May 06, 2026
Marketing and Segmentation in Running Groups
Luis Rafael Marques Ratão; Leonardo Carlim
Summary prepared by the ResumeAI tool, an artificial intelligence solution developed by the Pecege Institute focused on synthesis and writing.
Road running has established itself as one of the most widespread sporting practices in the contemporary scene, transcending mere physical activity to become a platform for socialization and the formation of local communities. This phenomenon is driven by the simplicity and accessibility of the modality, which favors the creation of interaction spaces that spark companies’ interest in using sport as a strategic channel for market relationship. In this context, the relationship between sports practices and business strategies directs the focus to marketing processes, as observed by Lim and Rasul (2022). Experience marketing emerges as a fundamental approach to establishing deep bonds between brands and consumers, using meaningful interactions that go beyond commercial transactions. In sports environments, brand activation manifests through practical actions, such as distributing giveaways, holding raffles, the presence of elite athletes, or offering specialized services at event venues, which reinforces brand recall and expands contact with heterogeneous audiences (Kotler and Keller, 2016). This dynamic is intrinsically linked to the advancement of relational marketing, which prioritizes a detailed understanding of consumer behaviors and preferences for building long-term loyalty (Kumar and Reinartz, 2018).
The relevance of this scenario stems from sport’s ability to mobilize various economic sectors, including health, fashion, food, and technology. Running events attract segments seeking not only physical performance but a specific lifestyle, creating a conducive environment for companies wishing to reinforce their identity through shared experiences among runners and their communities (Keller and Swaminathan, 2020). Consumer engagement in these events therefore becomes a central element for the effectiveness of relationship strategies (Lim and Rasul, 2022). However, a significant practical challenge arises in identifying which elements of marketing activations are effectively perceived as memorable by participants. Many organizations invest considerable resources in various actions without clarity on which factors truly stimulate engagement, consumption, or loyalty (Rungruang et al., 2024). The lack of this understanding compromises the assertiveness of investments and can drastically reduce the impact of campaigns in the sports environment, resulting in inefficient allocations of capital and operational effort.
Overcoming this gap allows for the formulation of more consistent and personalized strategies. By identifying the specific preferences of different runner profiles, companies can direct their activation campaigns in a segmented manner, optimizing resources and expanding the potential return on investment (Wang, Sun, and Yu, 2024). This strategic refinement process is strengthened by the use of customer value metrics and the application of dynamic segmentation, tools considered central in modern marketing management (Kumar and Reinartz, 2018). The benefits of overcoming this challenge include superior accuracy in choosing actions, allowing different runner profiles to respond distinctly to stimuli such as the presence of influencers, post-event benefits, or exclusive raffles. The use of quantitative approaches, combined with profile mapping, offers a solid basis for practice-oriented decisions, ensuring that brand communication resonates with the target audience’s values (Rungruang et al., 2024). Furthermore, sports event marketing acts on fundamental psychological factors, such as autonomy and relationship, which influence consumer participation and enhance the effects of activations (Moghaddam et al., 2024). Understanding experience as a relational process that connects the brand, the consumer, and the social context is, therefore, essential for the success of initiatives in the field of strategic marketing.
To investigate these dynamics, the study was structured as a field research of a quantitative nature and exploratory character, aiming to collect information directly from the participants of the investigated phenomenon. The quantitative approach was selected for allowing the measurement of responses and the analysis of statistical patterns among different participant profiles, using a structured questionnaire with closed-ended questions. The exploratory nature was justified by the need to identify which elements of marketing activations are perceived as most effective in a context still little explored in detail by national literature. The data collection instrument consisted of an online questionnaire, developed on the Google Forms platform, structured in thematic blocks to capture sociodemographic, behavioral, and perception information. The application took place between January 26 and February 19, 2025, with broad dissemination in running groups via instant messaging applications, which allowed reaching a total of 230 valid responses. The use of digital tools for data collection enabled access to a geographically dispersed audience, ensuring the standardization of the process and the integrity of the collected information for subsequent statistical analysis.
The first block of the questionnaire focused on sociodemographic variables, collecting data on gender, age, education, income, occupation, and region of residence, providing the basis for sample characterization. The second block addressed sports practice and consumption, inquiring about the frequency of participation in running groups and the types of products and services consumed, such as footwear, supplements, and health services. The third block specifically dealt with experiences with running events, gathering motivations for participation and elements that make activations attractive, as well as communication channels that influence engagement. The fourth block examined runners’ perceptions regarding engagement with brands, including changes in consumption behavior after events and interest in additional experiences. The formulation of these questions was anchored in consolidated theoretical references, following recommendations from Kotler and Keller (2016) for market segmentation and from Kumar and Reinartz (2018) for mapping consumption habits. The construction of the instrument also considered experience marketing studies that highlight the importance of face-to-face interactions and social engagement (Keller and Swaminathan, 2020).
The treatment of the collected data began with consistency verification, eliminating duplicate or incomplete records to ensure the validity of statistical results, as recommended by Hair et al. (2009). The data were organized and categorized, transforming textual responses into numerical categories to enable complex analyses. The first stage consisted of applying descriptive statistics to present frequencies and means, providing an initial overview of participation and consumption patterns. According to Field (2013), this stage is crucial for initial exploration before applying multivariate techniques. Subsequently, Analysis of Variance was used to verify significant differences between sociodemographic groups regarding perceptions of activations. To reduce data dimensionality and identify latent patterns, Principal Component Analysis was applied, allowing the grouping of items related to perceptions of events. Based on these results, cluster analysis was performed to segment runner profiles, identifying distinct groups with similar characteristics and preferences. The combination of these techniques allowed for a detailed understanding of both the sample’s general behavior and the existing nuances among specific profiles, strengthening data interpretation (Malhotra, 2012).
The results revealed a female predominance in the sample, with 66.5% of participants being women versus 33.5% men. This data suggests that marketing actions in running events can achieve greater reach by considering the consumption specificities and preferences of the female audience, incorporating elements of health, aesthetics, and well-being. The age distribution was predominantly concentrated between 26 and 39 years old, representing 57.4% of the total, a group marked by professional stability and rising purchasing power. The presence of young people aged 18 to 25 reached 23.5%, indicating the need for accessible digital strategies, while groups over 40 years old, although smaller, represent important niches for activations related to longevity. Regarding education level, a strong presence of individuals with postgraduate degrees (39.6%) and complete higher education (36.1%) was observed, reflecting an audience with high educational capital and sophisticated expectations regarding the technical quality of activations and brand narratives.
The monthly income analysis showed that 38.3% of respondents earn between 4 and 10 minimum wages, indicating medium-high purchasing power that values direct benefits and exclusive experiences. Employed professionals represent 58.7% of the sample, followed by self-employed individuals with 20.0%, suggesting a structured routine that favors the consumption of planned leisure experiences. Geographically, there was a massive concentration in the Southeast region, with 87.8% of responses, highlighting that the analyzed profile reflects the reality of large urban centers, especially São Paulo. As for participation frequency, 28.7% of runners participate in events every two to three months, demonstrating recurring engagement that allows for seasonal campaign planning. Furthermore, 83.5% of respondents have participated in events promoted by sports brands, which validates the group’s prior experience and reinforces the authority of the collected perceptions.
The Analysis of Variance application allowed for the identification of critical differences in perceptions according to sociodemographic variables. The age factor proved decisive in evaluating communication through traditional media, with a p-value of 0.0104, indicating that older groups attribute greater importance to newspapers and radio. In contrast, communication via official social media pages and the performance of digital influencers showed significant differences based on age, with younger individuals evaluating these actions much more positively (p < 0.001). These findings reinforce the need for generationally segmented communication strategies. The region variable also showed relevance in the perception of offline presence, such as banners and stands, suggesting that in territories with lower event saturation, these physical actions have a proportionally greater impact. The possibility of co-creation with the brand also varied according to geographical location, indicating that the desire for direct interaction with product development is influenced by the runner’s regional context.
The Principal Component Analysis identified two fundamental axes that explain 51.4% of the total data variance. The first component grouped items related to social media, influencers, and in-person events, configuring a digital and experiential dimension of engagement. The second component concentrated loadings on traditional media and offline presence, characterizing an axis of traditional exposure and directed participation. This distinction is fundamental for segmentation, as it demonstrates that perceptions are not independent but are organized into coherent behavioral structures. The analysis of factorial loadings confirmed that runners who value digital interaction also tend to be more receptive to direct experiences at events, composing a profile of high engagement. On the other hand, the traditional axis attracts an audience that prefers more passive and institutional interactions, requiring distinct communication approaches to ensure the effectiveness of the brand’s message.
The clustering technique allowed for the identification of two distinct groups. Cluster A, composed of 188 individuals, was classified as the engaged group, showing high averages in all activation elements, especially in social media (4.81) and in-person events (4.57). This group is characterized by frequent participation, with 34% participating in events every two to three months, and an age distribution concentrated between 26 and 39 years. Cluster B, with 42 individuals, showed less engagement, with significantly lower averages in all items, such as 2.38 for traditional media and 2.67 for influencers. This group has a more sporadic presence at events and a higher concentration of individuals in older age groups or with lower participation frequency. The distinction between these clusters is crucial for marketing management, as it indicates that Cluster A should be the focus of intensive and innovative strategies, while Cluster B demands low-cost maintenance actions and simplified institutional communication.
The discussion of the results reveals that the findings dialogue directly with the literature on experiential and relational marketing. The higher valuation of digital and in-person elements by Cluster A confirms Kotler and Keller’s (2016) thesis on the importance of creating meaningful connections to strengthen the brand. The need for segmentation based on engagement level, observed in the clear distinction between clusters, corroborates Kumar and Reinartz’s (2018) view on resource optimization through understanding customer behavior. The challenge pointed out by Rungruang et al. (2024) regarding clarity in the effectiveness of actions is addressed by demonstrating that intensive efforts in influencers and social media yield superior returns only when directed at the receptive profile. For the less engaged profile, insisting on these tactics can result in budget waste, suggesting that selective strategies are more appropriate.
The relevance of sporting events as brand-building platforms, discussed by Keller and Swaminathan (2020), is evidenced by the high value attributed to shared experiences by Cluster A members. The integration of digital channels and in-person experiences emerges as the most effective way to increase consumer proximity, as suggested by Lim and Rasul (2022). Furthermore, participants’ sensitivity to factors such as autonomy and relationship, mentioned by Moghaddam et al. (2024), is reflected in the preference for activations that allow interaction and co-creation. The study demonstrates that overcoming the challenge of mapping preferences allows for a much more precise allocation of resources, where investment in social media and influencers should be prioritized for the young and highly active audience, while the older or less frequent audience can be reached more efficiently through traditional and institutional touchpoints.
In practical terms, the data suggest that marketing managers should adopt a clear roadmap of action. For the engaged audience, investment in integrated campaigns is recommended, combining continuous digital presence with memorable experiences in the physical race environment, including co-creation actions that generate brand identification. For the less responsive audience, the strategy should focus on minimal visibility and relationship maintenance through discreet sponsorships and direct communication via email or traditional media. This segmented approach not only improves the efficiency of marketing budget use but also strengthens the relationship with different runner profiles, expanding the potential for long-term loyalty. The recognition of the sample’s geographical limitations, concentrated in the Southeast, suggests that future research should explore other regions to verify whether behavioral patterns are maintained in distinct cultural and economic contexts.
It is concluded that the objective was achieved, as the study clearly identified the elements of marketing activations that are most impactful for different runner profiles, demonstrating that social media, in-person events, and influencers are the main drivers of engagement for the more receptive audience. The existence of two distinct clusters reinforces the imperative need for strategic segmentation, showing that the more engaged profile values digital and experiential integration, while the less engaged profile demands low-cost maintenance strategies. The results confirm the theoretical propositions on experiential marketing and offer practical subsidies for professionals and companies to optimize resource allocation in sporting events. The research demonstrates that the effectiveness of activations depends directly on the alignment between the type of action and the runner’s behavioral profile, consolidating the sport as a robust platform for brand strengthening when managed under a dynamic and relational segmentation perspective.
Bibliographic References:
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Keller, K. L., & Swaminathan, V. (2020). Strategic brand management: Building, measuring, and managing brand equity (5th ed.). Pearson.
Kotler, P., & Keller, K. L. (2016). Marketing management (16th ed.). Pearson.
Kumar, V., & Reinartz, W. (2018). Customer relationship management: Concept, strategy, and tools (3rd ed.). Springer.
Lim, W. M., & Rasul, T. (2022). Customer engagement and social media: Revisiting the past to inform the future. Journal of Business Research, 148, 325–342. https://doi.org/10.1016/j.jbusres.2022.04.010
Malhotra, N. K. (2019). Marketing research: An applied orientation (7th ed.). Pearson.
Moghaddam, B., Nouri, M., Rezaei, S., & Safari, M. (2024). Enhancing consumer participation in the context of sport events: The power of three basic psychological needs. Journal of Convention & Event Tourism, 25(4), 259–281. https://doi.org/10.1080/15470148.2023.2256789
Rungruang, C., Riyapan, P., Intarasit, A., Chuarkham, K., & Muangprathub, J. (2024). RFM model customer segmentation based on hierarchical approach using FCA. Expert Systems with Applications, 237, 121449. https://doi.org/10.1016/j.eswa.2023.121449
Wang, S., Sun, L., & Yu, Y. (2024). A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering. Scientific Reports, 14, 17491.
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