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

Jinho Ahn, Jeungsun Lee

doi:10.17762/turcomat.v12i5.889

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.

Highlights

It is a hard time for companies to survive when they “sell only”
Quantitative collection based on big data, which is commonly used, interviews with ethnography rather than processing methods, data collection using observational survey methods, and affinity diagrams are primarily used to process and process humans who are the main subjects of experience data generation
Results of Hypothesis Test For the hypothesis H1 that the data collection using the ethnography method will have a positive (+) effect on the primary processing of the data of the affinity diagram formula, the t value was adopted as 4.887 (p = .000), and the data of the ethnography method The hypothesis H2 that collection will have a positive effect on the secondary processing of data in a phenomenological description method has a t value of 6.143 (p = .000)

Summary

Introduction

It is a hard time for companies to survive when they “sell only” This is because it is more important for these generations to continuously experience what they are getting than what they are doing . Experience data management strategy is needed to help this experience data create the economic added value of the company and to improve the important decision making that the actual resources of the company are put into. An important factor in the experience data management strategy requires a qualitative based experience data processing method to secure good experience data that is different from the general data collection and processing process[3]. This study is for the effects of qualitative experience data processing, which is secondarily processed using phenomenological method that interprets experience data, on customer experience management, customer loyalty enhancement, and corporate value creation. The research contributes companies with management difficulties in establishing customer-oriented management strategies and creating long-term profits and added value in the absence of information and strategies for customers

Literature Review

Research Model and Research Hypothesis

Empirical Analysis and Result

Conclusion