforms, virtual reality, drones and the Internet of Things to provide smart services and enable a seamless customer experience. The complexity of using these technologies within an organisation’s myriad touchpoints has led to a data explosion across touchpoints in the entire customer journey. Most of this customer data is unstructured textual data, which is generated at several touchpoints in the customer journey. Service managers use tools such as customer feedback surveys, questionnaires, and interviews as a method of measuring customer satisfaction and a company’s future performance. However, these tools are used at the end of the customer experience journey, masking the underlying issues of concern, which form the basis for identifying improvements. Furthermore, a survey data set cannot offer real-time responses; therefore, organisations have to rely on other real-time customer data sources, to identify critical pain points, to unmask underlying sources of friction at the various touchpoints and to provide deeper insights into critical touchpoints and how and where organisations can implement change to reduce friction more quickly. Furthermore, based on previous research with one of our industrial partners, we found that only 1-2% random sample of total customers is surveyed.

Thus, this year, we will experiment with one of our partners how we capture and analyse the raw components of CX (e.g., social listening, text, mobile applications, location-based data) to further understand CX and the consumer journey. The project aims to develop a review app and a text mining methodology for understanding the service consumer’s experience in real time and use the generated insights to improve and design better service delivery. The expected outcomes of this research are:

  1. A real-time review application paired with an analytical text mining model would allow our partners to resolve customer issues through the faster processing of data and accurate interpretation of the data. 
  2. An evaluation of the use of new text mining techniques (e.g. deep learning) in analysing qualitative data, and suggestions on their utilisation in an industrial context.
  3. A comparison between traditional survey vs. real-time for feedback collection and analysis (e.g. cost/benefit analysis of practices) 
  4. Researcher observations and findings, which will be recorded and anlaysed during the shadowing process with employees collected the feedback.

If you are interested to know more about this work or the experiment findings, please contact .

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