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Session Overview
Session
16-03: Eleonora Pantano
Time:
Sunday, 21/Jul/2019:
11:00am - 11:25am

Seminar Room 2-3

Chair: Eleonora Pantano


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Abstract

From Artificial Intelligence to Service Intelligence: Evidence from Luxury Department Stores

Authors: Eleonora Pantano (University of Bristol, United Kingdom), Nikolaos Stylos (University of Bristol, United Kingdom)

Automation is increasingly becoming of fundamental importance in marketing and, more generally, in the service domain (Rust and Huang, 2014). The scope is to expand integration of the technology in marketing activities, with emphasis in artificial intelligence and machine learning algorithms. This would render higher customized services, involving the learning process of consumers’ preferences and behavior, as well as relevant responses (Grewal et al., 2017; Huang and Rust, 2017). Indeed, relational technologies, such as learning technologies and Artificial Intelligence (AI) can adaptively interact with customers (Huang and Rust, 2018). Thus, the way marketing managers and retailers might use these technologies to collect and manage big data and adjust marketing strategies accordingly is an emerging retail topic (Bradlow et al., 2017; Pantano et al., 2017); the effects on store and customer-level strategies exemplify the importance of the technological forecasting and the big data emergence (Kumar et al., 2017). In fact, understanding customers and markets is of particular value in relation to segmentation and targeting as they allow marketing programmes to be personalized and support the maximisation of a consumer’s life time value to an organisation. Therefore, there is much interest towards big data and how to make use of it to gain a competitive advantage. To this end, new metrics to analyze this data are encouraged by recent studies (Ailawadi and Farris, 2017; Bradlow et al., 2017).

Although luxury sector is largely recognized as one of the fastest-growing sectors characterized by the challenge of creating a brand experience for consumers, it has not yet benefited enough from big data analytics (Hennig-Thurau et al. 2015). Indeed, luxury retailers are moving towards a smart retailing approach to maximize the benefits emerging from the technological integration, which also allows them to collect more detailed information on consumers (Pantano et al., 2018; Priporas, Stylos & Fotiadis, 2017). A new “intelligent” approach would improve consumers’ service while diffusing brand content that conveys the uniqueness of brand intangibles (Bo, 2014).

The aim of this study is to show how AI leads to the new service intelligence, by analyzing the case of luxury industry in general, and luxury department stores in particular. This study identifies consumers’ insights through the systematic analysis of consumers’ shared tweets.

The contribution of this paper is primarily methodological in nature. It proposes a novel approach for evaluating the emerging phenomenon of service intelligence as prompt by the increasing usage of machine learning algorithms, drawn upon the sentiment analysis and thematic analysis of tweets in a systematic way. This is based on salient word associations related to luxury products, and the relevant marketing strategies. Practically, it allows managers to effectively monitor consumers’ voice through systematic tools, integrated into the marketing strategies.



 
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