A data science-based marketing decision support system for brand management


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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.

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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
    • Table 1. General functional structure of the multi-agent MDSS “Brand’s marketing management system”
    • Conceptualization
      Galyna Chornous, Yana Fareniuk, Vincentas Rolandas Giedraitis
    • Methodology
      Galyna Chornous, Yana Fareniuk
    • Resources
      Galyna Chornous, Yana Fareniuk
    • Supervision
      Galyna Chornous, Vincentas Rolandas Giedraitis
    • Validation
      Galyna Chornous, Yana Fareniuk, Erstida Ulvidienė, Ganna Kharlamova
    • Writing – original draft
      Galyna Chornous, Yana Fareniuk
    • Writing – review & editing
      Galyna Chornous, Yana Fareniuk, Vincentas Rolandas Giedraitis, Erstida Ulvidienė, Ganna Kharlamova
    • Formal Analysis
      Yana Fareniuk
    • Investigation
      Yana Fareniuk
    • Visualization
      Yana Fareniuk
    • Project administration
      Vincentas Rolandas Giedraitis, Erstida Ulvidienė, Ganna Kharlamova