Using big data for customer centric marketing

This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe

Selection of Statistical Software for Solving Big Data Problems for Teaching

The need for analysts with expertise in big data software is becoming more apparent in 4 today’s society. Unfortunately, the demand for these analysts far exceeds the number 5 available. A potential way to combat this shortage is to identify the software sought by 6 employers and to align this with the software taught by universities. This paper will 7 examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and 8 it will outline the cost, training, statistical methods/tests/uses, and specific uses within 9 industry for each of these software. It will further explain implications for universities and 10 students (PDF

Applicability of artificial intelligence in e-commerce fashion platforms

A inovação tecnológica e a democratização da inteligência artificial (IA) têm vindo a alavancar o potencial de sucesso em todas as áreas que conhecemos hoje, com expectativas do que ainda está para vir. A presente dissertação propõe uma análise das aplicações da IA na indústria da moda, particularmente nas plataformas de marcas de moda do comércio eletrónico, e de que forma está a ter impacto na esfera pessoal do consumidor, particularmente no processo de tomada de decisão dos consumidores da Geração Z. O âmbito da IA tem vindo a evoluir de tal forma que permitiu às empresas não só melhorar a sua oferta e a procura dos clientes, como também proporcionar uma experiência de compra que vai para além da “seleção e compra” mecânica: os pontos de contacto impulsionados pela IA influenciam e enriquecem cada fase do processo de tomada de decisão, seja de forma mais positiva ou negativa. Em última análise, esta dissertação pretende proporcionar ao leitor um melhor conhecimento sobre a IA e o comércio eletrónico de moda, bem como delinear o seu impacto no comportamento online do consumidor.Technological innovation and democratization of artificial intelligence (AI) have been leveraging the potential success in every field we know today, while more is yet to come. The following dissertation proposes an analysis of AI achievements within the fashion industry, particularly in e-commerce fashion brand platforms, and how it is impacting the consumer personal sphere, particularly the decision-making process of Gen-Z consumers. The field of AI has been evolving in such a way that allows companies to not only improve their supply and customer demand, but also provide a shopping experience that goes beyond the mechanical “select and buy“: AI-driven touchpoints influence and enrich each stage of the decision-making process, whether more positively or negatively. Ultimately, this dissertation intends to provide the reader a better knowledge of AI and fashion e-commerce joining applications, and to delineate its impact on the online customer journey

Entrepreneurs'' attitude towards the computer and its effect on e-business adoption

This paper presents research exploring further the concept that many SMEs do not adopt computer based technologies due to decision maker's negative attitudes towards computers generally. Importantly, by assessing the entrepreneur's belief structure, we provide quantitative evidence how SMEs, particularly micros, are affected. Earlier research that addresses technology acceptance model (TAM) suggests that TAM parameters are particularly influential factors of e-commerce adoption, as perceived by top managers of SMEs. The model we develop is tested using a sample of 655 enterprises. The information was gathered, via a telephone survey of UK SMEs, from decisions makers in the enterprise. Technically, the paper uses k-means cluster analysis to segment respondents using the TAM perceptions, ease of use, usefulness and enjoyment. Based on two determined segments we look at the differential rate of adoption of internet, and the potential adoption of new e-collaborative technologies like video conferencing and electronic whiteboards. The diffusion of internet for low IT utility (LIT) segments was considerably slower than in the high utility segment (HIT). Similarly, the anticipated adoption of e-collaboration technologies was much lower for LIT than HIT. Interestingly, we find that LIT is populated by more micro SMEs than HIT. The results we present are limited however as our sample is considerably underweight in micro SMEs, suggesting that the problem may be much larger in the economy than our model predicts. For policy makers, this research confirms the value of knowledge transfer programs to SMEs in the form of technology support. Our research shows that organisations which have dedicated IT support will tend to be more advanced technologically than those that do not. The implication for entrepreneurs is if they can be persuaded that a technological route is beneficial to them, and that suitable support can be provided via KT, then operational efficiency gains could be made. This paper contributes to knowle

The Cinderella moment:Exploring consumers’ motivations to engage with renting as collaborative luxury consumption mode

Past literature argued that the purchase of luxury goods is driven by people’s motivation to conform or fit into our economic and social system. In this study, the authors focus on a new aspect of consumption, i.e. renting instead of purchasing luxury goods, backed by the emerging opportunities of sharing economy platforms. Drawing upon the analysis of spontaneous consumers’ online communications (in the form of tweets), this research aims to investigate the motivations to engage with luxury garment renting within a collaborative consumption context. To this end, a series of automatic content analyses, via two studies, were conducted using the tweets posted with respect to the Run the Runway collaborative consumption platform. Results demonstrate consumers’ increased willingness to show their social status through renting rather than owning luxurious apparel based on five main motivators (need to wear new clothes for a special event, inspirations created by the products/brands, possibility to explore a new way of consuming luxury goods, need to make more sustainable choices, and to increase the life cycle of each luxury product). The implications of these findings are discussed, while they pave the way for future research in collaborative consumption of luxury retailing

The Multi-Modal Universe of Fast-Fashion: The Visuelle 2.0 Benchmark

We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using only the time series in long-term forecasting scenarios, ameliorating the WAPE by 8.2% and the MAE by 7.7%

Digital Disruption beyond Uber and Airbnb – tracking the long tail of the sharing economy

The sharing economy can be regarded as a discontinuous innovation that creates increased abundance throughout society. Extant literature on the sharing economy has been predominantly concerned with Uber and Airbnb. As little is known about where the sharing economy is gaining momentum beyond transportation and accommodation, the purpose of this paper is to map in what sectors of the economy it is perceived to gain traction. Drawing on data from social and traditional media in Sweden, we identify a long tail of 17 sectors and 47 subsectors in which a total of 165 unique sharing-economy actors operate, including sectors such as on-demand services, fashion and clothing, and food delivery. Our findings therefore point at the expanding scope of the sharing economy and relatedly, we derive a set of implications for firms