Service modeling of compliments and complaints and its implications for value co-creation

The paper demonstrates the impact of using text mining techniques to automate analysis and classification of large amounts of customer compliments and complaints (C&C). The research is using an empirical approach to generate a better understanding of how co-creation processes can be designed based on customer feedback experiences. In order to improve the service propositions, the integration of customer comments as operant resources of the organisation is discussed. A cocreation feedback model is proposed, considering positive and negative comments across three main categories, resources, activities and attributes (positive/negative comments). Finally, the co-creation feedback model enables the mapping of the organisation’s service processes from the customer perspective

Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach

Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an "open learning" model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment. © The Author(s) 2014

A strategic framework

Customer experience (CX) has emerged as a sustainable source of competitive differentiation. Recent developments in big data analytics (BDA) have exposed possibilities to unlock customer insights for customer experience management (CXM). Research at the intersection of these two fields is scarce and there is a need for conceptual work that (1) provides an overview of opportunities to use BDA for CXM and (2) guides management practice and future research. The purpose of this paper is therefore to develop a strategic framework for CXM based on CX insights resulting from BDA. Our conceptualisation is comprehensive and is particularly relevant for researchers and practitioners who are less familiar with the potential of BDA for CXM. For managers, we provide a step-by-step guide on how to kick-start or implement our strategic framework. For researchers, we propose some opportunities for future studies in this promising research area.peerReviewe

A Layered Architecture for Sentiment Classification of Products Reviews in Italian Language

The paper illustrates a system for the automatic classification of the sentiment orientation expressed into reviews written in Italian language. A proper stratification of linguistic resources is adopted in order to solve the lacking of an opinion lexicon specifically suited for the Italian language. Experiments show that the proposed system can be applied to a wide range of domains

How Are Negative Customer Experiences Formed? A Qualitative Study of Customers’ Online Shopping Journeys

This study investigates how negative customer experiences are formed during customers’ online shopping journeys. A qualitative, in-depth dataset collected from 34 participants was employed to identify negatively perceived touchpoints that contribute to the customer experience in a negative way. The findings reveal that negative touchpoints are experienced during customers’ entire journeys, particularly after a purchase is completed. We identified 152 negative touchpoints from the data, of which 53 were experienced during search and consideration, 35 when finalizing a purchase, 33 during delivery, and 31 during after-sales interactions with the company. Within these four main categories, 20 subthemes describing the touchpoints and formation of customers’ negative experiences were identified therein. The findings highlight the importance of understanding the holistic customer experience formation, including the before- and after-purchase phases of the online shopping journey. In practice, the findings can be utilized in online service design and improvement.peerReviewe

Approach to Service Design Based on Customer Behavior Data: A Case Study on Eco-driving Service Design Using Bus Drivers??? Behavior Data

Various types and massive amounts of customer behavior data are collected in various industries, such as transportation, healthcare, hospitality, and logistics. The use of customer behavior data can improve the design activities of service firms. Despite the applicability of customer behavior data to service design, only a few studies have examined an approach to utilize customer behavior data in service design. This study proposes an approach for designing services with customer behavior data. The approach is based on a case study on eco-driving service design with the behavior data of bus drivers. This study extends the research on service design by demonstrating how customer behavior data are utilized for service design and assisting service designers in designing services with customer behavior data