Are black friday deals worth it? Mining twitter users' sentiment and behavior response

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area

Improving awareness and reinforcing brand positioning in savings and investments in a digital era: Pedagogical case study

The recent Covid19 pandemic put the entire world in lockdown and has been limiting people’s movement. This means that Banks have the urge necessity to increase their online presence and improve the online services provided to attract more clients. With the emergence of the pandemic, BiG needed to adapt to the market needs and adjust in a way that makes sense to BiG's core business. There had to be a migration to digital, since branches had to close "overnight" and invest in non-face-to-face care, which was not a priority until then. With all this happening, I joined the marketing department of Banco de Investimento Global, during the restructuring of the team, to help the bank grow and reinvent itself. Several campaigns were developed to attract new clients and promote BiG trading platforms. We created an ecosystem of digital channels and social networks that portray the bank essence. The impact of our marketing strategy was successful, showing that we put in place effective strategies. Banks do not have to have an aversion to change. They can adapt to new realities in a short period of time.Em 2020 uma pandemia entrou no mundo e fez com que, quer pessoas, quer setores se tivessem que reinventar, colocando o mundo inteiro em confinamento e limitando o movimento das pessoas. No caso do setor bancário, altamente dependente do contacto físico, teve que haver uma mudança de paradigma e mentalidade, de forma que usassem mais os meios digitais. De “um dia para o outro”, as agências tiveram que fechar, e um negócio tão assente nelas teve que se reinventar. Com a deslocalização das pessoas foi essencial investir no atendimento não presencial, algo que não era prioridade até então. Com tudo isto a acontecer, entrei para o departamento de marketing do Banco de Investimento Global, durante a restruturação da equipa, para ajudar o banco a crescer e reinventar, utilizando sempre estratégias que fizessem sentido para o core business do BiG. Foram desenvolvidas diversas campanhas e criado um ecossistema de canais digitais e redes sociais, tendo tido resultados positivos. O banco teve sucesso nas campanhas realizadas e mostrou ter implementado estratégias eficazes. Os bancos não têm que ter aversão à mudança e devem adaptar-se, num curto espaço de tempo, a novas realidades

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Listening in: Investigating Social Media Activity in the Streaming Service Industry

In this paper, we examine the social media activity surrounding three different brands (Hulu, Netflix, and Disney+) using two different and complimentary techniques. In Study 1, we use a popular social listening tool to examine quantitative data of different kinds including the share of voice of these brands as well as the major geographic markets and languages associated with these brands\u27 social media activity. These are three of the biggest brands in the over-the-top (OTT) industry and all three of these companies offer streaming services that are highly popular with consumers around the world. To get a better sense of the quantity and quality of the social media posts around these brands, we gathered and studied Twitter data for a four-week period using the Awario social listening tool. Building on this analysis, we then conduct a qualitative analysis of each brand\u27s social media activity using a netnographic, qualitative content analysis of branded social media posts that occurred during the aforementioned 4-week observation period in April 2020. This thesis begins with a literature review that focuses on the larger issue of big data and examines the various tools and techniques that firms use to interpret and act on their big data resources, especially social media posts by their fans and customers. We then move to a brief overview of the OTT industry to provide context for the data we have collected and to explain the competitive landscape in that sector. Next, building on the quantitative insights obtained in Study 1, Study 2 examines branded social media posts for these three brands and highlights the qualitative differences in tone, focus, and content that appear in posts that occurred during the observation period. Lastly, we conclude by briefly discussing the analytical approaches that were used for this research and considering the ways that marketers can use multimethod research techniques to acquire richer insights about their customers and their competitors

Evidence from hospitality loyalty programs

Rita, P., Tiago, M. T. B., & Caetano, J. (2023). The theory-practice research gains from big data: Evidence from hospitality loyalty programs. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-05-2022-0646 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, research grants UIDB/04521/2020 of the Advance/CSG, ISEG – Lisbon School of Economics and Management; and UIDB/00685/2020 of the Centre of Applied Economics Studies of the Atlantic, School of Business and Economics of the University of the Azores.Purpose The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs. Design/methodology/approach Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers. Findings This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers. Practical implications Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies. Originality/value As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.authorsversionepub_ahead_of_prin

Social media data analytics for the NSW construction industry : a study on Twitter

The primary aim of this dissertation is to explore the social interaction and relationship of people within the NSW construction industry through social media data analytics. The research objective is to perform social media data analytics through Twitter and explore the social interactions between different stakeholders in the construction industry to understand the real-world situations better. The data analytics was performed on Twitter tweets, retweets, and hashtags that were collected from four clusters on construction stakeholders in NSW, namely construction workers, companies, media, and union. Tweets, retweets, and hashtags that were collected from four clusters on construction stakeholders in NSW, namely construction workers, companies, media, and unions. The thesis seeks to perform social media data analytics in order to explore and investigate the social interactions and links between the different stakeholders that are present in the construction industry. Investigating these interactions will help reveal a multitude of other related social aspects about the stakeholders, e.g., their genuine attitudes about the construction industry and how they feel being involved in this field of work. In order to facilitate this research, a social media data analytics study was carried out to find out the links and associations that are present between the construction workers, companies, unions, and media group entities. Five types of analyses were performed, namely sentiment analysis, link analysis, topic modelling, geo-location analysis, and timeline analysis. The results indicated that there are minimal social interactions between the construction workers and the other three clusters (i.e., companies, unions, and the media). The main reason that has been attributed to this observation is the way workers operate in a rather informal and casual manner. The construction companies, unions, and the media define their behavior in a much more formal and corporate attitude, hence they tend to relate to one another more than they do with workers. A number of counteractive approaches may be enforced in an effort to restore healthy social relations between workers and the other three clusters. For example, the company management teams should endeavor to develop stronger interactions with the workers and improve the working conditions, in overall