Integrating Experience-Based and Practice-Based Perspectives on Value Co-Creation in Collective Consumption Contexts

[We’re pleased to welcome authors Carol Kelleher of Cork University, Hugh N. Wilson of the University of Warwick, Emma K. Macdonald of the University of Warwick, and Joe Peppard of MIT. They recently published an article in Journal of Service Research entitled “The Score Is Not the Music: Integrating Experience and Practice Perspectives on Value Co-Creation in Collective Consumption Contexts,” which is currently free to read for a limited time. Below, they reflect on this research:]

What motivated you to pursue this research?

Three interrelated and enduring research questions motivated this study as well as my other studies on collective consumption: 1) what is the individual experience of the collective? 2) what is the collective experience of the individual? and 3) how do they impact each other?
In many service settings, such as when attending a live orchestral music performance, the value that a customer derives from the experience depends on their interactions not just with service employees (such as when buying tickets, being ushered to a seat, or when hearing the music played by the musicians) but also from interactions with other customers in the service environment (such as others in the audience who sit together – in silence or not – to enjoy the musicians’ playing). We label these collective consumption contexts. Other examples, which have their own ‘rules of behaviour’, include spectator sports, choral singing, slimming clubs and orienteering, and examples in the online world include multi-player gaming and peer-to-peer IT support.

A key challenge for service managers in these contexts is to understand how customers coordinate with each other, particularly when there is variation in customers’ skill levels. Despite the difficulty, it is ultimately the service provider’s responsibility to ensure that the service experience is optimised for all customers irrespective of individual variation, lest it detract from the value that customers perceive.

What has been the most challenging aspect of conducting your research? Were there any surprising findings?

To address this challenging managerial issue, I conducted a six-month immersive study with the London Symphony Orchestra (LSO), a world leading orchestra, as part of my PhD. At the time, the LSO had a strong understanding of its core customers but did not know why 70% of first-time attendees failed to return. The problem did not appear to be pricing, as discounting a second visit did not improve return rates. Rather, this study’s findings resulted in the recognition that a key problem was how to support social learning.

In what ways is your research innovative, and how do you think it will impact the field?

An overarching implication for service managers is that they need to anticipate potential barriers to value co-creation that can arise from differences in customers’ prior learning. Immersive customer insight is needed to identify whether individual customers are able to learn the accepted ways of behaving, what barriers exist to this social learning, and where more expert customers will be only too happy to help less experienced peers. Service organizations can then design ways to facilitate social learning between novices and experts so as to optimize value for all.

What advice would you give to new scholars and incoming researchers in this particular field of study?

First, study and research what you are passionate about – this will be your energy source. You will always have a smile on your face, continue to be surprised and never be bored.

Second, research and scholarship is a shared social construction within the community of practice of experts and novices to – be generous and give generously. We need to appreciate the opportunity and responsibility to sustain such communities, assist junior or novice scholars and, each in our own way, leverage our shared endeavors to contribute to the greater good.

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Gaining Customer Experience Insights That Matter

[We’re pleased to welcome authors Janet R. McColl-Kennedy the University of Queensland Business School, Mohamed Zaki of the University of Cambridge, Katherine N. Lemon of the Carroll School of Management, Florian Urmetzer of the University of Cambridge, and Andy Neely of the University of Cambridge. They recently published an article in Journal of Service Research entitled “Gaining Customer Experience Insights That Matter,” which is currently free to read for a limited time. Below, they reflect on the motivation and impact of this research:]

What motivated you to pursue this research?

Customer experience is central to marketing. Providing a meaningful customer experience is viewed as essential to achieving competitive advantage and satisfied customers.
Customer experience management is listed in the top ten priorities of CEOs around the globe.
Organizations that carefully manage the customer experience reap rewards such as increased customer satisfaction, revenue growth, increased customer loyalty and greater employee satisfaction.

But to date knowing what to measure and how to gain rich insights that matter through multiple data sources, especially what to do with open-ended feedback has not been clear. Often open-ended feedback that firms receive is ignored, or simply categorized broadly as a complaint or a compliment.
We show that this rich feedback can be used to identify previously unrecognized, critical touchpoints in the customer experience and to take specific actions to strengthen the customer experience, thereby enhancing revenue growth, customer loyalty and employee and customer satisfaction.

In what ways is your research innovative, and how do you think it will impact the field?

This is the first empirical study of customer experience.
Large amounts of data, including textual data such as verbatim comments from customers, are now generated at many touchpoints in the customer journey. Text mining is well suited to extract customer insights from unstructured comments and customer satisfaction data. However, text mining is not yet mainstream in marketing. Text mining and other emerging technologies offer potentially better ways to measure and manage customer experience. In our model, we connect qualitative data and quantitative data with a text analytics approach. We show that customer experience analytics that apply big data techniques to the customer experience can offer significant insights that matter.

We identify seven root causes for the complex B2B service. Each of these represent opportunities for improving the CX.
Our model is also able to uncover customers who are at risk of leaving the firm, even customers who give high satisfaction scores (or NPS scores). Customers with high satisfaction scores would be seen by the firm as “satisfied”, or those with high NPS scores would be deemed “very likely to recommend”, and therefore not identified by the firm as requiring attention; yet we find that these customers are clearly voicing their concerns in the comments, and may be at higher risk of churning than traditional measures may suggest.

We uncover an entire “hidden” segment of supposedly highly satisfied customers who voice significant concerns. 42% of customers who give scores of 9.5 and above (out of 10) actually complain, as do many who give scores between 7 and 9.4 (44%). Complaints made by customers who gave satisfaction scores of 7 or greater were often ignored, despite these customers being worth over $250,000 on average and accounting for a significant portion of sales. Sales figures shows that when these customers’ concerns were not addressed sales went down significantly. For instance, one such “satisfied” customer reduced its purchases from over $200,000 to less than $2000. The key insight? Ignoring the small details that can be identified through the authors’ text analytics model can mean big losses for firms.

Our approach enables firms to link customer-centric CX elements from the conceptual framework (identified as potential pain points) to specific firm functions and jobs (identified as root causes) to take specific actions to strengthen the customer experience. We provide a step-by-step guide for implementing the approach highlighting what really matters to customers and what actions are needed by managers

What advice would you give to new scholars and incoming researchers in this particular field of study?

This is a very exciting time to be investigating customer experience with new data mining tools available. Learn new tools and apply them to make important contributions to theory and practice. Collaborating with a company that is interested in improving its performance through new approaches can yield innovative insights – both for the firm and for scholarship.

Professor Janet R. McColl-Kennedy, PhD, FAMI, FANZMAC, CPM
Professor of Marketing I UQ Business School The University of Queensland l Brisbane QLD 4072 l AUSTRALIA
P: +617 3346 8178 | E:
Google Scholar

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Using Text Mining for Customer Feedback

[We’re pleased to welcome Francisco Villarroel Ordenes, who is one of five collaborating authors on the article “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach” from Journal of Service Research.]

The Big Data phenomenon is not only about exponential growth of customer data, but about new and challenging data structures such as textual information which require new methods and metrics to facilitate 02JSR13_Covers.inddanalysis. Customer experience feedback, usually found in platforms such as social media, e-mails and feedback forms represents a form of complicated data structure which is challenging organizations to develop new methods for its timely and consistent analysis. Our paper, “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach”, is the result of a collaborative effort between Marketing and Information Systems researchers. We develop a Case Study with a UK service organization which receives more than 10000 comments of customer experience feedback per month. In this context, we design and implement the ARC (Activities, Resources, Context) framework, which is able to automate the analysis of customer feedback through a text mining model. The text mining approach used with this guiding framework is useful for analyzing customer experience feedback with the standard flow of activities (stages) of any service. Due to its flexible evolutionary format we describe it as an ‘open learning model’. Specifically, application of the text mining model within the ARC framework provides efficient and faster analysis of textual information compared with the current manual processes (seconds compared with 2 weeks). The consistency of the information extracted and the specificity of the analysis provided deliver an additional advantage: namely, the practicality of identifying resources or activities that the company can improve immediately. The article provides managers and researchers with a text analytics methodology and application which departs from simple sentiment analysis. Instead a more holistic representation of customer experience feedback in verbatim data is identified, which enables managers to identify what is causing particular sentiment outcomes and thus they can then act to reallocate resources or change processes at an organizational or even customer-specific level.

Read “Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach” from Journal of Service Research for free by clicking here. Make sure to click here to sign up for e-alerts and be the first to know all the latest from Journal of Service Research!

s200_francisco.villarroelFrancisco Villarroel Ordenes is a PhD candidate at the Marketing and Supply Chain Management Department at the School of Business Economics in Maastricht University. His research interests include social media conversations, customer experience feedback, sentiment analysis, value cocreation, and the development of text mining methods for marketing research.

publicphoto.ashxBabis Theodoulidis is an associate professor in information management at Manchester Business School, University of Manchester. His research has been published in both science and social science journals such as International Journal Services Technology and Management, Journal of Information Systems, Knowledge Management Research & Practice, Expert Systems with Applications, International Journal of Information Management, International Journal of Data Warehousing and Mining, and Journal of Visual Languages and Computing. His most recent research interests focus on the design of service-based information systems, the temporal and spatial aspects of information, the analysis of information using data and text mining techniques, the visualization of information, and service information management issues within organizations.

jamie.ashxJamie Burton is head of the marketing group and an associate professor in Marketing at Manchester Business School (MBS). He is a research director for MBS’s Customer Management Leadership Group, publishes in a number of journals including the Journal of Marketing Management and the Journal of Service Management and his research interests include customer experience and feedback, transformative service research including service marketing, servitization, relationship marketing, and customer profitability. He is a lead author of a 2013 British Quality Foundation report and is coauthor of Murphy, J. et al. (2006), Converting Customer Value: from Retention to Profit, Chichester: John Wiley and Sons.

Thorsten_GruberThorsten Gruber is a co-director of the Centre for Service Management and a professor of Marketing and Service Management at Loughborough University. His research interests include consumer complaining behavior, services marketing, and the development of qualitative online research methods. His work has been published in journals such as Journal of the Academy of Marketing Science, Journal of Product Innovation Management, Journal of Business Research, Journal of Service Management, and Industrial Marketing Management.

MZMohamed Zaki is a research associate at Cambridge Service Alliance, University of Cambridge. His research lies in the field of information governance, business intelligence, and big data analytics. He has many publications in these areas. His experience in the business intelligence/data analytics and service innovation areas enables him to consult in various projects to investigate business intelligence issues in different domains within a service-oriented architecture. Currently, he investigates “How Big data could play a role in improving and optimising services within complex service network organisation” in different sectors such as education, asset heavy, and defense.