Analyzing Customer Experience Feedback Using Text Mining

Abstract

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.

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“…Indeed, a well-analyzed customer journey leads to excellent customer experience, thus indicating how to innovate and differentiate the offers (Bolton et al, 2014;Voss & Zomerdijk, 2007). Despite the theory, mapping the customer journey is not an easy task, as well as capturing useful knowledge to leverage on the journey and identify the customers' insights at the basis of a true customer centric approach (Ordenes et al, 2014). That is the topic addressed by our study, trying to answer the following research question: How can companies align customer journeys and their business strategy?…”

Implementasi Metode Association Rule untuk Menganalisis Data Twitter tentang Badan Penyelenggara Jaminan Sosial dengan Algoritma Frequent Pattern-Growth

“…Text mining adalah perkembangan teknologi yang membahas masalah pengelolaan informasi melalui penggunaan teknik data mining, pembelajaran mesin, pengolahan bahasa alami, pencarian informasi, dan manajemen pengetahuan. Lebih khusus lagi, text mining melibatkan pemrosesan kumpulan dokumen, atau korpus, di mana dokumen diubah menjadi data terstruktur, sehingga masing-masing dokumen digambarkan menggunakan seperangkat fitur yang disebut konsep untuk memberikan perspektif holisme dari informasi tekstual dan nontekstual (Ordenes et al, 2014). Text mining adalah pengembangan teknologi dengan potensi komersial yang tinggi.…”

Mining Customer Feedback Documents

“…Kahya Özyirmidokuz and Özyirmidokuz [2] analyzed the top seven heating systems firms' customer complaint documents in Turkey via web TM. Ordenes et al [1] used linguistics-based TM modeling to help in the process of developing an improved framework. The proposed framework incorporated important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context.…”

“…Increasingly, customers use multiple communication channels to provide feedback, making it cumbersome for organizations to develop efficient and effective processes to collect and analyze all the information [1]. Firms must manage customer complaints to improve the quality of their service.…”