Abstract
Purpose - The purpose of this paper is to identify the current research gap that exists surrounding the use of big data in customer experience, especially in a business-to-business (B2B) context. The paper goes on to describe a research project aimed at contributing to knowledge in this area.
Design/methodology/approach - Based on a review of literature, a preliminary conceptual model has been devised which is presented in this paper. The model will be explored through case study research so that it might be improved. In particular, a working definition for customer experience will be discussed to derive a definition of more relevance to B2B firms.
Findings – this paper is conceptual but initial findings from research will be presented at the conference
Research limitations/implications – Data gathered through interviews and other case study techniques will always be subject to bias whether introduced by the researcher, the participant or the contextual environment. In addition, cases will be selected to an extent based on convenience and will be English speaking organisations based in the UK. Only companies willing to be named in the research will be recruited and this may have an impact on the openness and honesty of the participants.
Practical implications - The aims of the research are to contribute to knowledge and to provide transferable knowledge to practitioners. Focusing on a contemporary topic of interest and applying a strategic, commercial approach is more likely to provide results which are of relevance.
Social implications – Improving the understanding of how customer experiences can be enhanced in a B2B context should allow organisations to utilise data better and enhance the experience of their employees, customers and other stakeholders. This in turn should lead to enhanced wellbeing and/or enhanced profit.
Originality/value – This paper draws on existing literature to develop a more holistic and connected conceptual model which relates the topic of big data to the topic of customer experience and seeks to understand how it might be made more relevant for a B2B context.
Design/methodology/approach - Based on a review of literature, a preliminary conceptual model has been devised which is presented in this paper. The model will be explored through case study research so that it might be improved. In particular, a working definition for customer experience will be discussed to derive a definition of more relevance to B2B firms.
Findings – this paper is conceptual but initial findings from research will be presented at the conference
Research limitations/implications – Data gathered through interviews and other case study techniques will always be subject to bias whether introduced by the researcher, the participant or the contextual environment. In addition, cases will be selected to an extent based on convenience and will be English speaking organisations based in the UK. Only companies willing to be named in the research will be recruited and this may have an impact on the openness and honesty of the participants.
Practical implications - The aims of the research are to contribute to knowledge and to provide transferable knowledge to practitioners. Focusing on a contemporary topic of interest and applying a strategic, commercial approach is more likely to provide results which are of relevance.
Social implications – Improving the understanding of how customer experiences can be enhanced in a B2B context should allow organisations to utilise data better and enhance the experience of their employees, customers and other stakeholders. This in turn should lead to enhanced wellbeing and/or enhanced profit.
Originality/value – This paper draws on existing literature to develop a more holistic and connected conceptual model which relates the topic of big data to the topic of customer experience and seeks to understand how it might be made more relevant for a B2B context.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of EMAC 2018 Conference |
| Publication status | Accepted/In press - 5 Mar 2018 |
Keywords
- Big Data
- Customer Experience
- Business-to-Business
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