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Journal articles on the topic 'Data-Driven Markets'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Helbich, Marco, Wolfgang Brunauer, Julian Hagenauer, and Michael Leitner. "Data-Driven Regionalization of Housing Markets." Annals of the Association of American Geographers 103, no. 4 (2013): 871–89. http://dx.doi.org/10.1080/00045608.2012.707587.

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Dinter, Barbara, and Jan Krämer. "Data-driven innovations in electronic markets." Electronic Markets 28, no. 4 (2018): 403–5. http://dx.doi.org/10.1007/s12525-018-0316-3.

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Selz, Dorian. "From electronic markets to data driven insights." Electronic Markets 30, no. 1 (2020): 57–59. http://dx.doi.org/10.1007/s12525-019-00393-4.

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Kathuria, Vikas. "Greed for data and exclusionary conduct in data-driven markets." Computer Law & Security Review 35, no. 1 (2019): 89–102. http://dx.doi.org/10.1016/j.clsr.2018.12.001.

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Trenevska Blagoeva, Kalina, and Marina Mijoska Belsoska. "DEVELOPING DATA DRIVEN PRODUCTS IN THE EMERGING MARKETS." KNOWLEDGE INTERNATIONAL JOURNAL 30, no. 1 (2019): 197–202. http://dx.doi.org/10.35120/kij3001197t.

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The expansion of the digital economy and the rapid developments of technology induced the creation of new products and industries and drove a significant increase in data resources. The information products industry, including products based on data, information and knowledge, is intensely dynamic in terms of growth and the pace of new product introduction. The complexity in the variety of product offerings and the number of firms offering those products in this industry is increasing exponentially every day. Data-driven innovation forms a key pillar in the 21 century sources of growth. Large

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Talekar, P. R. "Data-Driven Marketing: Leveraging Data for Targeted Marketing Success." International Journal of Advance and Applied Research 5, no. 10 (2024): 29–31. https://doi.org/10.5281/zenodo.11298367.

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Data-driven marketing gathers and builds strategies based on big data to inform marketing decisions and personalize the customer experience. Now, in today’s digital world businesses have access to extensive amounts of data from various sources including customer interactions, Online behavior demographic information, and market trends harnessing this data through advanced analytics and technology markets can acquire a profound perception of targeted audiences, and create more personalized, relevant, and impactful campaigns. A data-driven media arrangement is now assisted by the massi

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Chiang, Wen-Yu. "Establishing high value markets for data-driven customer relationship management systems." Kybernetes 48, no. 3 (2019): 650–62. http://dx.doi.org/10.1108/k-10-2017-0357.

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PurposeOnline customer relationship management (CRM) is an important issue for implementing digital marketing of electronic commerce or social commerce. The purpose of this study is to establish valuable markets for discovering customer knowledge from data-driven CRM systems for enhancing growth rates of businesses. Airline or travel agency industries are online businesses in the world. Therefore, the industries in Taiwan will be an empirical case for this study.Design/methodology/approachThis research applied a procedure with an applied proposed model for establishing valuable markets from da

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Ritala, Paavo, Joona Keränen, Jessica Fishburn, and Mika Ruokonen. "Selling and monetizing data in B2B markets: Four data-driven value propositions." Technovation 130 (February 2024): 102935. http://dx.doi.org/10.1016/j.technovation.2023.102935.

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Woźniak-Cichuta, Monika. "Digital Data-Driven Mergers: Is a Data-Sharing Remedy a Panacea?" Yearbook of Antitrust and Regulatory Studies 17, no. 29 (2024): 9–48. http://dx.doi.org/10.7172/1689-9024.yars.2024.17.29.1.

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The article contributes to the current debate on the interplay between the data economy and competition law. First, on the basis of theories of harm related to datadriven merger, it is stated that such transactions require a particular assessment in merger control proceedings, rather than having them cleared unconditionally during phase 1. Such examination should take into consideration data-induced market power, not necessarily related to traditionally defined relevant markets. Therefore, it is postulated to take an ecosystem perspective on harm stemming from digital data-driven mergers. Seco

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Paredes, Jose, Gerardo Simari, Maria Martinez, and Marcelo Falappa. "First Steps towards Data-Driven Adversarial Deduplication." Information 9, no. 8 (2018): 189. http://dx.doi.org/10.3390/info9080189.

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In traditional databases, the entity resolution problem (which is also known as deduplication) refers to the task of mapping multiple manifestations of virtual objects to their corresponding real-world entities. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual objects appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wis

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Wang, Harry, J. Leon Zhao, and Guoqing Chen. "Managing Data Security in E-Markets through Relationship Driven Access Control." Journal of Database Management 23, no. 2 (2012): 1–21. http://dx.doi.org/10.4018/jdm.2012040101.

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Data security in e-markets is vital to maintaining trust among trading partners. In an e-market, companies must share information to improve operational efficiency in their supply chains, while at the same time, access to sensitive information by rival companies should be prevented. In today’s highly dynamic business environment, the relationships among companies in e-markets are constantly changing while these relationships determine how company information should be shared with other companies. In this paper, the authors show that existing access control models are not designed for managing

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Van Miegroet, Hans J., Kaylee P. Alexander, and Fiene Leunissen. "Imperfect Data, Art Markets and Internet Research." Arts 8, no. 3 (2019): 76. http://dx.doi.org/10.3390/arts8030076.

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The sheer volume of data generated on the Internet has reached unprecedented numerical heights and has enabled new data-driven methodologies to study art and its markets. Yet, this type of data-driven research has also generated several unexpected methodological constraints for art markets researchers, particularly due to informational asymmetry. This observation is related to how various players on the Internet make data available, as well as summarize, transmit, gather, and access those data globally. It is not our ambition to present another historiography of art markets research, past and

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Vansh Momaya, Siddharth Hefa, Yash Bhate, and Dr. Nilesh Marathe. "Bridging AI and Financial Markets: A Sentiment Analysis Data-Driven Approaches for Stock Market Prediction." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3667–75. https://doi.org/10.32628/cseit25112845.

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The conventional financial value estimating techniques rely primarily on historical stock data, technical indicators, and fundamental parameters, and frequently ignore the psychological and sentiment-driven characters. The most research efforts in ML-based stock prediction models, focusing on decision fusion techniques, hybrid ensemble learning, deep neural networks, and sentiment-aware financial forecasting models. The recently designed time-series predicting models like RNN, CNN, and LSTM show a certain level of accuracy of the model. However, the market trend is influenced by social media m

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Backer, Larry Catá. "China's Social Credit System: Data-Driven Governance for a ‘New Era’." Current History 118, no. 809 (2019): 209–14. http://dx.doi.org/10.1525/curh.2019.118.809.209.

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Robertson, John, Vivin Paliath, Jana Shakarian, Amanda Thart, and Paulo Shakarian. "Data Driven Game Theoretic Cyber Threat Mitigation." Proceedings of the AAAI Conference on Artificial Intelligence 30, no. 2 (2016): 4041–46. http://dx.doi.org/10.1609/aaai.v30i2.19082.

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Penetration testing is regarded as the gold-standard for understanding how well an organization can withstand sophisticated cyber-attacks. However, the recent prevalence of markets specializing in zero-day exploits on the darknet make exploits widely available to potential attackers. The cost associated with these sophisticated kits generally precludes penetration testers from simply obtaining such exploits – so an alternative approach is needed to understand what exploits an attacker will most likely purchase and how to defend against them. In this paper, we introduce a data-driven security g

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Huang, Hao, Chuyu Zhao, and Xiangyi Liu. "Data driven Comprehensive Replenishment and Pricing Model." Highlights in Science, Engineering and Technology 101 (May 20, 2024): 481–87. http://dx.doi.org/10.54097/13h79z41.

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Pricing and replenishment of vegetable commodities is a key step in solving the practical problem of short shelf life in vegetable markets. Our research introduces a nuanced, data-driven approach to optimize replenishment and pricing decisions. At the core of our methodology is the development of a comprehensive model that leverages an enhanced ARIMA, meticulously tailored for distinct vegetable categories. This model is designed to capture the intricate dynamics of market demand and supply fluctuations, enabling precise predictions that guide effective pricing and replenishment strategies. To

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Nuccio, Massimiliano, and Marco Guerzoni. "Big data: Hell or heaven? Digital platforms and market power in the data-driven economy." Competition & Change 23, no. 3 (2018): 312–28. http://dx.doi.org/10.1177/1024529418816525.

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Digital transformation has triggered a process of concentration in several markets for information goods with digital platforms rising to dominate key industries by leveraging on network externalities and economies of scale in the use of consumer data. The policy debate, therefore, focuses on the market control allegedly held by incumbents who build their competitive advantage on big data. In this paper, we evaluate the risk of abuse of a dominant position by analysing three major aspects highlighted in economic theory: entry barriers, price discrimination, and potential for technological impr

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Yang, Changhee, Yongjin Lee, and Chulung Lee. "Data-Driven Order Consolidation with Vehicle Routing Optimization." Sustainability 17, no. 3 (2025): 848. https://doi.org/10.3390/su17030848.

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This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear

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Foluke Ekundayo. "Economic implications of AI-driven financial markets: Challenges and opportunities in big data integration." International Journal of Science and Research Archive 13, no. 2 (2024): 1500–1515. https://doi.org/10.30574/ijsra.2024.13.2.2311.

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The integration of Artificial Intelligence [AI] and Big Data into financial markets has revolutionized their dynamics, offering unprecedented opportunities and posing complex challenges. This article examines the transformative impact of AI-driven financial systems on market operations, with a focus on algorithmic trading, market efficiency, and economic stability. AI-powered models enable rapid decision-making and data-driven strategies, enhancing liquidity and reducing transaction costs. However, these advancements also introduce volatility, systemic risks, and ethical concerns, necessitatin

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Kumar Saw, Abhishek, and Rachit Arora. "The Impact of Artificial Intelligence on big Data Analytics in Facilitating Data-Driven and Strategic Decision-Making in Financial Markets." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44601.

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The rapid advancements in Artificial Intelligence (AI) and Big Data Analytics have significantly transformed financial markets, enabling more precise, data-driven, and strategic decision-making. This study explores the profound impact of AI-driven analytics in financial decision-making, focusing on how machine learning algorithms, predictive analytics, and automation enhance market efficiency, risk assessment, and investment strategies. The research highlights how AI-powered Big Data Analytics processes vast amounts of structured and unstructured financial data to identify patterns, trends, an

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Wong, Kok-Seng, and Myung Ho Kim. "Privacy Protection for Data-Driven Smart Manufacturing Systems." International Journal of Web Services Research 14, no. 3 (2017): 17–32. http://dx.doi.org/10.4018/ijwsr.2017070102.

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The Industrial Internet of Things (IIoT) is a new industrial ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. The integration of industry and IoT creates various attack surfaces and new opportunities for data breaches. In the IIoT context, it will often be the case that data is considered sensitive. This is because data will encapsulate various aspects of industrial operation, including highly sensitive information about products, business strategies, and companies. T

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Pillai, Vinayak. "Integrating AI-Driven Techniques in Big Data Analytics: Enhancing Decision-Making in Financial Markets." International Journal of Engineering and Computer Science 12, no. 07 (2023): 25774–88. http://dx.doi.org/10.18535/ijecs/v12i07.4745.

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The growing complexity and volume of financial data, driven by globalization and advancements in digital technologies, have significantly transformed decision-making processes in financial markets. This paper explores the integration of Artificial Intelligence (AI)-driven techniques in Big Data Analytics to enhance decision-making capabilities in the financial sector. AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), are reshaping the landscape of data analytics by providing more accurate predictions, uncovering market trends, and automa

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Dokumacı, Melis. "AI in Forecasting Financial Markets." Human Computer Interaction 8, no. 1 (2024): 127. https://doi.org/10.62802/1twmvt88.

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Artificial Intelligence (AI) is transforming the landscape of financial market forecasting, offering innovative approaches to predict trends, optimize investments, and mitigate risks. By leveraging machine learning, natural language processing (NLP), and advanced statistical methods, AI-driven models analyze vast amounts of structured and unstructured data in real time, uncovering patterns and insights beyond human capabilities. This research explores the application of AI in financial market forecasting, emphasizing techniques such as deep learning for time-series analysis, sentiment analysis

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Thomas Aerathu Mathew. "Enhancing data platform observability with AI-driven metadata analytics." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 039–47. https://doi.org/10.30574/wjaets.2025.15.2.0536.

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This article explores the transformative potential of AI-driven metadata analytics for enhancing data platform observability across modern enterprise ecosystems. As organizations navigate increasingly complex data landscapes comprising cloud warehouses, orchestration tools, and visualization platforms, traditional monitoring approaches fall short of providing comprehensive visibility. The integration of artificial intelligence with metadata management emerges as a solution that enables proactive issue detection, automated root cause analysis, and predictive insights. Through examining metadata

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25

Wang, Bingxing. "Big Data-Driven ESG Quantitative Investment Strategy." Journal of Economic Theory and Business Management 2, no. 2 (2025): 8–13. https://doi.org/10.70393/6a6574626d.323837.

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With sustainability becoming more important worldwide, investors are looking more closely at environmental, social, and governance (ESG) factors. This paper looks at how big data could help investors use ESG information effectively in quantitative investing. It discusses how the use of big data techniques can lead to more accurate and transparent ESG analyses. Using regression models, the study identifies a positive relationship between companies' ESG scores and their expected stock returns. It also illustrates how detailed big data analysis can enrich the evaluation of corporate ESG performan

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Katam, Brahma Reddy. "Revolutionizing Data Accessibility: AI-Driven Natural Language Interfaces for Democratizing Data Discovery." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41620.

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We live in an era where data fuels everything—from business decisions and healthcare innovations to government policies and financial markets. Yet, a vast majority of professionals, the very people who need this data the most, struggle to access it because of technical barriers. Data isn’t just sitting there; it’s locked away behind SQL queries, complex BI dashboards, and intimidating APIs. This paper explores how we can break down these barriers and build truly user-friendly data access tools for non-technical users. We introduce a novel framework that combines AI-powered natural language que

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Shittu, Adenike Kudirat. "Optimizing Sales Operations in Industrial Solutions: A Data-Driven Approach for Global Markets." International Journal of Management and Organizational Research 4, no. 1 (2025): 101–17. https://doi.org/10.54660/ijmor.2025.4.1.101-117.

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Optimizing sales operations in the industrial solutions sector is critical for achieving sustainable growth in highly competitive global markets. This study explores a data-driven approach to transforming sales processes by leveraging advanced analytics, artificial intelligence (AI), and customer relationship management (CRM) technologies. With the increasing complexity of industrial solutions and the demand for personalized offerings, traditional sales strategies are often inadequate to meet evolving market requirements. The research examines how data-driven methodologies enable precise segme

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Feng, Yuanhua. "Data-driven estimation of diurnal patterns of durations between trades on financial markets." Statistics & Probability Letters 92 (September 2014): 109–13. http://dx.doi.org/10.1016/j.spl.2014.05.011.

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Guo, Hongye, Qixin Chen, Yuxuan Gu, Mohammad Shahidehpour, Qing Xia, and Chongqing Kang. "A Data-Driven Pattern Extraction Method for Analyzing Bidding Behaviors in Power Markets." IEEE Transactions on Smart Grid 11, no. 4 (2020): 3509–21. http://dx.doi.org/10.1109/tsg.2019.2962842.

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Yang, Jingwen. "Data-Driven Investment Strategies in International Real Estate Markets: A Predictive Analytics Approach." International Journal of Computer Science and Information Technology 3, no. 1 (2024): 247–58. http://dx.doi.org/10.62051/ijcsit.v3n1.32.

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The study investigates the application of advanced predictive analytics in formulating investment strategies for the international real estate market. Utilizing extensive datasets, including real estate transaction records, economic indicators, and market reports, covering over ten years of data from 2010 to 2020 across multiple regions, we implemented predictive models such as linear regression, decision trees, random forests, support vector machines (SVM), neural networks, and gradient boosting machines (GBM). The results indicate that AI and machine learning models significantly outperform

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Alaql, Abeer Abdullah, Fahad AlQurashi, and Rashid Mehmood. "Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media." Journalism and Media 4, no. 1 (2023): 120–45. http://dx.doi.org/10.3390/journalmedia4010010.

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We live in the information age and, ironically, meeting the core function of journalism—i.e., to provide people with access to unbiased information—has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets; people’s skills and education; and businesses and industries from multi-genera

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Omowonuola Ireoluwapo Kehinde Olanrewaju, Portia Oduro, and Olusile Akinyele Babayeju. "Exploring capital market innovations for net zero goals: A data-driven investment approach." Finance & Accounting Research Journal 6, no. 6 (2024): 1091–104. http://dx.doi.org/10.51594/farj.v6i6.1244.

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This paper examines the role of capital market innovations in advancing net-zero goals through a data-driven investment approach. In the face of escalating climate change concerns, achieving net-zero emissions has become a global imperative. Traditional investment strategies have often overlooked sustainability considerations, but recent innovations in capital markets are enabling a shift towards more responsible investing practices. This paper explores how data-driven investment strategies can be leveraged to channel capital towards sustainable solutions while maximizing financial returns. Th

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Yuaniko Paramitra, Syamsu Rijal, Guellica Agnesia Claudia Thanos, et al. "Advances in Data-Driven Marketing Technologies: A Bibliometric Analysis of Indonesian Research Trends." Journal of Applied Science, Engineering, Technology, and Education 6, no. 2 (2024): 201–10. https://doi.org/10.35877/454ri.asci3811.

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Indonesia, the largest economy in Southeast Asia, has experienced an increase in need for innovative, data-driven marketing research given its fast economic expansion. Business strategy and decision making depend on this study as it let companies adjust to the specificities of local markets affected by socioeconomic, technological, and demographic variables. This study aims to assess the direction and breadth of the research as well as identify any knowledge gaps or underutilized studies in the field of marketing, especially in Indonesia. Bibliometrics is the technique utilized by means of dat

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Andersen, Torben G. "THE ECONOMETRICS OF FINANCIAL MARKETS." Econometric Theory 14, no. 5 (1998): 671–85. http://dx.doi.org/10.1017/s0266466698145073.

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The abundance of high-frequency financial data and the rapid development of computer hardware have combined to transform financial economics into, arguably, the most empirically oriented field within the social sciences. At the same time, as a result of the difficulty of conducting genuine market experiments, empirical finance remains firmly grounded in the tradition of model-driven statistical inference that is characteristic of economics. Even so, the richness of data has often spurred a practical orientation that is more familiar in the natural sciences. The combination has proved fertile,

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35

Enajero, Jude. "The Impact of AI-Driven Predictive Models on Traditional Financial Market Volatility: A Comparative Study with Crypto Markets." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 416–27. https://doi.org/10.35629/5252-0701416427.

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This study investigates the effectiveness of AIdriven predictive models in managing volatility in traditional financial markets compared to cryptocurrency markets. By examining the impact of these models on market stability, the research aims to identify best practices and areas for improvement in the deployment of AI technologies across different asset classes. We employed a Maximum Likelihood (ML) ARCH model to analyze market volatility, effectively capturing the time-varying volatility characteristics inherent in financial data. Our empirical framework integrates AI-driven metrics, such as

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Mohtasim Wasif, Sujana Samia, Md Sohanur Rahman Sourav, Arafat Hossain, and Md Redwanul Islam. "Data-Driven insights on the relationship between BRICS financial policies and global investment trends." Journal of Economics, Finance and Accounting Studies 7, no. 2 (2025): 133–47. https://doi.org/10.32996/jefas.2025.7.2.12.

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This study investigates the dynamic relationship between the financial policies of BRICS nations—Brazil, Russia, India, China, and South Africa—and global investment trends. As emerging markets like the BRICS play a crucial role in the global economic growth, it is critical to understand how changing in the financial policies in these markets interact with international investment flows for both investors and policymakers. The study leverages data of economic indicators, policy measures, and global investment patterns by building regression, decision trees and deep learning models based on adv

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Ria Setyawati, Stefan Koos, and Zalfa A.F. Jatmiko. "Data Driven Dominance in Digital Markets: Assessing Indonesian Competition Law in the Digital Age." Jurnal IUS Kajian Hukum dan Keadilan 12, no. 2 (2024): 264–84. http://dx.doi.org/10.29303/ius.v12i2.1377.

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Digital markets and multi-sided platforms are created by the internet, that are characterized by the use of big data as new market power. Big data enabled dominant and digital-based key players such as Instagram and Facebook/Meta to record and forecast their users’ personal data and spending capacity to increase their economies of scale by tailoring updates to the users’ demand. Therefore, big data becomes an essential market share, and its scarcity determines newer entrants’ ability to enter the market and the existing incumbents’ ability to survive by grappling with fast-paced digital change

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Ramudu, P. Janaki, and Santosh Shrivastav. "OPPORTUNITIES AND REQUIRED SKILLS IN DATA-DRIVEN JOBS: EMPIRICAL EVIDENCE FROM INDIAN JOB MARKETS." Advances and Applications in Statistics 82 (November 11, 2020): 101–24. http://dx.doi.org/10.17654/0972361722082.

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Anand, A., J. Petzschmann, K. Strecker, R. Braunbehrens, A. Kaifel, and C. L. Bottasso. "Profit-optimal data-driven operation of a hybrid power plant participating in energy markets." Journal of Physics: Conference Series 2767, no. 9 (2024): 092069. http://dx.doi.org/10.1088/1742-6596/2767/9/092069.

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Abstract An energy management system (EMS) is formulated for a hybrid power plant (HPP), consisting of a wind power plant and battery storage plant, participating in bidding stages in the German energy market. The EMS utilizes supervisory control and data acquisition (SCADA) measurements from the site to improve power forecast from the wind power plant. First, the measurement data are used together with numerical weather prediction data to accurately forecast local wind conditions. Second, the measurement data are used to adapt a baseline engineering wake model that gives the total wind power

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Nyangoma, Daphine, Ejuma Martha Adaga, Ngodoo Joy Sam-Bulya, and Godwin Ozoemenam Achumie. "Strategic Digital Marketing Models for Agribusiness: Connecting Producers to Markets through Data-Driven Platforms." Journal of Frontiers in Multidisciplinary Research 5, no. 1 (2024): 141–48. https://doi.org/10.54660/.ijfmr.2024.5.1.141-148.

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This paper explores the role of data-driven digital marketing in enhancing the connection between agribusiness producers and markets. As the agricultural sector faces challenges related to market access, inefficient supply chains, and limited consumer engagement, digital marketing platforms present innovative solutions for improving market visibility, optimizing supply chain management, and enhancing customer satisfaction. The research examines key strategies, including leveraging data analytics, consumer insights, e-commerce, and digital payment systems, to facilitate direct market access for

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Ranjani, K. S., Sumi Jha, and Neeraj Pandey. "DealShare: Value Based Positioning in B2C Markets." Emerald Emerging Markets Case Studies 14, no. 1 (2024): 1–23. http://dx.doi.org/10.1108/eemcs-10-2023-0368.

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Learning outcomes After reading this case study, the students will be able to identify the various choices available in social e-commerce using network marketing, interpret data-driven decisions in social e-commerce and evaluate their role in scaling business, analyse cost and revenue management in value segments, evaluate technology adoption among the masses using appropriate communication structures and develop customer relationships and manage their sentiments in the era of social media. Case overview/synopsis DealShare became a unicorn in 2022 and targeted the rural and low-income groups.

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Latif, Saima, Faheem Aslam, Paulo Ferreira, and Sohail Iqbal. "Integrating Macroeconomic and Technical Indicators into Forecasting the Stock Market: A Data-Driven Approach." Economies 13, no. 1 (2024): 6. https://doi.org/10.3390/economies13010006.

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Forecasting stock markets is challenging due to the influence of various internal and external factors compounded by the effects of globalization. This study introduces a data-driven approach to forecast S&P 500 returns by incorporating macroeconomic indicators including gold and oil prices, the volatility index, economic policy uncertainty, the financial stress index, geopolitical risk, and shadow short rate, with ten technical indicators. We propose three hybrid deep learning models that sequentially combine convolutional and recurrent neural networks for improved feature extraction and

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Afifi, Nehal, Victor Mas, Jonas Hemmerich, et al. "Data-Driven Decision-Making: Leveraging Digital Twins for Reprocessing in the Circular Factory." Zeitschrift für wirtschaftlichen Fabrikbetrieb 120, s1 (2025): 170–76. https://doi.org/10.1515/zwf-2024-0160.

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Abstract Circular factories must ensure the functionality and reliability of used components for recombination with other components or subsystems from the same or different product generations. This paper presents a data-driven decision-making framework integrating the Functional Behavior Model and System Reliability Model within a Digital Twin. Data from physical testing is continuously incorporated, simulating recombination scenarios and guiding decision-making on component reprocessing. An angle grinder is used as a case study for demonstration. The proposed framework enhances sustainabili

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Sciaba, Andrea, Helge Meinhard, Germán Cancio, et al. "Trends in computing technologies and markets: The HEPiX TechWatch WG." EPJ Web of Conferences 245 (2020): 07056. http://dx.doi.org/10.1051/epjconf/202024507056.

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Driven by the need to carefully plan and optimise the resources for the next data taking periods of Big Science projects, such as CERN’s Large Hadron Collider and others, sites started a common activity, the HEPiX Technology Watch Working Group, tasked with tracking the evolution of technologies and markets of concern to the data centres. The talk will give an overview of general and semiconductor markets, server markets, CPUs and accelerators, memories, storage and networks; it will highlight important areas of uncertainties and risks.

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Shukla, Varun, and Pradip Patil. "Enhancing Customer Sales Prediction through Advanced Data Visualization Techniques: A Data-Driven Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem37680.

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This research paper examines advanced techniques used in visualizing data in regard to improved sales prediction by the customers within an enterprise. The data-driven decision-making process is explored better through the enhancing of the accuracy of sales forecast and actual understanding of customers' behaviour. There are steps begun with data cleaning to create accuracy and reliability. This is then followed by detailed analysis on trends, patterns, and perhaps sales drivers. The study unfolds the importance of visual tools, dashboards, charts, and graphs, that translate complex analytical

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Gaur, Aditya. "ML Based Macroeconomic Model Simulator." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45585.

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Abstract - This AutoFi, a cutting-edge AI-driven macroeconomic simulation model, provides a robust framework for forecasting financial markets. Financial markets are deeply influenced by macroeconomic indicators such as GDP, unemployment rates, inflation, and interest rates. Accurate forecasting of these indicators is critical for optimizing investment strategies. This research presents AutoFi, a machine learning-based macroeconomic simulation model that predicts key financial indicators and asset performance over time. The proposed system incorporates historical macroeconomic data, time serie

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Akter, Shahriar, Md Afnan Hossain, Qiang (Steven) Lu, and S. M. Riad Shams. "Big data-driven strategic orientation in international marketing." International Marketing Review 38, no. 5 (2021): 927–47. http://dx.doi.org/10.1108/imr-11-2020-0256.

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PurposeBig data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.Design/methodology/approachThe study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databa

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Oosthoek, Kris, Jack Cable, and Georgios Smaragdakis. "A Tale of Two Markets: Investigating the Ransomware Payments Economy." Communications of the ACM 66, no. 8 (2023): 74–83. http://dx.doi.org/10.1145/3582489.

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Wang, Jiufan, Qi Xin, Yuning Liu, Junliang Wang, and Tianyi Yang. "Predicting Enterprise Marketing Decision Making with Intelligent Data-Driven Approaches." Journal of Industrial Engineering and Applied Science 2, no. 3 (2024): 12–19. https://doi.org/10.5281/zenodo.11357252.

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In order to improve the marketing effect of new media, traditional enterprises should increase the proportion of new media advertising and pay attention to the flow of audience's attention. On the basis of integrating data and information, advertising should conform to the changing trend of the media environment and increase the proportion of new media, so as to understand the audience's consumption habits of media use and formulate a reasonable media mix plan. Promote the integration of traditional media and new media to obtain higher publicity results at the lowest cost. This article delves

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Dutulescu, Andreea, Andy Catruna, Stefan Ruseti, et al. "Car Price Quotes Driven by Data-Comprehensive Predictions Grounded in Deep Learning Techniques." Electronics 12, no. 14 (2023): 3083. http://dx.doi.org/10.3390/electronics12143083.

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The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduced two datasets from different markets, one with over 300,000 entries from Germany to serve as a training basis for deep prediction models and a second dataset from Romani

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