Integrating Big Data Analytics Into Retail Services Marketing Management: The Case of a Large Shopping Center in London, UK

Integrating Big Data Analytics Into Retail Services Marketing Management: The Case of a Large Shopping Center in London, UK

Eleonora Pantano (University of Bristol, UK), Simona Giglio (University of Calabria, Italy) and Charles Dennis (Middlesex University, London, UK)
DOI: 10.4018/978-1-7998-0131-3.ch010

Abstract

This chapter aims at exploring the extent to which the recent trends in digitalization of marketing and related services are leading to a massive amount of consumers' information (big data) in order to suggest possible solutions and recommendations. To this end, the chapter will focus on the case of a large shopping center in London (UK) as meaningful example of how retailers might exploit big data analytics such as sentiment and image analytics to get useful consumers' insights to be successfully integrated into marketing strategies. Finally, the chapter discusses the implications for scholars and practitioners and proposes a future research agenda.
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Introduction

Automation is increasingly becoming of fundamental importance in marketing and, more generally, in the service domain (Rust and Huang, 2014). The actual trend characterizing marketing management is to integrate more the technology in marketing activities to provide higher customized services, which involves the learning process of consumers’ preferences and behavior and according responses (Grewal, Roggeveen, and Nordfält, 2017; Huang and Rust, 2017). For instance, in increasing diffusion of self-service technologies, robotic services or collaborative filtering allow the improvement of the efficiency of traditionally standardized activities that fulfil a given set of customer needs. Indeed, relational technologies, such as learning technologies and Artificial Intelligence (AI) are able to adaptively interact with customers (Huang and Rust, 2018). Thus, how marketing managers and retailers might use these technologies to collect and manage big data on consumers and adjust management strategies accordingly is emerging as a powerful line of inquiry (Bradlow et al., 2017; Pantano, Priporas, & Stylos, 2017; Balducci and Marinova, 2018; Hartman et al., 2019), with emphasis on their effect on store and customer-level strategies that will be more affected by technological forecasting and the emergence of big data (Kumar Anand, and Song, 2017). Indeed, the understanding of customers and markets that data generate supports effective decision-making in relation to all aspects of the marketing intelligence (Janssen, van der Voort, Wahyudi, 2017). They are 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. The growth of Data Science lead by the massive amount of unstructured data (including “big data”) is seen as one of the most influential factors in contemporary marketing (Balducci and Marinova, 2018). Therefore, there is much interest in big data and how to successfully analyse it to gain competitive advantage. In other words, how to explore big data to get real-world business results and transfer them into successful strategies is becoming an important asset for today’s marketing research and practice. To this end, new metrics to analyze this data are encouraged by recent studies (Ailawadi and Farris, 2017; Bradlow et al., 2017; Dindar and Yaman, 2018; Balducci and Marinova, 2018; Hartman et al., 2019; Pantano, Giglio and Dennis, in print.).

The aim of this chapter is to show how it is possible to transfer big data analytics into marketing management, in order to figure out both the extent to which these findings impact traditional marketing strategies and the opportunities emerging from the integration into marketing practice. To this end, the research reported in this chapter refers to the biggest Shopping Center in London: Westfield London, and data collected from two different social media, Twitter (for text) and Instagram (for images). The data are analysed through Wolfram Mathematica software for sentiment analytics for tweets analysis, and image analytics for pictures evaluation.

The chapter is structured in three main parts: (i) the background to recent innovation and technology in digital marketing, the emerging topic of big data in marketing intelligence and related issues and controversies, (ii) proposed solutions consisting of two new methodologies for big data analytics, and (iii) implications for marketers and scholars, and the future research agenda.

Key Terms in this Chapter

Hashtag: A particular type of metadata tag used in social networks (i.e. on Twitter and Instagram) and microblogging allowing users to apply a certain tag, which allows other users to easily find the messages with a specific theme or content (identified by this tag).

Marketing Intelligence: A set of accurate information on consumers behavior (consumer intelligence) and competitors’ behavior (competitor intelligence) that supports companies in the decision-making process to identify market opportunities and penetration strategies.

Wolfram Mathematica: A tool for computing, simulation and mathematical modeling providing a computation environment through its already available functions (including machine learning algorithms, neural networks, image processing, visualization techniques, etc.).

Machine Learning Algorithms: A set of tools that classifying a large amount of data can predict different dynamics in several areas of interest through several methods and tools such as object representation, optimization processes (Bayesian theorem), and neural networks. These tools aim to reduce the complexity of the data while searching for hidden patterns. To this end, these algorithms perform a basic cognitive function of the development of human cognition as learning through experience. In other words, starting from the identification of a certain “law” on the scattered data, these algorithms create categories, similarly to the evolutionary processes occurring in childhood for the development of conceptual categories. This process allows the learning system to recognize the input data that belonging to a certain category.

Image Analytics: A process consisting of the analysis of images that facilitates investigation of images identifying objects, places, and people in order to extract significant information. Image recognition is applied to perform several tasks, such as providing labels on the content images, in other words it assigns each object included in the image to a certain category (i.e., house, food, person, etc.).

Big Data: Huge amount of structured (text fields such as name and phone number) and unstructured (e.g., video, images, gestures, etc.) data in sizes that exceed the capacity of traditional tools to manage them satisfactorily.

Artificial Intelligence (AI): Technology able to adaptively interact with customers. It consists of the simulation of human intelligence by machines (computer systems) through different processes including learning (machine learning), reasoning and self-correction.

Sentiment Analytics: A process producing computing sentiment scores for each text, which can be used to quantify the positivity and emotional intensity with which people talk about a certain brand, product, service, and shopping.

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