The importance and authority of the MarTechMap crowdsourced database, which is based on the division of the marketing technology market into six product segments according to S. Brinker, is universally recognized. However, the scope of the map excludes the Chinese and Russian markets, for which no crowdsourced map is currently available. Additionally, there is a notable absence of independent annual reviews that aggregate supplier data by MarTech segment. Consequently, a direct comparison of the MarTech markets is an arduous task, and to the best of the authors’ knowledge, it has never been done. The research model employed by the authors in the second section of this paper is grounded in practical and theoretical approaches presented in contemporary sources. For instance, the Gartner website contains a paragraph in the “Methodology” section titled “How Forecasts Work.” This paragraph indicates that one of the factors for forecasting the IT market (including the Software and Services segments) is the analysis of internet user queries. Therefore, the authors of the present article, lacking data on the MarTech markets in Russia and China, used this approach to obtain indicators of market structure. These indicators can also be used to predict the near future, as shown in research literature (Mao et al., 2011; Bulczak, 2021).

As shown in Table 1, each MarTech market segment contains between 9 and 10 subsegments¹. The names of the MarTech market segments are generally descriptive, so the subsegment names—illustrated as examples in Tables 2 and 3—were used as search queries to analyze internet user interest. The total number of queries for each segment was calculated by summing the queries across all subsegments (see Figures 1 and 2). This approach provides an aggregate measure of user interest for each segment.

From October to November 2025, we analyzed search query volumes, taking into account the linguistic specifics of each country. For China, data from Baidu and the 5118 marketing data platform were used, including desktop and mobile search queries. These queries were then summed. For Russia, keyword search volume data were collected via the Yandex Wordstat service. Both the Chinese and Russian services track search query volumes, ensuring data comparability. To enable clear comparison between the two markets, the analysis was conducted based on the percentage share of queries across MarTech market segments rather than absolute query counts (see Figure 3).

Figure 1 shows that a significant proportion of search queries in the Chinese MarTech market are concentrated in the Data segment, indicating high user interest in this area. Interest is lowest in the Advertising and Promotion segment. Therefore, based on the structure of search interest, the Chinese MarTech market is primarily oriented towards solutions related to data accumulation, integration, processing and analytical utilisation, rather than advertising tools.

The reasons for the pronounced dominance of the data segment are analyzed in Table 2. As can be seen, user interest is concentrated in sub-segment 3.4: Business Analytics/Customer Analytics Services. These practical tools involve integrating information to determine the contribution of marketing channels and tools to business outcomes supporting managerial decision-making. These areas cannot develop independently of artificial intelligence technologies, which, in particular, enable the automated processing of large volumes of data, for example, for customer behavior analysis, building user profiles, predictive analytics, personalized communication and intelligent audience segmentation.

In order to assess user interest in marketing technology solutions in Russia, an analysis was conducted of the volume of major search queries across MarTech market segments for the year 2025.

Figure 2 shows that user interest in the Russian MarTech market is most pronounced in the ‘Social and Relationships’ segment. Table 3 presents Yandex Wordstat data on the volume of search queries for the Social and Relationships subsegments in 2025.

As shown in Table 3, the highest number of search queries is concentrated in the following subsegments: 2.7 Review Management, 2.5 Loyalty Programs and CRM Systems, and 2.6 Recommendation Systems. All of these services are closely integrated with artificial intelligence technologies, which enable user behavior analysis, personalization and the strengthening of customer relationships.

Figure 3 shows the structure of search queries in the MarTech market in Russia and China. The segment-level data in the chart are expressed as a percentage of the total number of queries, enabling clear comparisons to be made between the two countries’ MarTech market segments in terms of their search query structure.

The comparison results reveal differences in the structure of internet user queries related to the MarTech market in Russia and China. In Russia, the largest proportion of queries fall within the Social and Relationships segment. Meanwhile, queries for products in the E-Commerce, Advertising and Promotion, and Marketing Management segments are distributed relatively evenly, with the smallest share pertaining to the Data Management segment. In contrast, Chinese users show a particularly high level of interest in the Data Management segment, while the Advertising and Promotion segment receives the lowest share of queries.

In Chinese scientific literature, user segments in the MarTech market are categorized by industry and functional characteristics. The existence of such segments in the Russian MarTech market has not yet been studied. In this paper, the authors address this issue in the context of AI implementation for marketing purposes by Russian companies. In the third section of the paper, the authors relied on the research model applied by S. Brinker et al. during the surveys conducted in December 2024 and the first quarter of 2025 on AI adoption in company marketing stacks for MarTech market segmentation (Brinker, 2025)).

This study uses a questionnaire developed by the authors of this article to analyze the Russian MarTech market. The questionnaire has a specific structure and logic. Following a screening question (Q1) about the use of marketing services within company activities, the first section of the questionnaire begins with ‘Experience in Using Marketing Technologies’. Respondents are then asked to select the MarTech market segment and sub-segment to which they believe their technologies belong. Questions Q9–Q11 cover the preferred number of functions, the pricing model for services and problems encountered when implementing and using marketing technologies. The second section (Q12–Q16) of the questionnaire is devoted to the use of AI in company marketing. The third section, ‘About Your Company’ (Q17–Q20), collects information about the company and the person completing the questionnaire.

To study and segment consumers in the Russian MarTech market, a survey of Russian users of marketing services was conducted from September to November 2025. Responses were collected from 407 participants. The following methods were used to obtain respondents’ answers:

1. Direct distribution of the questionnaire to respondents;

2. Sharing the survey link within the professional community Telegram Data Practicum Chat;

3. Distribution of the questionnaire through the Russian survey platform Anketolog, used for survey creation, administration and analysis;

4. Formal face-to-face questionnaire interviews at conferences: 1) MarTech Spring, AdTech Talks ’24; 2) Marketing Data Analytics 2025.

Sample characteristics. To confirm the use of MarTech, a screening question was included at the beginning of the questionnaire: “Do you use marketing technologies (MarTech) in your work for the company?” Out of 519 respondents, 407 confirmed that they use marketing services. The respondent companies varied in size: 22.1% were small enterprises with 1–50 employees, 30.0% were medium-sized enterprises with 51–200 employees, and 47.9% were large enterprises with 201 or more employees. In terms of professional roles, the sample included technical and marketing specialists (26.0%), content specialists (16.2%), advertising specialists (9.6%), managers (31.2%), individual entrepreneurs (12.3%), and representatives of other categories (4.7%). Overall, 88.0% of respondents said that their company uses artificial intelligence for marketing purposes.

The primary method used to process the survey data for the purpose of segmentation was cluster analysis (McQueen, 1967). The optimal number of clusters was determined using the elbow method and the silhouette coefficient, which confirmed that identifying three segments was the most robust solution both statistically and substantively.

Clustering was performed using SPSS 27.0 based on the following questionnaire items: Q9 – preferences for using specialized or multifunctional marketing services; Q10 – assessment of price dependence on the number of functions in a service; Q13 – frequency of AI tool usage; Q14 – expectations regarding staff replacement by AI; Q15 – assessment of AI’s impact on business practices; Q16 – existence of an official AI usage policy in the company; Q18 – company size; Q19 – duration of marketing technology usage; and Q20 – respondent’s position.

To validate the clustering, multicollinearity was assessed using the Variance Inflation Factor (VIF), which identifies excessive correlation between variables. This analysis was conducted using SPSS 27.0 on a sample of 407 completed questionnaires that had no missing values.

Based on the assessment, all variables had VIF values below 5, indicating the absence of significant linear dependence among them and allowing the selected set of variables to be used for multiple regression and other multivariate analysis methods (see Table 4). Therefore, the original sample can be characterized as a statistically robust and balanced dataset.

The cluster analysis identified three user segments. The first segment included 112 respondents, the second 182, and the third 113, as shown in Figure 4.

Segment 1 comprises large companies with moderate AI usage intensity. Segment 2 consists of medium and advanced small companies with a high frequency of AI usage. Segment 3 consists of small companies that are lagging behind with low AI usage frequency.

Figure 5 shows that Segment 1 (large companies) has the most balanced profile, with broad industry coverage. In contrast, Segment 2 (medium and advanced small companies) is primarily focused on the consumer sector, information and communication services, education, and finance. Segment 3, comprising lagging small companies, shows a narrower industry focus, mainly in information technology and consumer goods.

Figure 6 illustrates how frequently companies in each segment use artificial intelligence tools.

Figure 7 shows how the segments are characterized according to respondents’ expectations of the impact of AI on company operations. Phi = 0.374 and Cramer’s V = 0.265 indicate a statistically significant association between segment membership and expectations regarding AI’s influence on business practices (p < 0.001). In all three segments, the majority of respondents believe that AI can improve business practices, although the strength of this assessment varies.

The highest percentage of respondents who rated the impact of AI as positive is found in Segment 2, which comprises medium and advanced small companies (77.5%). This corresponds with the more frequent and active use of marketing technologies and AI within this segment. In Segment 1 (large companies), positive evaluations also predominate (62.5%), though the proportion of respondents is lower than in Segment 2. In Segment 3, which comprises lagging small companies, 38.9% of respondents assessed the impact of AI positively, while 27.4% were unsure. This suggests that perceptions of the practical effects of AI are not yet fully formed in this segment and companies in Segment 3 are less ready to adopt it.

Figure 8 presents user segment preferences regarding the set of functions in a marketing service. Cramer’s V = 0.178 with p < 0.001 indicates a statistically significant, though weak, association between segment membership and preferences for the type of marketing service.

Multifunctional platforms are the most demanded across all segments: Segment 1 — large companies (82.1% of respondents), Segment 2 — medium and advanced small companies (75.3% of respondents), and Segment 3 — lagging small companies (58.4% of respondents). Companies in different segments use marketing technologies with varying intensity, which accounts for the differences in preferences between segments.

The following priorities can be observed among the user segments: Segment 1: Large companies value customization and security.

Segment 2: Medium and small advanced companies focus on efficiency and convenience.

Segment 3: Small lagging companies prioritize simplicity and ease of use.

Figure 10 shows the differences in perception of marketing technology barriers across user segments. However, statistical analysis indicates that there are no statistically significant differences between segments for all barriers (p = 0.746, 0.854, 0.670, 0.669, 0.291, 0.440, 0.256, and 0.465, respectively). Analysis of the overall sample showed that each barrier occurred in approximately the same proportion of respondents: about 62% indicated high service costs, 50% — difficulties with setup and integration of services, 47% — insufficient flexibility in configuring services, 26% — problems with data quality and analytics, 48% — challenges in employee training, 31% — data security issues, 33% — rapid technology obsolescence, and 29% — insufficient support from service providers.