• Journal of Information Technology Services
• /
• v.17 no.2
• /
• pp.207-218
• /
• 2018

Recently, it becomes a big trend in the banking industry to apply a big data analytics technique to extract essential knowledge from their customer database. Such a trend is based on the capability to analyze the big data with powerful analytics software and recognize the value of big data analysis results. However, there exits still a need for more systematic theory and mechanism about how to adopt a big data analytics approach in the banking industry. Especially, there is no study proposing a practical case study in which big data analytics is successfully accomplished from the marketing perspective. Therefore, this study aims to analyze a target marketing case in the banking industry from the view of big data analytics. Target database is a big data in which about 3.5 million customers and their transaction records have been stored for 3 years. Practical implications are derived from the marketing perspective. We address detailed processes and related field test results. It proved critical for the big data analysts to consider a sense of Veracity and Value, in addition to traditional Big Data's 3V (Volume, Velocity, and Variety), so that more significant business meanings may be extracted from the big data results.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.205-211
• /
• 2012

This paper presents research result of digital marketing model for improving enterprise marketing campaign performance. Recently, the enterprises which had completed projects such as ERP, CRM, and SCM for business value chain process transformation are working to improve enterprise marketing process. It is the trend for enterprises to use digital marketing tactics to overcome the limit of existing traditional marketing tactics. Especially, enterprises try to adopt digital marketing for marketing campaign performance. In this paper, digital marketing research model and hypothesis were established and statistically analyzed by marketing expert survey research. The research finding is that Web Analytics, Social Analytics, Personalized CRM, Campaign execution automation, Real-Time campaign management can be core influencers for marketing campaign performance improvement.

• Journal of Distribution Science
• /
• v.21 no.1
• /
• pp.53-63
• /
• 2023

Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

• The Journal of Asian Finance, Economics and Business
• /
• v.10 no.2
• /
• pp.109-121
• /
• 2023

This study aims to address the literature gap by examining the direct relationship between big data analytics capability, marketing innovation, and organizational innovations. Additionally, this study would examine big data analytics capability as the antecedent for both innovation types and how these relationships influence firm performance. The research model is developed based on the integration of resource-based view and knowledge-based view theories. The quantitative method is used as the research methodology for this study. Based on a purposive sampling method, a total of 115 questionnaires were obtained from managers in star-rated hotels located in Malaysia. Partial least square structural equation modeling (PLS-SEM) is utilized for the data analysis. The result shows that big data analytics capability positively affects marketing and organizational innovations. The findings show that big data analytics capability and organizational innovation positively influence firm performance. Nonetheless, the result revealed that marketing innovation is not positively related to firm performance. The findings also indicate to hotel managers the importance of big data analytic capability and the resources required to build and develop this capability. The contributions from this study enrich the literature on big data and innovation, which is particularly limited in the hospitality and tourism context.

• International Journal of Contents
• /
• v.17 no.1
• /
• pp.1-10
• /
• 2021

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

• Journal of Smart Tourism
• /
• v.2 no.2
• /
• pp.15-19
• /
• 2022

Destination marketing organizations (DMOs) utilize the official website for marketing and promotional purposes, while tourists often navigate through the official website to gather necessary information for their upcoming trips. With the advancement of business analytics, DMOs may need to exploit the clickstream data generated through their official website to develop more suitable and persuasive strategic marketing and promotional activities. As such, the primary objective of the current study is to show whether clickstream data can successfully identify the trip purposes of a particular user. Using a latent class analysis and multinomial logistic regression, this study found the meaningful and statistically significant variations in webpage visits among different trip purpose groups (e.g., weekend getaways, day-trippers, and other purposes). The findings of this study would provide a foundation for more data-centric destination marketing and management practice.

• Journal of Practical Engineering Education
• /
• v.16 no.1_spc
• /
• pp.95-103
• /
• 2024

With the rapid decline of the school-age population, the competition for admissions has increased and marketing through digital channels has become more important, so universities are investing more resources in online promotion and communication to recruit new students. This study uses Google Analytics, a web log analysis tool, to track the visitor behavior of a university admissions website and establish a digital marketing strategy based on it. The analysis period was set from July 1, 2023, when Google Analytics 4(GA4) was integrated, to January 10, 2024, when the college admissions process was completed. The analysis revealed interesting patterns such as geographical information based on visitors' access location, devices(operating systems) and browsers used by visitors, acquisition channels through visitors traffic, conversions on pages and screens that visitors engaged with and visitor flow. Based on this study, we expect universities to find ways to strengthen their admission promotion through digital marketing and effectively communicate with applicants to gain a competitive edge.

• Journal of Smart Tourism
• /
• v.4 no.2
• /
• pp.5-14
• /
• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

• The Journal of Asian Finance, Economics and Business
• /
• v.10 no.2
• /
• pp.15-27
• /
• 2023

The purpose of this research is to investigate the impact of Business Intelligence (BI) on the relation between Big Data Analytics (BDA) and Financial Performance (FP), at the beginning we reviewed the academic accounting and finance literature to develop the theoretical framework of business intelligence, big data and financial performance in terms of definition, motivations and theories, then we conduct an empirical analysis based on questionnaire-base survey data collected. The researchers identified the study population in the joint-stock companies listed on the Egyptian Stock Exchange and operating in the sectors and activities related to modern technologies in information systems, big data analytics, and business intelligence, in addition to the auditing offices that review the financial reports of these companies, and The sector closest to the research objective is the communications, media, and information technology sector, where the survey list was distributed among the sample companies with (15) lists for each company, and (15) lists for each audit office, so that the total sample becomes (120) individuals (with a response rate 83.3%), The results show, First, Big data analytics significantly affect organizations' financial performance, second, Business intelligence mediates (partial) the relationship between big data analytics and financial performance.

• Asia Marketing Journal
• /
• v.20 no.2
• /
• pp.65-83
• /
• 2018

Technological advances are bringing autonomous vehicles to the ever-evolving transportation system. Anticipating adoption of these technologies by users is essential to vehicle manufacturers for making more precise production and marketing strategies. The research investigates regulatory focus and consumer innovativeness with consumers' adoption of autonomous vehicles (AVs) and to consumers' subsequent willingness to pay for AVs. An online questionnaire was fielded to confirm predictions, and regression analysis was conducted to verify the model's validity. The results show that a promotion focus does not have a significantly positive effect on the automation level at which consumers will adopt AVs, but a prevention focus has a significantly positive effect on conditional AV adoption. Consumer innovativeness, consumers' novelty-seeking have a significantly positive relationship with high and full AV adoption, and consumers' independent decision-making has a significantly positive effect on full AV adoption. The higher the level of automation at which a consumer adopts AVs, the higher the willingness to pay for them. Finally, using a neural network and decision tree analyses, we show methods with which to describe three categories for potential adopters of AVs.