The article aimed to develop a methodology for monitoring the quality level of the synergistic effect of enterprises' economic activity in business network interaction on the example of mechanical engineering in Ukraine. Using the expert assessments of 386 senior and middle managers of 27 mechanical engineering enterprises of the Kharkiv region and the main components' method, the components of qualitative parameters of the synergetic effect of economic activity result from network interaction have been determined. An additive econometric model has been developed to calculate the integrated indicator of enterprises' economic activity's synergetic effect in terms of the business network of the studied enterprises. Using Fibonacci rules, the levels of qualitative component components and the integrated indicator of the synergy of economic activity in the context of a single network partner are determined.

The authors analyzed the basic trends in development of marketing communications in Ukraine and defined efficient innovating marketing methods, one of which is neuromarketing. The article generalizes experience in implementing neuromarketing techniques in Ukraine and all over the world, systematizes methods, technologies and tools of neuromarketing and defines the areas of usage for business activity in Ukraine. Besides, the article analyzes faults, advantages and prospects of neuromarketing development, classifies target audiences in terms of their response to neuromarketing methods and techniques. It also touches a need to implement on-line neuromarketing tools.

The article deals with the hierarchical clustering of operating rail national companies as the main stage of the benchmarking of Europe's rail transport market, which was proposed for the first time. It was established that at Euro integration the results of taxonomy make it possible to determine efficient areas of cooperation and provide a sufficient competitive level in the rail industry both for European counties and Ukraine. And an optimal criterion for cluster analysis, as was established, is the efficiency indices. Hence, the basic result in hierarchical clustering is division of rail companies into groups with similar performance characteristics. It will allow revealing the main competitors in the industry for each company on the basis of technical, geographic and economic conditions in a particular country. Detailed research of clusters with the k-means method made it possible to obtain more detailed information on characteristics of division and influence of factors on clusters. For accurate identification of characteristics for each cluster a single-factor dispersion analysis was performed. The analysis established the average value of an indicator for the clusters, standard deflection, maximal and mini mal values. This information is required for development of the priority areas to improve the operating efficiency and ensure a sufficient competitive level in the rail industry of the EU countries.Keywords: Benchmarking of transport market;Cluster analysis for the rail transport; Efficiency of railway operations; Hierarchical clustering; The kmeans method.