A data science-based marketing decision support system for brand management
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Author(s)Galyna ChornousLink to ORCID Index: https://orcid.org/0000-0003-4889-1247
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Yana FareniukLink to ORCID Index: https://orcid.org/0000-0001-6837-5042
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Vincentas Rolandas GiedraitisLink to ORCID Index: https://orcid.org/0000-0002-0293-0645
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Erstida UlvidienėLink to ORCID Index: https://orcid.org/0000-0003-2172-465X
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Ganna KharlamovaLink to ORCID Index: https://orcid.org/0000-0003-3614-712X
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DOIhttp://dx.doi.org/10.21511/im.19(2).2023.04
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Article InfoVolume 19 2023, Issue #2, pp. 38-50
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- 780 Views
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298 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
To improve the marketing activity and brand management and justify the most effective marketing decisions, organizations should implement different information technologies, mathematical methods and models into the marketing decision support system (MDSS). The goal of this paper is to form an architecture of an MDSS, the model base of which is developed on Data Science tools, in particular regression analysis and machine learning methods. The proposed MDSS is a multi-agent information system comprising nine intellectual agents (market environment monitoring, data processing, marketing mix modeling, price policy support, portfolio management, strategic analysis, forecasting, customer segmentation, and customer classification). The functionality of these agents is realized through Data Science, which allows for the optimization of marketing activities (e.g., an effective brand management strategy and its elements (portfolio strategy, price policy, and media strategy) or solving the problems of attracting new and retaining current customers with the maximal return on marketing investments). The MDSS analyzes the marketing environment, media activity, and business indicators by constructing different models and forecasting various combinations of marketing factors to select the best one. The joint work of MDSS agents provides decision-makers with interactive reports. The research findings offer a scientific basis for making effective marketing decisions based on data, and the proposed MDSS can become part of an intelligent system for planning marketing activities.
- Keywords
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JEL Classification (Paper profile tab)M30, C10, С61, D81
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References39
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Tables1
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Figures3
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- Figure 1. Architecture of the proposed multi-agent MDSS for brand management
- Figure 2. Example of the optimal level of the price index considering the growth of market share in money
- Figure 3. Approach to the formation of an effective portfolio marketing strategy
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- Table 1. General functional structure of the multi-agent MDSS “Brand’s marketing management system”
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- Ai, W.-G., Sun, J., & Li, H. (2004). A distributed marketing decision support system based on multi-intelligent-agent. Proceedings of 2004 International Conference on Machine Learning and Cybernetics (pp. 233-238). Shanghai, China.
- Alexouda, G. (2005). A user-friendly marketing decision support system for the product line design using evolutionary algorithms. Decision Support Systems, 38(4), 495-509.
- Birn, R., & Stone, M. (2021). Key issues in managing marketing research and customer insight. In L. Wright, L. Moutinho, M. Stone, & R. Bagozzi (Eds.), The Routledge companion to marketing research (1st ed.) (pp. 219-243). London: Routledge.
- Bulling, N. A. (2014). Survey of multi-agent decision making. KI – Künstliche Intelligenz, 28, 147-158.
- Chornous, G. (2016). Ahentna model intelektualnoi informatsiinoi systemy upravlinnia v ekonomitsi [Agent-based model of intelligent information control systems in economics]. Bulletin of Taras Shevchenko National University of Kyiv. Economics, 1(178), 41-47. (In Ukrainian).
- Chornous, G., & Fareniuk, Y. (2022). Optimization of portfolio marketing strategy based on data science technologies. CEUR workshop proceedings (pp. 89-100).
- Chornous, G., & Iarmolenko, I. (2017). Decision support system for predicting stock prices based on sentiments in social media. Proceedings of the second international conference on Internet of things, data and cloud computing (pp. 1-4). Cambridge, United Kingdom.
- Demoulin, N. T. M. (2007). Marketing decision support system openness: A means of improving managers’ understanding of marketing phenomena. Decision Support Systems, 44(1), 79-92.
- Dostatni, E., Diakun, J., Grajewski, D., Wichniarek, R., & Karwasz, A. (2015). Multi-agent system to support decision-making process in ecodesign. In Á. Herrero, J. Sedano, B. Baruque, H. Quintián, & E. Corchado (Eds.), 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, 368 (pp. 463-474). Cham: Springer.
- Farm, A. (2020). Pricing in practice in consumer markets. Journal of Post Keynesian Economics, 43(1), 61-75.
- Fedirko, O., Zatonatska, T., Wolowiec, T., & Skowron, S. (2021). Data science and marketing in e-commerce amid COVID-19 pandemic. European Research Studies Journal, XXIV (S2), 3-16.
- Figueroa-Pérez, J. F., Leyva-López, J. C., Pérez-Contreras, É. O., & Sánchez-Sánchez, P. (2021). A multi-agent MDSS for supporting new product design decisions. In E. León-Castro, F. Blanco-Mesa, A. M. Gil-Lafuente, J. M. Merigó, & J. Kacprzyk (Eds.), Intelligent and Complex Systems in Economics and Business. Advances in Intelligent Systems and Computing, 1249 (pp. 41-51). Cham: Springer.
- Grygoruk, P. M. (2011). Informatsiina tekhnolohiia yak instrument pidtrymky pryiniattia marketynhovoho rishennia [Information technology as a tool to support marketing decision making]. Pratsi Odeskoho politekhnichnoho universytetu – Proceedings of the Odessa Polytechnic University, 3, 170-176. (In Ukrainian).
- Günesen, S. N., Şen, N., Yıldırım, N., & Kaya, T. (2021). Customer Churn Prediction in FMCG Sector Using Machine Learning Applications. In E. Mercier-Laurent, M.Ö. Kayalica, & M. L. Owoc (Eds.), IFIP advances in information and communication technology, 614 (pp. 82-103). Cham: Springer.
- Habib, F., & Ahmad, M. J. (2019). Factors affecting consumer buying decision: A quantitative perspective. International Journal of Scientific and Technology Research, 8(10), 3022-3025.
- Heliste, A. (2019). Adapting marketing mix modelling for the retail marketing environment – A road map for development (Master’s Thesis). Aalto University.
- Hurtado, P., Dorneles, C., & Frazzon, E. (2019). Big data application for e-commerce’s logistics: A research assessment and conceptual model. IFAC-Papers OnLine, 52(13), 838-843.
- Huseynov, F., & Özkan Yıldırım, S. (2019). Online consumer typologies and their shopping behaviors in B2C e-commerce platforms. SAGE Open, 9(2).
- Ibrahim, E. B., & Harrison, T. (2020). The impact of internal, external, and competitor factors on marketing strategy performance. Journal of Strategic Marketing, 28(7), 639-658.
- Islamgaleyev, A., Petrova, M., Kurenkeyeva, G., Shalbayeva, S., & Kadirbergenova, A. (2020). Increasing customer focus in metal trading. Entrepreneurship and Sustainability Issues, 8(1), 604-617.
- Johnson, D. S., Sihi, D., & Muzellec, L. (2021). Implementing big data analytics in marketing departments: Mixing organic and administered approaches to increase data-driven decision making. Informatics, 8(4), 66.
- Juan, Y.-K., Gao, P., & Wang, J. (2010). A hybrid decision support system for sustainable office building renovation and energy performance improvement. Energy and Buildings, 42(3), 290-297.
- Kayabay, K., Gökalp, M., Gökalp, E., Erhan Eren, P., & Koçyiğit, A. (2022). Data science roadmapping: An architectural framework for facilitating transformation towards a data-driven organization. Technological Forecasting and Social Change, 174, 121264.
- Kingsman, B. G., & de Souza, A. A. (1997). A knowledge-based decision support system for cost estimation and pricing decisions in versatile manufacturing companies. International Journal of Production Economics, 53(2), 119-139.
- Lamb, C. W., Hair, J. F., & McDaniel, C. (2012). Marketing (912 p.). Cengage Learning.
- Li, D.-C., Lin, Y.-S., & Huang, Y.-C. (2009). Constructing marketing decision support systems using data diffusion technology: A case study of gas station diversification. Expert Systems with Applications, 36(2(P1)), 2525-2533.
- Little, J. D. (2004). Models and managers: The concept of a decision calculus. Management Science, 50(12), 1841-1853.
- Mo, L., & Yang, L. (2022). Research on application effective evaluation of artificial intelligence technology in marketing communication. Security and Communication Networks, 2022, 3507353.
- Nadj, M., Maedche, A., & Schieder, C. (2020). The effect of interactive analytical dashboard features on situation awareness and task performance. Decision Support Systems, 135, 113322.
- Osaysa, E. K. (2022). Improving the quality of marketing analytics systems. International Journal for Quality Research, 16(1), 119-132.
- Saura, J. R. (2021). Using data sciences in digital marketing: Framework, methods, and performance metrics. Journal of Innovation & Knowledge, 6(2), 92-102.
- Schuh, G., Gützlaff, A., Cremer, S., & Schopen, M. (2020). Understanding process mining for data-driven optimization of order processing. Procedia Manufacturing, 45, 417-422.
- Stone, M., Aravopoulou, E., Woodcock, N., Laughlin, P., & Stott, R. (2021). Interactive marketing, customer information and marketing research. In L. Wright, L. Moutinho, M. Stone, & R. Bagozzi (Eds.), The Routledge companion to marketing research (1st ed.) (pp. 38-54). London: Routledge.
- Vahidov, R., & Fazlollahi, R. (2003). A multi-agent DSS for supporting e-commerce decisions. Journal of Computer Information Systems, 44, 87-94.
- van Bruggen, G. H., Smidts, A., & Wierenga, B. (1996). The impact of the quality of a marketing decision support system: An experimental study. International Journal of Research in Marketing, 13(4), 331-343.
- Wierenga, B., & Oude Ophuis, P.A.M. (1997). Marketing decision support systems: Adoption, use, and satisfaction. International Journal of Research in Marketing, 14(3), 275-290.
- Wöber, K.W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255.
- Zhang, D., Pee, L., & Cui, L. (2021). Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse. International Journal of Information Management, 57, 102304.
- Zhecheva, D., & Nenkov, N. (2022). Business demands for processing unstructured textual data – Text mining techniques for companies to implement. Access to Science, Business, Innovation in Digital Economy, 3(2), 107-120.
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Business dynamism in the world economy
Zuzana Virglerova ,
Felice Addeo ,
Eliska Zapletalikova
doi: http://dx.doi.org/10.21511/ppm.18(3).2020.14
Problems and Perspectives in Management Volume 18, 2020 Issue #3 pp. 160-169 Views: 2788 Downloads: 313 TO CITE АНОТАЦІЯThe creation and growth of new enterprises and, on the other hand, their decline and market exit are crucial factors of business dynamism and economic growth. Thus, business dynamism is an important aspect in the market chain and productivity of an economy, as well as a trigger for market reforms. The aim is to analyze business dynamism using Global Competitiveness Index 4.0 and its variables worldwide and to verify the relationship between business dynamism in the EU states and economic characteristics such as Valued added at factor cost, Enterprise Birth Rate and Enterprise Death Rate. Data were collected from the 2019 Global Competitiveness Report and from the EUROSTAT database, using the most recently updated source for each indicator. The 11th pillar of the Global Competitiveness Index, focused on business dynamics, and a set of indicators were analyzed using PCA to verify if all the variables are effective representatives of the concept. It was found out that the pillar does not effectively represent the concept of business dynamism in case of the EU countries; therefore the new pillar was constructed. A strong and statistically significant correlation between business dynamism and Value Added was confirmed. A relationship between business dynamism and other economic indicators was not proven. From a territorial point of view, Oceania achieved the best overall result in the analyzed field. The process of starting a new business is the most challenging in terms of start-up costs in South America. By contrast, the EU has reached the best result in this process.
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Retraction: Leaders and decision making: a study of the drivers of courage
Rasoava Rijamampianina
doi: http://dx.doi.org/10.21511/ppm.16(1).2018.31
Problems and Perspectives in Management Volume 16, 2018 Issue #1 pp. 320-329 Views: 2088 Downloads: 195 TO CITE АНОТАЦІЯRetracted on the 15th of October, 2020 by the Journal’s Editor-in-Chief request dated October 12, 2020. The type of retraction – plagiarism.
The journal was asked to retract this article because of plagiarism. The request came from the author of the article. The author informed the journal that original idea published in this paper wasn’t his and asked to remove the article. -
State financial assistance within Lesotho Maseru’s small, medium and micro enterprises
Public and Municipal Finance Volume 6, 2017 Issue #4 pp. 7-14 Views: 2005 Downloads: 293 TO CITE АНОТАЦІЯSmall, Medium and Micro Enterprises (SMMEs) function in both the micro and macro environments, and are subject to internal and external forces in most economies. The monitoring of the external and internal factors and vital strategies by SMME owners and stakeholders including government and supporting institutions is necessary to attempt a reduction in their failure rates. Literature supports this proposition in the development of SMMEs.
The aim of this study was to analyze the environmental factors that result in a low rate of success and a high failure rate of SMMEs in Maseru the capital city of Lesotho. Primary data for this study was collected through questionnaires which were distributed to a sample of 250 SMMEs owners, however, only 180 respondents completed and returned questionnaires, which were then used for data analysis. The data was analyzed using the Statistical Package for Social Sciences (SPSS21). The findings revealed that there still persists inadequacy in financial assistance and the need to develop a financial strategy by the state. Government key intervention is proposed to enable growth and development in a sector that sustains job creation and poverty reduction through financial strategy. Further research on how the sector establishes support is also proposed.
