INTERNET TECHNOLOGIES AND DIGITAL MARKETING IN THE STRUCTURE OF MODERN MARKETING RESEARCH

European Science Marketing INTERNET TECHNOLOGIES AND DIGITAL MARKETING IN THE STRUCTURE OF MODERN MARKETING RESEARCH Kateryna Shikovets Associate Professor, PhD in Economics, Department of Marketing and Communication Design, Kyiv National University of Technologies and Design, Kyiv, Ukraine ORCID ID: https://orcid.org/0000-0002-9578-2396 Pavlo Shevchuk Assistant of the Department of Marketing and Communication Design Kyiv National University of Technologies and Design, Kyiv, Ukraine ORCID ID: https://orcid.org/0009-0002-2797-8079 Abstract. The article analyzes in detail the conceptual provisions of the need for the development of digital technologies and analytical marketing in the system of modern marketing research. It is noted that the intensification of the spread, the total spread of Internet technologies and digital marketing requires the formation of fundamentally new strategies for dealing with the consumer or the market as a whole. The study considers Internet technologies as the most important communication tool, a source for analyzing supply or demand, forecasting the variability of market conditions, which in the future will help to make the right management decisions. The methodological achievement of the research is statistical and mathematical models that allow to assess the depth, viability and efficiency of digital channels. The results of the article are of practical importance in terms of the possibility of implementing the developed models to ensure the competitiveness of domestic enterprises and increase their level in the international digital market. Keywords: digital marketing, market, Internet technologies, market research Introduction The total spread of Internet technologies and digital marketing require a fundamental change in the conduct of business, the formation of a new model of cooperation with competitors, consumers or other stakeholders represented in the domestic market. The active introduction of the Internet into all spheres of human life defines it as a conceptual driver of changes in the marketing environment. Today, we are witnessing a dynamic European Science Marketing accumulation of digital platforms and data, the development of mobile applications, and the active digitalization of all production processes, which necessitates the use of the latest approaches to managing consumer demand. In today's market environment, existing marketing tools have already lost their relevance, as information flows have become very dynamic and changeable, which necessitates the development of mathematical models capable of taking into account the multi-segment nature of communication channels. An important task is to timely diagnose consumer reactions in the market and track cluster synergies on the consumer-producer platform. It is time to develop adaptive statistical and mathematical models for evaluating the effectiveness of digital marketing that can be used in practice in the formation of marketing policy of business entities. Literature review.Modern research on digital marketing and Internet technologies is very common in the scientific world. The scientific literature shows in detail the stages of development of marketing analytics, methods of working with Internet data, the use of artificial intelligence and market analysis. For example, the scientific works of Coviello, N., Milley, R., & Marcolin, B. (2001); Steinhoff, L., Arli, D., Weaven, S., & Kozlenkova, I. V. (2019) lay down fundamental approaches to understanding how information technology changes marketing interaction. For our article, this group is important as a theoretical framework that allows us to explain the transition from one-way research to multichannel, interactive, and long-term communication [1-2]. References Faruk, M., Rahman, M., & Hasan, S. (2021); Krishen, A. S., Dwivedi, Y. K., Bindu, N., & Kumar, K. S. (2021) analyze the development of digital marketing based on bibliometric and network methods. For our study, this group is important as evidence of the need to apply systematic and quantitative methods of analysis, which directly resonates with our own models [1-4]. The studies of Jayaram, D., Manrai, A. K., & Manrai, L. A. (2015); Sharabati, A. A. A., et al. (2024); Sharma, A., & Sharma, S. (2025) highlight how digital technologies are used in various business environments. All of them demonstrate the applied dimension of digital strategies. For our article, this group is an empirical basis that supports the results of the calculations in the tables and confirms the feasibility of a multichannel approach in Ukrainian realities [5-7]. Kumar, V., Ramachandran, D., & Kumar, B. (2021); Liu, X., & Li, J. (2025) reveal the impact of new technologies on marketing strategies and corporate performance. This group of authors sets a promising vector of development that correlates with our author's model, which takes into account the investment and inertial factors of digital communications development [8-9]. Summarizing previous studies, we argue that the key methodological challenge is to create universal, yet flexible models that can take into account the complexity of digital processes and the speed of their evolution. European Science Marketing Research methodology. As we have already mentioned, it is time to identify approaches and tools that can determine the effectiveness of modern digital channels, predict changes in consumer behavior, the profitability of investment, or the market's dependence on communication flows. To this end, we will present four statistical and mathematical models that will best help business entities assess the effectiveness of digital strategies. 1. Digital Marketing Integration Index (1): √ (1) where: - the value of the channel indicator (conversion rate, average time spent, share of mobile traffic, etc.); - reference (maximum) value of the indicator in the industry; - weighting factor that reflects the importance of the indicator; - number of channels. allows to combine heterogeneous indicators into a single integrated assessment of the effectiveness of the digital strategy. 2. Model of cognitive user interaction (2): (2) where: - is a function of the intensity of emotional engagement (reactions, comments, repeat visits); - number of clicks during the time ; - average session duration; - a nonlinear function of the influence of these parameters on the overall level of interaction; - time horizon of the analysis. The model takes into account the cognitive-behavioral dimension of marketing research effectiveness, which cannot be reflected by traditional metrics. 3. Dynamic Internet Technology Penetration Index (3): European Science Marketing (3) where: ‒ is the level of penetration of Internet technologies in marketing strategies at a given time ; ‒ coefficient of inertia (indicates the persistence of previous trends); ‒ the volume of investments in digital channels in the period ‒ investment elasticity coefficient; ‒ stochastic influence of the external environment (changes in legislation, global trends, military risks). allows modeling the dynamics of digital transformation, taking into account investment and external factors. 4. Network Digital Impact Index (4): Where: ‒ is an element of the adjacency matrix showing the strength of the relationship between the channels and ; , - weights of channels by the level of influence on the final conversion; ‒ number of channels. allows us to assess the systemic effect of channel interaction, taking into account their network nature, and determine which combinations provide the greatest increase in results. Thus, the proposed formulas form a comprehensive approach to the analysis of complex marketing systems. In the next section, we will perform calculations based on these models using statistical data from Ukraine and international marketing analytical platforms. To adapt the proposed methodology, we used open sources on the digital environment of Ukraine at the beginning of 2025 (https://datareportal.com/) and official publications of the State Statistics Committee of Ukraine (https://www.ukrstat.gov.ua/) on statistics and methodological approaches to ICT measurement used for indicator selection and normalization. Data on Internet penetration (approximately 82-83% of the population, 31.5 million users) is confirmed by a survey (https://datareportal.com/) based on this report, which is consistent with the full survey https://itc.ua/. The global context of the share of digital in advertising budgets according to (https://www.emarketer.com/) 75% of digital spending in the global media mix is also taken into account, which is used as an external comparison parameter for sensitivity. European Science Marketing To harmonize with official approaches to ICT accounting, we used materials and annuals of the State Statistics Committee of Ukraine, in particular the 2023 annual and methodological compendiums that set out indicators of access and use of ICT in households and enterprises. Results. The first model uses three channel indicators: conversion rate, click-through rate, and average customer value, which reflects the marginality of a contact. They were normalized by the reference maximums of industry ranges. We will assume that cognitive interaction for was approximated by discrete summation with a daily time interval during the month. SEO is search engine optimization or increasing the visibility of a website in search engines, SMM is social media marketing, brand promotion through social networks, Email marketing is the use of email for personalized communication with customers, PPC is pay-per-click advertising, an online advertising model where you pay for clicks on ads. In this case, we consider that CR is the conversion rate, CTR is the ratio of the number of clicks on an ad or link to the number of its impressions, ACV is the revenue or average check brought by a client, CRmax is the maximum possible value of the conversion rate in the industry for normalization, CTRmax is the CTR benchmark for comparison, ACVmax is the benchmark value of the average customer value (Table 1). Table 1. Input channel indicators for and reference limits Channel Conversion CR CTR ACV, UAH CRmax, UAH CTRmax ACVmax SEO 0,050 0,100 1000 0,100 0,150 1500 SMM 0,030 0,080 800 0,100 0,150 1500 Email marketing 0,100 0,150 1200 0,100 0,150 1500 PPC 0,040 0,120 1050 0,100 0,150 1500 Source: normalization maxima are chosen taking into account the ranges typical for the Ukrainian e-commerce market; macro context of Internet penetration at https://datareportal.com/ In , weights of 0.4 for CR, 0.3 for CTR, and 0.3 for ACV were applied as they ensure a balance between behavioral and economic components. Table 2. Calculation of by channel (weights for indicators: 0.4, 0.3, 0.3) Channel Normal (CR) Norm(CTR) Norm(ACV) DCIM SEO 0,500 0,667 0,667 0,60 SMM 0,300 0,533 0,533 0,46 Email marketing 1,000 1,000 0,800 0,92 PPC 0,400 0,800 0,700 0,67 Source: calculated by the authors European Science Marketing Intermediate degrees of ascent and products are omitted for compactness; the final values of show the integrated effectiveness, where email marketing leads, followed by PPC and SEO, while SMM is inferior due to lower CR and ACV. The model uses with parameters = 0.35, = 0.25, = 1. The data for 30 days is modeled within realistic levels for Ukrainian online retail. Average by channel: for Email - higher repeat visits and session time; for SEO - stable conversions; for PPC - higher clickthrough rate with lower S; for SMM - higher E due to emotional reactions. Table 3. Generalized monthly (discretization of the integral by the sum of 30 days) Channel Average E Average C Average S UCM SEO 120 2400 80 5,95×10^4 SMM 220 2000 70 6,08×10^4 Email marketing 180 1200 140 6,42×10^4 PPC 100 3800 55 5,51×10^4 Source: calculated by the authors Using step elasticities, emphasizes that even with a relatively lower number of clicks, the email channel is ahead due to its higher depth of contact (S) and stable engagement (E). For the dynamic model, the time step is in years. The baseline of 2021 is taken as 100. The values for 2022-2024 have increased, which is consistent with the global increase in the share of digitalization in the media mix; 2025 is a model forecast. The parameters = 0.65 and = 0.35 reflect the ratio of inertia and investment momentum; the noise in the table is zero for transparency. The global corridor for the digital share of advertising spending and the overall acceleration of digitalization is confirmed by eMarketer 2025 and the summary panels https://www.emarketer.com/ and https://datareportal.com/. Table 4. Reconstruction and forecast of based on the investment index (base 2021 = 100)* Year Reconstruction Calculation 2021 100 100 - 2022 108 106 102,8 2023 115 113 107,5 2024 123 121 112,1 2025* 130 - 118,5 *2025 is a model step forward assuming further investment growth after the digital communications market stabilizes in 2024-2025. Source: calculated by the authors European Science Marketing Four channels are used for the network model . The adjacency matrix A= measures the strength of cross-channel influence from to (01). The weight vector was obtained from and scaled to the sum of 1.0. Table 5. Adjacency matrix A and channel weights of (normalized) SEO SMM Email PPC SEO 0,00 0,30 0,15 0,25 SMM 0,20 0,00 0,15 0,20 Email 0,10 0,15 0,00 0,20 PPC 0,25 0,20 0,10 0,00 Source: calculated by the authors Weight vectors (from , normals): VSEO= 0.23, V(SMM) = 0.18, VEmail= 0.35, V(PPC) = 0.24. Table 6. Calculation of the network index Source of influence SEO 0,30-(0,23/0,18)+0,15-(0,23/0,35)+0,25- (0,23/0,24) = 0,383 SMM 0,20-(0,18/0,23)+0,15-(0,18/0,35)+0,20- (0,18/0,24) = 0,335 Email 0,10-(0,35/0,23)+0,15-(0,35/0,18)+0,20- (0,35/0,24) = 0,649 PPC 0,25-(0,24/0,23)+0,20-(0,24/0,18)+0,10- (0,24/0,35) = 0,490 Total 1,857 Source: calculated by the authors The resulting network index of 1.857 indicates a distinct systemic effect, in which the strongest "donor of influence" is the email channel, which is consistent with the results of and : its interactions transfer weight to other touches, strengthening conversion chains. The aggregate index has formed a channel ranking in which email marketing is the leader due to the most normalized CR and CTR and high ACV; PPC is second due to higher CTR and average check, while SEO, despite its stability, is inferior in terms of conversion; SMM shows lower efficiency in monetary terms due to lower CR and ACV, although it partially compensates for this with emotional engagement. The model emphasizes the importance of cognitive interaction, where even with lower click-through rates, the depth of contact enhances the integrated result, reflecting the realities of Ukrainian audience behavior in 2024-2025 (which is European Science Marketing also reflected in our observations in https://datareportal.com/ and https://itc.ua/). The network index makes cross-channel synergies visible: email campaigns not only close transactions but also "heat up" SEO and PPC, enhancing remarketing effects, while SMM acts as a trigger for emotionality and social proof. Applied recommendations follow directly from the numerical results: increase the share of email touches in the cascade, optimize segmentation for PPC, consolidate content strategies for SEO, and target SMM at the upper funnel stages. Conclusions. Four author's models have been built that form an integral system for evaluating digital marketing in the structure of modern marketing research. The integral sets the quantitative framework for comparing channels; captures the nonlinear cognitive effects of interaction; reproduces the inertial-investment vector of digital transformation; reveals the network synergy of channels. The original calculations demonstrate the leadership of the email channel in integrated metrics and the importance of combining PPC and SEO as cascade amplifiers, as well as the role of SMM as an emotional engagement generator. The combination of macro-sources (https://datareportal.com/, https://www.emarketer.com/) with the State Statistics Committee of Ukraine's ICT accounting methods ensures reproducibility and methodological consistency. Further research should focus on expanding the time series, enriching with physiological markers of attention, and calibrating on the real investment curves of Ukrainian companies. References 1. Coviello, N., Milley, R., & Marcolin, B. (2001). Understanding IT-enabled interactivity in contemporary marketing. Journal of interactive marketing, 15(4), 18-33. https://doi.org/10.1002/dir.1020 2. Steinhoff, L., Arli, D., Weaven, S., & Kozlenkova, I. V. (2019). Online relationship marketing. Journal of the Academy of marketing science, 47(3), 369-393. https://link.springer.com/article/10.1007/s11747-0180621-6 3. Faruk, M., Rahman, M., & Hasan, S. (2021). How digital marketing has evolved over time: A bibliometric analysis on scopus database. Heliyon, 7(12). 4. Krishen, A. S., Dwivedi, Y. K., Bindu, N., & Kumar, K. S. (2021). A broad overview of interactive digital marketing: A bibliometric network analysis. Journal of Business Research, 131, 183-195. https://doi.org/10.1016/j.jbusres.2021.03.061 5. Jayaram, D., Manrai, A. K., & Manrai, L. A. (2015). Effective use of marketing technology in Eastern Europe: Web analytics, social media, customer analytics, digital campaigns and mobile applications. Journal of economics, finance and administrative science, 20(39), 118-132. https://doi.org/10.1016/j.jefas.2015.07.001 European Science Marketing 6. Sharabati, A. A. A., Ali, A. A. A., Allahham, M. I., Hussein, A. A., Alheet, A. F., & Mohammad, A. S. (2024). The impact of digital marketing on the performance of SMEs: An analytical study in light of modern digital transformations. Sustainability, 16(19), 8667. https://doi.org/10.3390/su16198667 7. Sharma, A., & Sharma, S. (2025). Digital marketing adoption by small travel agencies: a comprehensive PLS-SEM model using reflective and higher-order formative constructs. European Journal of Innovation Management, 28(2), 560-590. https://doi.org/10.1108/EJIM-09-20220532 8. Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of newage technologies on marketing: A research agenda. Journal of Business Research, 125, 864-877. https://doi.org/10.1016/j.jbusres.2020.01.007 9. Liu, X., & Li, J. (2025). Digital Marketing and Corporate Market Performance: The Mechanism of FinTech Development. Finance Research Letters, 108203. https://doi.org/10.1016/j.frl.2025.108203