Towards a Data-Driven Understanding of The Customer Experience Gestalt

Abstract

Customer experience (CE) has become a focal area of interest for both academics and managers, providing firms with a competitive edge over others in the "experience economy". Despite the importance of CE and the many conceptual studies aiming to deliver a better understanding of it's multidimensional and subjective nature, a gap exists in developing an empirically grounded understanding of this construct. Generally, CE has been mainly addressed through its antecedents (e.g., touchpoints) and outcomes (e.g., loyalty and satisfaction), while the CE phenomenon has often been treated as a black box. With recent advancements in data-driven technology ushered by what some call the fourth industrial revolution, methods involving artificial intelligence, big data analytics, conversational agents, and neuroscience show promise in providing marketers, and specifically service researchers the tools to explore and better understand complex constructs such as CE. As a result, the objectives of this thesis are to develop a data-driven understanding of the CE phenomenon by incorporating three studies that: (i) Develop an experiential and sub-elemental data-driven CE framework (RAFTS) (Study1), (ii) develop and verify a data-driven method that examines customer feelings using conversational agents and storytelling (Study 2), and (iii) explore the dynamic interplay of the customer's state-of-mind on recalled experiences and conversational data-driven agents (Study 3). The main contributions of this thesis are: (i) Provide service researchers with RAFTS that incorporates the customer journey and allows multiple data-driven collection and analysis methods (e.g., AI chatbots) to map experiential (realtime) data to a unified and semantically clear sub elemental CE framework, (ii) propose and verify conversational agents that use sentiment analysis as promising, novel, cost/resource-effective tools to be used in interviewing customers about their CE feelings, (iii) and establish that the customer's state-of-mind does influence recalled experiences and feelings towards customer-facing conversational agents, thus highlighting the importance of designing human-friendly technologies capable of managing and adapting to the customer's state-of-mind throughout customer service encounter. While a holistic data-driven CE understanding is an ambitious endeavour, this thesis provides a path towards achieving this objective. A unified data-driven CE framework (RAFTS) enables researchers and managers to use similar units of analysis allowing them to "speak the same language" when measuring and analysing CE. This aids in further benchmarking CE across different contexts, industries, and cultures allowing for valuable insights that aid in progressing the conceptualisation of this construct. Moreover, the more data points captured with RAFTS across customer journey touchpoints, the more understanding can be achieved and thus more avenues for improving CE management. Furthermore, this thesis paves the way forward with conversational agents that can interview customers about their CE feelings, allowing both academics and researchers to improve scalability and resource/cost efficiency when collecting and examining CE feelings data. Lastly, this thesis highlights the importance of the human experience in the path of better understanding and examining holistic CE using data-driven methods. These methods should account for and manage customer feelings and their state-of-mind to mitigate negative CEs and as a result, a decline in satisfaction and loyalty. The thesis also provides an extensive future work agenda to continue this ambitious effort by the means of a CE archetype map that is broken down into four quadrants (service, customer, touchpoint, and human), each addressing an area of CE that aims at improving data-driven holistic CE understanding.

Date of Award	31 Dec 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Matti Jaakkola (Supervisor) & Jamie Burton (Supervisor)