A Study on the Qualitative Data Analysis for Customer Experience Management Strategy

Abstract

Securing customer experience data that creates positive emotions for customers and differentiates them from products and services from competitors is becoming important to a company's growth engine. In particular, an important factor in the management of experience data requires a qualitative-based experience data processing method to secure good experience data different from the quantitative data collection such as big data and processing method. With the emergence of the experience economy, it is very important for companies to collect and process experience data in the existing big data processing method. However, the experience data processing method based on big data that analyses the current quantitative data is difficult to provide good experience data from a corporate data strategic point of view. In particular, for corporate customer experience management, mix studies are required for analysis method of qualitative experience data to meaningfully interpret the expansive quantitative experience data of big data and phenomena and context in social science. This is because it is possible to discover the meaning of experience data by reading the context of phenomena by collecting experiences through ethnography methods such as observation or interviewing the context that could not be read in the process of processing the vast quantitative experience data of the big data method. In this study, the first processing was performed as an affinity diagram through a method of collecting experience data using ethnography method. Secondly, the effect of the qualitative experience data processing method on customer experience management, customer loyalty reinforcement, and enterprise value creation was studied. As a result, only the research hypothesis that there was a direct relationship between the affinity method and the utilization of experience data was rejected, and all the research settings set for the remaining qualitative experience data processing and utilization model were adopted.