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Journal articles on the topic 'Big data in marketing'
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Simakina, M. "Features of using Big Data technologies in marketing." Bulletin of Science and Practice 4, no. 6 (2018): 255–60. https://doi.org/10.5281/zenodo.1289864.
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The article discusses the applicability of Big Data technologies in modern marketing. The author analyzes the problems, limitations and risks of their application in practice. As a director of development of Big Data in marketing, the author focuses on Smart Data technologies.
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Graczyk-Kucharska, Magdalena. "Big Data as a Necessity of Modern Marketing." Zeszyty Naukowe Uniwersytetu Szczecińskiego. Problemy Zarządzania, Finansów i Marketingu 41 (2015): 265–78. http://dx.doi.org/10.18276/pzfm.2015.41/2-22.
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Pinarbasi, Fatih, and Zehra Nur Canbolat. "Big data in marketing literature." International Journal of Business Ecosystem & Strategy (2687-2293) 1, no. 2 (2019): 15–24. http://dx.doi.org/10.36096/ijbes.v1i2.107.
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The concept of big data is one of the important issues in business decision making in recent years. The expansion of social media platforms, the increase in data production devices and the evaluation and interpretation of the data produced by developing technology become crucial. Previous studies in the big data area have addressed the issue in limited contexts, and there are few studies in the field of marketing with a bibliometric approach. This study, which aims to examine how big data concept is evaluated in marketing literature, examines the publications on big data in indexed marketing journals using bibliometric methodology. This study starts with descriptive statistical information and then includes the top published journals, authors and corresponding author’s countries statistics. This study also includes most influential studies for big data concept in marketing literature, employs spectroscopy for detecting historical roots of studies and finally plots growth progress of keywords for predicting, future themes. This study contributes to current literature by providing a summarizing and instructive content for researchers interested in big data in marketing.
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Matthew, N. O. Sadiku, C. Chukwu Uwakwe, Ajayi-Majebi Abayomi, and M. Musa Sarhan. "Big Data in Industry: An Overview." Journal of Scientific and Engineering Research 8, no. 8 (2021): 144–52. https://doi.org/10.5281/zenodo.10612339.
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<strong>Abstract</strong> With the rise of digital technology, big data has remarkably revolutionized the industry standards. Big data refers to large, hard-to-manage volumes of data, both structured and unstructured. The marketing and sales of any industry largely depend on how data is used. Industries that are improving their services through big data include healthcare, manufacturing, agriculture, telecommunications, retail, fitness, travel, finance, e-commerce, entertainment, banking, oil and gas, sports, and social media. This paper is an overview on the application of big data in industries.
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Chasovskikh, V. P., and Е. V. Koch. "THE USE OF BIG DATA IN MARKETING RESEARCH." International Journal of Applied and Fundamental Research (Международный журнал прикладных и фундаментальных исследований), no. 3 2023 (2023): 47–50. http://dx.doi.org/10.17513/mjpfi.13521.
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Zheng, Shi, Xia Jin, and Wen Zheng. "Big Data Usage in Marketing Research." Frontiers in Business, Economics and Management 5, no. 3 (2022): 242–48. http://dx.doi.org/10.54097/fbem.v5i3.2029.
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In the marketing field, the use of big data in research can make us understand consumer deeply. In some areas of market research, big data is already established today. The social media analytics and the use of cookie data to measure internet coverage are two prominent examples. This essay combs through relevant literatures, discusses the big data uses in the marketing research and its contribution for decision-making. It presents a revision of main concepts about marketing research, the new possibilities of use and a reflection about limitations of big data in the marketing research.
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Diesperova, Natal'ya. "CAPABILITIES AND LIMITATIONS OF BIG DATA IN MARKETING." Russian Journal of Management 8, no. 4 (2021): 16–20. http://dx.doi.org/10.29039/2409-6024-2020-8-4-16-20.
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Using of Big Data increases the efficiency of solving of marketing tasks, including drawing up a portrait of a consumer and building communication with him, based on the analysis of information from social networks (Facebook) and search engines (Google). An assessment of the capabilities and limitations of these technologies showed that the use of Big Data doesn’t always provide the absolute best marketing solutions. Therefore, the use of Big Data seems to be justified in marketing only in a number of areas, directed by a professional marketer, for whom Big Data technologies are an effective tool that allows to prepare an analytical base for making creative decisions.
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Mamlouk, Lamia, and Olivier Segard. "Big Data and Intrusiveness: Marketing Issues." Indian Journal of Science and Technology 8, S4 (2015): 189. http://dx.doi.org/10.17485/ijst/2015/v8is4/71219.
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Zanella-Martínez, Leonardo Mauricio, Fidel Ricardo Chiriboga-Mendoza, and Enrique Cristóbal Zambrano-Pilay. "Big data y marketing de experiencias." Revista Científica Arbitrada de Investigación en Comunicación, Marketing y Empresa REICOMUNICAR 2, no. 3 (2019): 15–22. http://dx.doi.org/10.46296/rc.v2i3.0034.
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Hoy por hoy, con el auge del internet y de la era digital, las empresas tienen el deber de adaptar sus estrategias y sistemas metodológicos de comercio para lograr crear y mantener la competitividad en los mercados existentes. En este marco contextual, el marketing de experiencias es una de las formas del marketing contemporáneo que busca crear dependencia a través de determinado producto o servicio en los consumidores, ofreciendo recursos empíricos para crear preferencias en los consumidores de una marca con respecto a otra marca. El marketing de experiencias, siendo una de las tantas formas existentes del marketing relacional, ha sido modificado por las empresas para ser fácilmente transmitido a los consumidores a través de estrategias de comunicación digital en entornos digitales, como en el caso de los sitios web, redes sociales etc. Este proceso de recuperar, clasificar y detallar la información recopilada se conoce como "análisis de big data" y consiste en la adaptación de un conglomerado de información que tiene una estructura de datos integrada compleja con las notorias dificultades para buscar, almacenar, analizar, proteger, transferir y visualizar los datos recopilados. En particular, el análisis de big data es muy importante para las empresas con la base del comercio electrónico y el comercio digital, que pueden estudiar el comportamiento del consumidor, luego realizar un análisis detallado y finalmente obtener importantes beneficios en la competencia en un mercado determinado. Palabras claves: Big data, marketing, redes sociales, empresa, consumidor.
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Wu, Yiheng. "BIG DATA PROJECT - BANK MARKETING CAMPAIGN." European Journal of Economics and Management Sciences, no. 2 (2021): 3–15. http://dx.doi.org/10.29013/ejems-21-2-3-15.
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Barutçu, Merve Türkmen. "Big Data Analytics for Marketing Revolution." Journal of Media Critiques 3, no. 11 (2017): 163–71. http://dx.doi.org/10.17349/jmc117314.
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Chintagunta, Pradeep, Dominique M. Hanssens, and John R. Hauser. "Editorial—Marketing Science and Big Data." Marketing Science 35, no. 3 (2016): 341–42. http://dx.doi.org/10.1287/mksc.2016.0996.
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Lehenchuk, S. F., and T. O. Zavalii. "Big Data in marketing analytics: opportunities and problems of use." Problems of Theory and Methodology of Accounting, Control and Analysis, no. 1(54) (May 1, 2023): 52–58. http://dx.doi.org/10.26642/pbo-2023-1(54)-52-58.
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The relevance of conducting research in the direction of using Big Data to improve marketing activities has been grounded. A bibliometric analysis of publications on the subject of «Big Data and marketing» in the scientometric database Scopus has been carried out. The main aspects of using Big Data in marketing analytics have been grounded. The main advantages of using Big Data in marketing analytics have been analyzed. The prospects and opportunities for the development of marketing analytics based on the use of Big Data have been identified and characterized (improvement of interaction with customers; improved positioning of brands; optimization of the marketing budget; improvement of the effectiveness of predictive analytics). The main problems of using Big Data for the development of marketing analytics have been described (objective limitation of the use of Big Data; the time gap between the occurrence and receipt of processed data for decision-making; storage and processing of streaming data; the need to acquire new competencies by employees of the marketing department; the need to improve the structure of the marketing department).
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Qin, Yumeng. "Analysis and Application of Big Data in Social Media Marketing." International Journal of Global Economics and Management 2, no. 2 (2024): 266–72. http://dx.doi.org/10.62051/ijgem.v2n2.33.
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This paper discusses the importance and advantages of big data analysis and application in social media marketing. With the popularity of social media platforms, big data analysis provides enterprises with opportunities to deeply understand user needs, optimize marketing strategies and improve marketing effects. This paper introduces the current situation of social media marketing, and expounds in detail the application of big data analysis in user portrait analysis, user behavior analysis and marketing effect evaluation. Through big data analysis, enterprises can formulate more accurate marketing strategies, improve marketing accuracy, optimize user experience and improve marketing efficiency. However, big data analysis also faces challenges such as data quality and privacy protection, which requires enterprises to pay attention to data security and compliance in the process of application.
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Wu, Jiatong, Jun Zhang, and Jing Qiao. "Adaptive Integration Algorithm of Sports Event Network Marketing Data Based on Big Data." Security and Communication Networks 2022 (May 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/7660071.
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To address the issues of low-data integration accuracy and efficiency, as well as a lack of data integration impact, an adaptive data integration algorithm for sports event network marketing data based on big data is presented. The fundamental theory of tensor is researched by examining the notion and features of big data and by using the associated technologies of the big data framework. Collect network-marketing data from a variety of sporting events and feed it to a big data platform. Combined with MapReduce parallelization mode, tensor represents the online marketing data of sports events according to the structured, semistructured, and unstructured characteristics of different big data. Integrate each tensor model based on semitensor product, build a unified data adaptive integration tensor model, and realize the adaptive integration of sports event network marketing data. The experimental results show that the proposed algorithm has a good effect on the adaptive integration of sports event network marketing data and can effectively improve the accuracy and efficiency of data adaptive integration.
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Johnson, Devon S., Laurent Muzellec, Debika Sihi, and Debra Zahay. "The marketing organization’s journey to become data-driven." Journal of Research in Interactive Marketing 13, no. 2 (2019): 162–78. http://dx.doi.org/10.1108/jrim-12-2018-0157.
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Purpose This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation. Design/methodology/approach A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions. Findings The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization. Originality/value From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.
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Gold Nmesoma Okorie, Zainab Efe Egieya, Uneku Ikwue, et al. "LEVERAGING BIG DATA FOR PERSONALIZED MARKETING CAMPAIGNS: A REVIEW." International Journal of Management & Entrepreneurship Research 6, no. 1 (2024): 216–42. http://dx.doi.org/10.51594/ijmer.v6i1.778.
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The burgeoning field of big data analytics has revolutionized the landscape of marketing, offering unprecedented opportunities for personalized marketing campaigns. This review aims to synthesize the current state of knowledge on leveraging big data for personalized marketing, elucidating the objectives, methodologies, key findings, and conclusions drawn from recent research in this domain. The primary objective of this review is to explore how big data analytics can be effectively utilized to tailor marketing strategies to individual consumer preferences, behaviors, and patterns. Methodologically, the review adopts a comprehensive approach, examining a wide range of studies that employ various big data tools and techniques, including machine learning algorithms, data mining, and predictive analytics, in the context of personalized marketing. Key findings indicate that big data analytics significantly enhances the ability of marketers to understand and predict consumer behavior, leading to more effective targeting and segmentation strategies. The integration of big data has shown to improve customer engagement, satisfaction, and loyalty by delivering more relevant and timely marketing messages. However, challenges such as data privacy concerns, the need for advanced analytical skills, and the potential for data inaccuracies are also highlighted. In conclusion, while big data presents substantial opportunities for personalizing marketing campaigns, its effective implementation requires careful consideration of ethical implications, investment in technological infrastructure, and ongoing skill development. Future research directions include exploring the impact of emerging technologies like artificial intelligence and the Internet of Things (IoT) on personalized marketing, and developing frameworks for ethical data usage in marketing practices. This review underscores the transformative potential of big data in reshaping personalized marketing strategies, offering valuable insights for both practitioners and researchers in the field.
 Keywords: Big Data, Marketing Strategies, Consumer Behavior, Data Analytics, Personalized Marketing, Market Segmentation, Privacy Concerns, Ethical Challenges, Digital Transformation, Artificial Intelligence (AI).
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Bai, Chengwei, and Xiangting Chen. "A Case Study of Apples Success in Marketing Through Big Data Analysis." Advances in Economics, Management and Political Sciences 33, no. 1 (2023): 128–31. http://dx.doi.org/10.54254/2754-1169/33/20231647.
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Apple Inc. is one of the most successful technology companies in the world and has been at the forefront of the big data analytics and marketing revolution. The use of big data has become a key element in marketing, and Apple's approach is a good case study for companies seeking to use big data to enhance their marketing strategies. This paper will take it as a case study to discuss how to use big data to promote marketing. This paper will briefly introduce Apple and the concept of big data and marketing, then analyze how Apple applies big data to analyze the market, discuss Apples successful cases in marketing, and summarize Apples marketing strategies. A conclusion will be drawn at the end.
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Tajidan, Tajidan, Halil Halil, Fernandez Edy, Efendy Efendy, Nabilah Sharfina, and Mulyawati Sri. "The customer satisfaction of store pasarmandalika.com regarding the attributes of vegetable and fruit products and strategies for increasing sustainable sales turnover." International Journal of Management and Commerce Innovations 11, no. 1 (2023): 443–50. https://doi.org/10.5281/zenodo.8375689.
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<strong>Abstract:</strong> Online marketing is increasingly sought after by customers to meet their needs, however the online marketing business is considered risky due to sharp fluctuations in sales turnover from time to time. In an effort to anticipate this risk, research was carried out with the aim of determining the level of customer satisfaction and the sustainability strategy of the online marketing business for fruit and vegetable products. To achieve the research objectives, research was carried out on the customers of the pasarmandalika.com store. As customers are hotels, restaurants and fresh markets. Data collection is carried out on all customers using census techniques. Primary data was obtained from 14 population units in North Lombok Regency, Mataram City, West Lombok Regency and Central Lombok Regency in West Nusa Tenggara Province. Data collection was carried out by combining observation, survey and in-depth interview techniques. The collected data was analyzed using the Customer Satisfaction Index and continued with SWOT analysis to formulate a sustainable online marketing strategy. The results of the analysis show that the customers of the pasarmandalika.com store are satisfied with the attributes of vegetable and fruit products. To achieve sustainability in the online marketing business, the strategy chosen is to increase customer accessibility to the pasarmandalika.com website by increasing the capacity to become a big data-based business website. <strong>Keywords:</strong> big data, big data, turnover, web, accessibility. <strong>Title:</strong> The customer satisfaction of store pasarmandalika.com regarding the attributes of vegetable and fruit products and strategies for increasing sustainable sales turnover <strong>Author:</strong> Tajidan Tajidan, Halil Halil, Edy Fernandez, Efendy Efendy, Sharfina Nabilah, Sri Mulyawati <strong>International Journal of Management and Commerce Innovations </strong> <strong>ISSN 2348-7585 (Online)</strong> <strong>Vol. 11, Issue 1, April 2023 - September 2023</strong> <strong>Page No: 443-450</strong> <strong>Research Publish Journals</strong> <strong>Website: www.researchpublish.com</strong> <strong>Published Date: 25-September-2023</strong> <strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.8375689</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.researchpublish.com/papers/the-customer-satisfaction-of-store-pasarmandalikacom-regarding-the-attributes-of-vegetable-and-fruit-products-and-strategies-for-increasing-sustainable-sales-turnover</strong>
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Zhou, Dan. "Tourism destination marketing strategy driven by big data." Journal of Computational Methods in Sciences and Engineering 24, no. 4-5 (2024): 2593–609. http://dx.doi.org/10.3233/jcm-247486.
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In this study, the marketing strategy of tourism destination driven by big data is deeply discussed. Firstly, the application of big data in the tourism industry and the current strategy of tourism destination marketing are analyzed, and then the research methods are designed through factor analysis and big data analysis theory. After processing and factor analysis of the collected data, the influence of big data on tourism destination marketing strategy is analyzed, and the possible optimization path is explored. At the same time, the problems and challenges encountered in this process are also found and analyzed. Based on these analysis results, this study provides some theoretical and practical implications to promote the application of big data in tourism destination marketing strategies. Finally, it emphasizes the importance of big data and factor analysis in the formulation of future tourism destination marketing strategy, and puts forward the direction of future research.
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Vaughan, Richard J. "Examining the Data Analytics Skill Gap in Mid-Level Marketing Professionals, Driven by the Continuing Exponential Growth of Big Data." Journal of Business Theory and Practice 5, no. 3 (2017): 267. http://dx.doi.org/10.22158/jbtp.v5n3p267.
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<p><em>The purpose of this paper is to examine the impact of big data in the skill requirements of marketing professionals. Over 12,000 marketing job listings in the top six major marketing cities were researched to determine how many positions required big data analytics skills. 38% of big data’s biggest impact is in marketing, advanced analytics are used in customer-facing marketing, sales and customer service departments. This research found 39% of all marketing positions in the defined search criteria listed “big data” as a required skill in the combined six cities. It is interesting to note the relatively new term “big data” was cited 39% and surpassed the term “Data”, which was cited 35% across all 12,796 positions.</em><em></em></p><p><br /><em></em></p>
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Li, Qingrui. "Research on Optimization of Marketing Strategy Based on Big Data Analysis." Academic Journal of Management and Social Sciences 10, no. 1 (2025): 138–44. https://doi.org/10.54097/v8ef8s24.
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This research focuses on optimizing marketing strategies through big data analysis. By investigating the specific applications, challenges, and opportunities of leveraging big data in marketing, the study aims to develop a comprehensive framework for integrating data-driven insights into marketing decision-making processes. It examines the impact of big data-driven strategies on key performance indicators (KPIs), including customer acquisition, engagement, and conversion rates. The findings contribute to the development of best practices and frameworks for integrating big data into marketing decisions, providing empirical evidence on the effectiveness of data-driven marketing strategies. This research serves as a practical guide for marketers on effectively leveraging big data analysis to optimize marketing strategies, identifying key success factors and challenges associated with data-driven marketing.
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Terui, Nobuhiko. "Marketing Analytics: Statistical Modeling of Big Data and Small Data in Marketing." Japanese Journal of Applied Statistics 44, no. 1-2 (2015): 3–15. http://dx.doi.org/10.5023/jappstat.44.3.
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Lutsiі, Oleksandr, and Oleksandr Helevei. "Formation of Components of the Marketing Information System for Agricultural Products Using Big Data Methods." Oblik i finansi, no. 3(101) (2023): 145–51. http://dx.doi.org/10.33146/2307-9878-2023-3(101)-145-151.
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Big data is a source of innovation. Big data helps companies in various fields move towards digital transformation. The purpose of the article is to reveal the possibilities of using big data methods in marketing agricultural products to increase production efficiency, support the agricultural economy, and develop information marketing systems. The research identifies the possibilities of creating intelligent marketing systems based on big data; evaluates the technological challenges associated with collecting, storing and analysing large volumes of data in real-time; studies the localisation of data and the possibilities of their use to support the agricultural sector in the regions. Intelligent marketing creates both great opportunities and challenges for data analysis. Significant challenges include extensive samples and high dimensionality of the data, missing data and noise, and low data reliability. In addition, with the complex internal structure of the information system, the analysis of collected data becomes more time-consuming, requiring innovative data processing methods. At the same time, thanks to the intelligent marketing data processing centre, it is possible to control the regional circulation of agricultural products, their quality and safety, and prices, and react to sudden changes in the market in real-time. The article presents the idea of a big data centre for intelligent marketing of agricultural products. The components of the block of service provision using big data methods were described. The study results indicate that for the further development of the marketing of agricultural products using big data methods, it is crucial to develop the components of the marketing information system, ensure the accuracy and timeliness of marketing information, integrate data from various sources, and also support constant analysis and correction of strategies based on the collected data.
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Yakhshiboeva, Laylo, and Oybek Eshbayev. "The Synergistic Impact of Artificial Intelligence and Big Data Analytics on Marketing Communication Strategies in Entrepreneurial Ecosystems: A Data-Driven Approach." Financial Technology and Innovation 3, no. 2 (2023): 16–22. http://dx.doi.org/10.54216/fintech-i.030202.
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In today's competitive entrepreneurial landscape, effective marketing communication strategies play a pivotal role in success. Entrepreneurs are increasingly adopting cutting-edge technologies like artificial intelligence (AI) and big data analytics to optimize their marketing efforts. This research explores the synergistic impact of AI and big data analytics on marketing communication strategies within entrepreneurial ecosystems, presenting a data-driven approach. The study assesses the current marketing communication landscape in entrepreneurial ventures, identifying challenges faced by entrepreneurs in connecting with their audiences. By reviewing the latest trends in AI and big data applications, we investigate their integration into marketing communication strategies. Through case studies and empirical data analysis, the research uncovers the successful adoption of AI and big data analytics in entrepreneurial marketing communication. These technologies enable personalized and targeted campaigns by identifying customer preferences and behaviors. Big data analytics helps refine marketing strategies by extracting valuable insights from vast datasets. The research also addresses challenges and ethical considerations related to data privacy and bias. Additionally, it explores the necessary infrastructure and human capital for effective implementation. The findings highlight AI and big data's critical role in driving innovation and growth in marketing communication within entrepreneurial ecosystems. Adopting a data-driven approach empowers entrepreneurs to enhance marketing effectiveness and gain a competitive edge. In conclusion, this research showcases AI and big data analytics as transformative tools for shaping marketing communication in entrepreneurial ventures. Leveraging these technologies strategically can unlock novel opportunities and ensure long-term business success in the dynamic marketplace.
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Raximkulova, Madina Istamovna, and Dildora Erkin qizi Norbutayeva. "MARKETING SOHASI UCHUN BIG DATA TAHLIL QILISH USULLARI." Journal of International science networks 1, no. 4 (2025): 6–9. https://doi.org/10.5281/zenodo.14903925.
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<em>Maqolada marketing sohasida Big Data (Katta Ma’lumotlar) dan foydalanishning ahamiyati va usullari yoritilgan. Big Data tahlilining marketing strategiyalarini shakllantirish, mijozlar xatti-harakatlarini tushunish va marketing kampaniyalarini optimallashtirishdagi roli ko‘rsatilgan. Shuningdek, Big Data tahlili uchun qo‘llaniladigan asosiy usullar, vositalar va texnologiyalar haqida batafsil ma’lumot berilgan. Mijozlarga yo‘naltirilgan yondashuv, shaxsiylashtirilgan marketing va real vaqtda ma’lumotlarni qayta ishlash kabi tushunchalar o‘rganilgan.</em>
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Mao, Ganfeng, and Liwen Dong. "Research on Marketing Strategy based on Social Media Big Data." Science, Technology and Social Development Proceedings Series 1 (October 2, 2024): 269–75. http://dx.doi.org/10.70088/zwwac169.
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With the rapid advancement of internet technologies, social media platforms have emerged as pivotal channels for information exchange and dissemination. The vast amounts of user-generated data provide businesses with unprecedented insights. The analysis of big data from social media allows for more precise and effective marketing strategies. This paper aims to explore the characteristics of big data from social media and analyze its applications in marketing strategies. By delving into data types, volumes, structures, and user behavior, it reveals the main challenges businesses face in big data applications and proposes corresponding solutions. Precision marketing, brand management, market trend forecasting, and crisis management are significant areas of application for big data in marketing. This paper, through a detailed examination of these strategies, highlights the potential and challenges of big data in contemporary marketing. Ultimately, it seeks to offer theoretical support and practical guidance for businesses on how to develop and implement effective marketing strategies in a big data-driven market environment.
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Ghasemaghaei, Maryam, and Goran Calic. "Assessing the impact of big data on firm innovation performance: Big data is not always better data." Journal of Business Research 108 (January 2020): 147–62. http://dx.doi.org/10.1016/j.jbusres.2019.09.062.
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Xu, Ying. "Research on Marketing Management Enabled by Big Data Technology." Proceedings of Business and Economic Studies 8, no. 2 (2025): 287–92. https://doi.org/10.26689/pbes.v8i2.10336.
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In the era of big data, data has gradually become an important asset of enterprises, and the application of big data technology has gradually become the key to the optimization of enterprise marketing management mode. Enterprises take the initiative to meet the development trend of the times, rely on big data technology to effectively process and analyze data, innovate decision-making methods and operation models, and achieve efficient marketing and fine management, which is an important way to improve their market competitiveness. Therefore, the author first analyzes the empowering role of big data technology on enterprise marketing management, and then discusses the difficulties faced by enterprise marketing management in the era of big data, and finally puts forward targeted improvement strategies, aiming to provide a reference for enterprises to innovate and change the marketing management mode.
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Qingqing, Yang. "Research on the Application of Big Data in Marketing Management." Journal of Business and Marketing 1, no. 4 (2024): 87–89. https://doi.org/10.62517/jbm.202409411.
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In the context of the new economy, there are increasingly more development opportunities for enterprises, while the industry competition and market challenges they face are gradually intensifying. In order to achieve high-quality sustainable development, enterprises should fully recognize that marketing management is aimed at profitability. In order to pursue sustainable development and competitiveness, optimizing the allocation of various resources in the enterprise is an effective means. Therefore, continuously improving the level of marketing management is crucial. Big data technology is a representative product of the digital age, and its application in enterprise marketing management can help companies adjust their marketing strategies in a timely and effective manner, achieve precision marketing, and improve marketing effectiveness. This article first analyzes the advantages of applying big data in marketing management, and then proposes the direction of big data application in enterprise marketing management, aiming to promote the dual improvement of enterprise brand influence and economic benefits through big data marketing.
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Tang Jiangting. "Research on the Precision Marketing and Optimization of Pinduoduo in the Context of Big Data." Pacific International Journal 5, no. 4 (2022): 51–55. http://dx.doi.org/10.55014/pij.v5i4.240.
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Precision marketing is widely used in mobile applications at present, and big data provides a technical foundation for precision marketing. Precision marketing refers to the use of big data technology to push marketing content to target users through various new media to maximize marketing effects. The implementation of precision marketing through big data has the characteristics of accuracy, large amount of data and target customer-oriented. As an emerging Chinese e-commerce enterprise, Pinduoduo positioned itself as the pioneer of new e-commerce. It is the third-party social e-commerce application focusing on Customer-to-Manufacturer group shopping, that is, with the original social group shopping as the core mode. On the basis of previous research, this paper analyzes the advantages of precision marketing for Pinduoduo under the background of big data, including reducing marketing costs, improving marketing efficiency, reducing the cost of meeting target customers and flexibly adjusting marketing strategies. Pinduoduo still faces some problems despite its rapid development, including the fatigue of target customers due to a large amount of information push, lack of professionals proficient in big data analysis, the possibility of data security risks and the imperfect legal and regulatory system related to big data and precision marketing. Finally, this paper puts forward some suggestions to optimize the precision marketing of Pinduoduo under the background of big data from four aspects: respecting the choice of target customers, introducing professionals related to big data analysis and mining, training on data security knowledge, and further improving laws and regulations related to big data and precision marketing.
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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 massive quantities of information that organizations have access to. Marketing teams gather data through applications or a multiplicity of websites and quality attribution modeling can trail each brand interaction along with the customer journey. When this information is inspected marketing teams can see which creative assets drove more engagements, which channels provide the highest ROI, and much more. Based on these findings organisations can know their campaigns to ensure the best customer experiences and greatest written-on marketing investment. In this conceptual paper, we discuss data-driven marketing and its use in different segments of business, and how data analytics has become the secret ingredient for success in decision-making in marketing.
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Luther, Kington Nwobodo. "The Impacts of Big Data Analytics and Artificial Intelligence on Marketing Strategies." Global Journal of Economic and Finance Research 02, no. 01 (2025): 33–44. https://doi.org/10.5281/zenodo.14650131.
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<strong>ABSTRACT:</strong> The marketing sector has seen a significant transformation, particularly due to the emergence of data-driven decision-making and the dominance of digital platforms. This transition signifies a deviation from traditional marketing strategies, which formerly depended on more direct contact methods and conventional market research techniques. As digital technologies have grown, they have changed how we can track and change customers' buying habits and given us new ways to connect with them. Digital platforms and enhanced data provide marketers new consumer insights, making marketing more challenging. A detailed literature review and practical assessment analyse the real and prospective benefits of big data analytics and artificial intelligence on marketing decision-making. According to the paper, AI and big data analytics may assist companies understand customer and industry developments. They might then modify their marketing for each user. Big data analytics and AI improve target market positioning, simplify marketing, and educate consumers, affecting marketing strategy. The research suggests creating a data analysis team, streamlining data gathering and combination, using adaptable analytical tools, and customising marketing efforts. Some issues remain with this research. Data reliability and group size matter. The steps are unclear. For more solid and unambiguous results, future study should examine the impact of marketing using AI and big data analytics, maybe on a particular sector. This research may assist firms greatly enhance their marketing.
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Ivanov, Sergey, and Mykola Ivanov. "Marketing forecasting based on Big Data information." SHS Web of Conferences 107 (2021): 05002. http://dx.doi.org/10.1051/shsconf/202110705002.
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In the paper discusses the use of big data as a tool to increase data transfer speed while providing access to multidimensional data in the process of forecasting product sales in the market. In this paper discusses modern big data tools that use the MapReduce model. The big data presented in this article is a single, centralized source of information across your entire domain. In the paper also proposes the structure of a marketing analytics system that includes many databases in which transactions are processed in real time. For marketing forecasting of multidimensional data in Matlab, a neural network is considered and built. For training and building a network, it is proposed to construct a matrix of input data for presentation in a neural network and a matrix of target data that determine the output statistical information. Input and output data in the neural network is presented in the form of a 5x10 matrix, which represents static information about 10 products for five days of the week. The application of the Levenberg-Marquardt algorithm for training a neural network is considered. The results of the neural network training process in Matlab are also presented. The obtained forecasting results are given, which allows us to conclude about the advantages of a neural network in multivariate forecasting in real time.
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Jang, Jin Hee. "Suggestion for Jewelry Marketing Utilizing Big Data." KOREA SCIENCE & ART FORUM 19 (March 31, 2015): 593. http://dx.doi.org/10.17548/ksaf.2015.03.19.593.
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Voss, Angi, and Karl-Heinz Sylla. "Innovationspotenzialanalyse Big Data — Ergebnisse für das Marketing." Marketing Review St. Gallen 31, no. 1 (2014): 36–45. http://dx.doi.org/10.1365/s11621-014-0319-1.
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Agarwal, Sugandha, Norita Ahmad, and Dima Jamali. "AI and Big Data in Contemporary Marketing." Computer 57, no. 4 (2024): 137–42. http://dx.doi.org/10.1109/mc.2024.3360588.
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Mukhopadhyay, Shameek, Rohit Kumar Singh, and Tinu Jain. "Developing big data enabled Marketing 4.0 framework." International Journal of Information Management Data Insights 4, no. 1 (2024): 100214. http://dx.doi.org/10.1016/j.jjimei.2024.100214.
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Al-Ababneh, Hassan Ali. "Utilizing Big Data in digital marketing strategies for the energy sector." E3S Web of Conferences 541 (2024): 02005. http://dx.doi.org/10.1051/e3sconf/202454102005.
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The main goal of the study is the constructive formalization of the main aspects of big data in digital marketing of companies in the energy sector. With the help of constructive analysis, criticism and scientific generalization, the essence of the concept of “big data” is argued, the key characteristics, types and tasks within the framework of business implementation in digital marketing of companies in the energy sector are structured. The main functions and tasks of big data in the context of companies’ digital marketing strategy are highlighted. It has been proven that in modern business conditions, big data must be consumer-oriented and are an integral part of the digital marketing strategy of companies in the energy sector. Big data is conceptualized as a key innovative tool for online brand advertising in the context of digital marketing. The findings provide a theoretical framework and framework for the study of big data as part of its implementation in the digital marketing strategy of companies in the energy sector. The developed recommendations can be applied in practice for the business implementation of Big Data digital marketing strategies for companies in the energy sector in order to ensure the achievement of business goals.
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Ding, Ruixuan. "Research and Analysis of Precision Marketing Strategy in the Era of Big Data." Advances in Economics, Management and Political Sciences 170, no. 1 (2025): 123–28. https://doi.org/10.54254/2754-1169/2025.lh23989.
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In the era of big data, precision marketing has become a research focus in today's academic and business circles. With the increasingly fierce market competition and the increasing demand of consumers for personalized products and services, the traditional marketing model has been unable to meet the needs of modern market. In the realm of contemporary business, precision marketing strategy has increasingly emerged as a vital tool for enterprises to bolster their competitive edge, by using the keywords "big data" and "precision marketing" in EBSCO, CNKI and other databases, the target research literature related to the subject was selected. Through a comprehensive review and analysis of these documents, this paper delves deeply into the core technologies of big data application in precision marketing, and elaborates on how it significantly enhances marketing efficiency and effectiveness by precisely targeting customers, customizing marketing content, and optimizing marketing channels. The study found that with the support of big data, precision marketing can realize the efficient allocation of marketing resources and the precise push of personalized services. However, big data precision marketing also faces many challenges in the development process, such as data security, privacy protection and algorithm bias. To encourage the long-term growth of big data precision marketing, future research should focus more on the cross-industry application of technology and the feasibility of long-term implementation of marketing strategies, and further improve technical ethics.
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Na, Im Ha, Yoo In Jae, and Park In Hwa. "Personalized Digital Marketing Strategies: A Data-Driven Approach Using Marketing Analytics." Journal of Management and Informatics 4, no. 1 (2025): 668–86. https://doi.org/10.51903/jmi.v4i1.149.
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The rapid development of digital technology has transformed marketing strategies, enabling companies to leverage big data analytics to enhance personalized marketing approaches. With the increasing volume of customer interaction data collected from various digital platforms, businesses can now gain deeper insights into consumer preferences and behaviors. This study aims to analyze the impact of big data analytics on personalized digital marketing and evaluate the role of data visualization in improving decision-making processes. The research employs an exploratory approach by analyzing secondary data from multiple digital sources, including e-commerce platforms, social media, and company websites. The study applies data-driven segmentation models and machine learning-based predictive analytics to assess customer engagement and conversion rates. The findings reveal that implementing big data analytics leads to a 48.57% increase in customer engagement and a 132% improvement in conversion rates compared to traditional marketing methods. Furthermore, the integration of data visualization techniques enables marketers to interpret complex consumer patterns effectively, contributing to a 46.67% rise in average transaction value per customer. These results indicate that data-driven personalization significantly enhances marketing effectiveness and customer loyalty. This research contributes to the field by providing empirical evidence on the advantages of utilizing big data analytics in digital marketing and highlighting the importance of interactive dashboards for real-time customer trend analysis. Future research is encouraged to explore the automation of personalized marketing through machine learning algorithms and the optimization of real-time data-driven strategies.
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Qingqing, Yang. "Analysis of New Marketing Strategies in the Era of Big Data." Journal of Statistics and Economics 1, no. 4 (2024): 53–55. https://doi.org/10.62517/jse.202411408.
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In the new era, the development speed of big data technology is gradually accelerating, bringing certain opportunities for marketing activities. Developing and mining big data technology, building a digital and information-based marketing system, and innovating and optimizing marketing plans and strategies can further improve marketing efficiency and lay a solid foundation for the sustained and efficient development of the industry. Based on this, this article conducts in-depth analysis and research on new marketing strategies in the era of big data.
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Eshbayev, Oybek, and Laylo Yakhshiboeva. "Harnessing Big Data Processing in Computer Networks for Digital Marketing Entrepreneurship." Financial Technology and Innovation 3, no. 2 (2023): 30–36. http://dx.doi.org/10.54216/fintech-i.030204.
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This research investigates the integration of big data processing in computer networks for digital marketing entrepreneurship to optimize marketing strategies and drive business growth. By analyzing current digital marketing practices, we identify key challenges faced by entrepreneurs. Through examining successful case studies, we showcase the effectiveness of big data processing in marketing campaigns. A practical framework is developed to guide startups and small businesses in integrating big data processing into their marketing strategies, considering factors like customer behavior analysis, segmentation, and personalized marketing. Additionally, we explore scalability and cost-effectiveness concerns, particularly relevant for entrepreneurs with limited resources. Ethical implications of data collection, processing, and utilization are thoroughly examined, and strategies to address challenges and limitations are proposed. Through comparative analysis, we assess the performance of big data-driven marketing campaigns in comparison to traditional approaches, revealing improved outcomes and return on investment. This research provides entrepreneurs with valuable insights and recommendations, empowering them to make data-driven decisions and succeed in the dynamic world of digital marketing. Moreover, it contributes to the discourse on big data in entrepreneurship, promoting responsible and innovative practices in the digital marketing landscape.
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Krasniqi, Arjan, Berat Maksutaj, and Erblin Januzaj. "Using big data for optimizing advertising campaigns in social networks." South Florida Journal of Development 5, no. 3 (2024): e3727. http://dx.doi.org/10.46932/sfjdv5n3-015.
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In the digital era, advertising on social networks has become a crucial element of marketing strategies. With the increasing number of users on platforms such as Facebook, Twitter, Instagram, and others, companies have unprecedented opportunities to target and engage consumers. This paper aims to examine the role and importance of Big Data usage in optimizing marketing campaigns on social networks. In an era where data is limitless, Big Data has become a valuable resource for businesses looking to understand consumer behavior and improve the efficiency of advertising campaigns. In this paper, we will shed light on the history and development of Big Data, including their volumes and sources. We will explore the potential of Big Data and categorize their sources to better understand how they can be leveraged for social media marketing. Furthermore, we will analyze the benefits and challenges of using Big Data in marketing. Additionally, we will examine cases where the use of Big Data has failed to optimize advertising campaigns on social networks and focus on the influence of Big Data in digital marketing, including personalization and sales promotion. We will also explore the technologies and methodologies used for making marketing decisions using Big Data. This study concludes that Big Data offers exceptional potential for innovation in the field of advertising on social networks, helping companies cope with rapid changes in consumer preferences and market dynamics. The results indicate that the strategic use of Big Data can lead to a deeper understanding of consumer behavior and offer a competitive advantage in a crowded and fast-paced market.
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Liu, Qing, Hao Wan, and Hongfang Yu. "Application and Influence of Big data Analysis in Marketing Strategy." Frontiers in Business, Economics and Management 9, no. 3 (2023): 168–71. http://dx.doi.org/10.54097/fbem.v9i3.9580.
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This paper aims to study the application and impact of Big data analysis in marketing strategies. Through comprehensive literature review and empirical research, this paper discusses the potential value and actual effect of Big data analysis on marketing decisions. The study found that Big data analysis can provide accurate and comprehensive market insight, help enterprises better understand consumer needs and Market trend, and thus develop personalized and accurate marketing strategies. The impact of Big data analysis on marketing strategy is mainly reflected in the improvement of consumer insight, the accuracy of target market positioning and the implementation of personalized marketing. In addition, this study proposes practical significance and suggestions, including strengthening data collection and integration, establishing a data analysis team, flexibly utilizing various analysis techniques, and implementing personalized marketing strategies. However, this study also has some limitations, such as sample limitations and data reliability issues. Future research can further explore the application effect of Big data analysis in different industries and market environments, and further study its impact on different types of enterprises. This study has important practical significance for enterprises to develop more effective marketing strategies.
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D'Arco, Mario, Letizia Lo Presti, Vittoria Marino, and Riccardo Resciniti. "Embracing AI and Big Data in customer journey mapping: from literature review to a theoretical framework." Innovative Marketing 15, no. 4 (2019): 102–15. http://dx.doi.org/10.21511/im.15(4).2019.09.
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Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.
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Paas, Leo. "Marketing research education in the Big Data era." International Journal of Market Research 61, no. 3 (2019): 233–35. http://dx.doi.org/10.1177/1470785319825535.
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The excellent and timely Nunan and Di Domenico International Journal of Market Research (IJMR) paper addressed the stagnant state of many marketing research courses. Following up on this contribution, I will propose some specific changes for enhancing relevance of marketing research curricula, emphasizing transactional data and Big Data. Moreover, I will propose that marketing research education should develop analysts who can convert data into advice for decision makers and we should educate marketing managers to become more quantitatively oriented to ensure they can base their decisions on data and analytical results.
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Vasilopoulou, Christina, Leonidas Theodorakopoulos, and Giorgos Igoumenakis. "The Promise and Peril of Big Data in Driving Consumer Engagement." Technium Social Sciences Journal 45 (July 9, 2023): 489–99. http://dx.doi.org/10.47577/tssj.v45i1.9133.
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The advent of big data has transformed the way in which businesses interact with consumers. With the ability to collect and analyze vast amounts of data from multiple sources, companies are now able to gain insights into consumer behavior that were previously impossible. This paper explores the relationship between big data, consumer behavior, digital marketing and personalizing the customer experience. It examines the ways in which big data is being used to gain insights into consumer behavior, the benefits and limitations of using big data, ethical considerations and the role of digital marketing in leveraging big data to create more effective marketing strategies.
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Rhoda, Adura Adeleye, Feranmi Awonuga Kehinde, Franca Asuzu Onyeka, Leonard Ndubuisi Ndubuisi, and Sunday Tubokirifuruar Tula. "Digital marketing analytics: A review of strategies in the age of big data and AI." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 073–84. https://doi.org/10.5281/zenodo.13993764.
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Digital Marketing Analytics has become increasingly crucial in the contemporary business landscape, especially with the advent of Big Data and Artificial Intelligence (AI). This paper provides a comprehensive review of the strategies employed in Digital Marketing Analytics within the context of the rapidly evolving landscape of Big Data and AI. In the age of Big Data, businesses are inundated with vast amounts of information, making it imperative for marketers to leverage analytics tools effectively. This review explores the role of Digital Marketing Analytics in harnessing the power of Big Data, enabling marketers to extract actionable insights, identify trends, and make informed decisions. The integration of AI further enhances these capabilities, automating processes and offering predictive analytics for more targeted and personalized marketing strategies. The paper delves into various strategies employed in Digital Marketing Analytics, encompassing data collection, analysis, and interpretation. It discusses the significance of real-time analytics in responding promptly to market changes, optimizing campaigns, and enhancing customer experiences. Additionally, the review addresses the ethical considerations surrounding data privacy and the responsible use of AI in marketing practices. The synergy between Big Data and AI is explored as a catalyst for innovation in digital marketing. Strategies such as machine learning algorithms for customer segmentation, sentiment analysis, and predictive modeling are examined for their potential to revolutionize marketing effectiveness. Moreover, the paper highlights the evolving role of analytics in measuring the return on investment (ROI) of digital marketing initiatives. This review provides insights into the evolving landscape of Digital Marketing Analytics, emphasizing the strategic importance of leveraging Big Data and AI. Businesses that embrace these technologies stand to gain a competitive edge by unlocking valuable insights, optimizing marketing efforts, and staying agile in response to dynamic market conditions.
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Deng, Yifeng. "Business marketing analysis of agricultural products on online platforms based on big data." SHS Web of Conferences 163 (2023): 02002. http://dx.doi.org/10.1051/shsconf/202316302002.
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In recent years, online platform shopping has gradually become a mainstream shopping method for the public. And with the continuous development of China’s poverty alleviation cause in rural areas, marketing of agricultural products has al-so been added to online marketing, but in the process of development, various unsuitable situations have emerged, and data cannot be combined with products, resulting in the further development of agricultural products in this marketing mode has been restricted. Nowadays has entered the big data era and the market-ing of big data has appeared in many industries. This paper, through a systematic review of domestic and foreign literature, uncovers a basic framework of big data for agricultural products in online platform marketing, explaining the basic defini-tion and characteristics of big data, and elaborating the application. Although there is logistic regression model to truly realize big data in agricultural products online platform. The application of big data in agricultural products online plat-form marketing is explained.