Assessment of the level of business readiness for digitalization using marketing and neural network technologies

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The marketing environment of the world economy is changing due to intensive digitalization of trade exchange operations. Formation of marketing forecasts based on current and past periods in modern conditions is irrelevant to the current situation. The purpose of the article is to assess the situational precedents of business readiness for digitalization based on monitoring data, operating environment, applications and management system when using the tools of marketing and neural network modeling. The article uses a systematic approach and methods of statistical, financial and marketing analysis, tools for modeling a neural network. Based on the estimated indicators, the current and forecasted levels of electronic retail in the world are revealed. Based on the application of the concept of portfolio analysis to the data of national and international monitoring, а marketing model of research has been built, in which low business efficiency has been determined, situational modeling of business readiness for digital transformation has been carried out and characteristics of the identified precedents have been given. A low degree of business readiness to digitize the economy has been established. The results emphasize the importance of monitoring business readiness for the digitalization of the economy in real time with marketing and neural network modeling.

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    • Figure 1. Results of marketing analysis of digital process perspectives for the modified BCG matrix
    • Figure 2. Generalized view of the CPSS architecture in the digital economy
    • Figure 3. Errors in training educational and test samples of mathematical models of neural networks, the influence of the business processes digitization, which showed the highest performance
    • Figure 4. Scatter diagram of calculated and actual value of a resulting indicator of the mathematical model of neural network, influence of the digitization of business processes such as MLP 3-4-1
    • Figure 5. Mathematical dependence of the resulting indicator (y) and independent variable x1 on the type MLP 3-4-1 neural network
    • Figure 6. Mathematical dependence of the resulting index (y) and the independent variable x2 on the MLP 3-4-1 type neural network
    • Figure 7. Mathematical dependence of the resulting index (y) and the independent variable x3 for the MLP 3-4-1 type neural network
    • Figure 8. Cross-section of response surface of result indicator (y) and independent variables of x1 and x2 for a neural network of MLP 3-4-1 type
    • Figure 9. Cross-section of response surface of result indicator (y) and independent variables x2 and x3 for a neural network of MLP 3-4-1 type
    • Figure 10. Cross-section of response surface of result indicator (y) and independent variables of x1 and x3 for a neural network of MLP 3-4-1 type
    • Table 1. The rating of European countries in the strategic vision of the development of outsourcing business (worldwide and European ratings)
    • Table 2. Worldwide electronic retail commerce by country
    • Table 3. TOP-10 retail companies of the world in 2016
    • Table 4. World e-commerce by regions (forecast), sales, %
    • Table 5. The dynamics of independent factors and the resulting indicator
    • Table 6. Results of mathematical models of neural networks of the impact of the business processes digitization, which showed the highest performance in the training, control and test samples
    • Table 7. Baseline data for situational modeling of independent factors influencing the business processes digitization based on the design of artificial neural network at worst, average and best script