A Systems Perspective on Customer Segmentation as a Strategic Tool for Sustainable Development Within Slovakia’s Postal Market

Abstract

Customer segmentation is a foundation of Customer Relationship Management (CRM) and is widely regarded as a key to business development success. As the principles of sustainable development become increasingly central to business strategy, it is necessary that social, environmental, and economic considerations be incorporated into customer segmentation—even in regulated markets such as the postal market. The article develops and applies a three-dimensional (3D) segmentation model of business customers in the Slovak postal market, utilizing cluster analysis within STATISTICA analytical software for operationalization of the segmentation criteria. The 3D model reacts to the three pillars of sustainable development and is verified under real conditions at Slovak Post, plc. By adopting a systems perspective, the research places customer segmentation as an integral component of the entire socio-technical system, emphasizing the interrelatedness of organizational, social, and environmental considerations. The study illustrates how a systems-based approach to segmentation enables postal operators to uncover key customer segments, optimize resource allocation, and support competitiveness and sustainability goals. The practical applicability of the model is illustrated by its potential for application in other regulated service industries, providing a solid framework for sustainable customer management and strategic decision-making in complex environments. The research underscores the critical role of systems thinking in addressing the complex challenges of sustainable development in regulated industries.

1. Introduction

Many businesses are currently adapting their strategies to respond to the growing emphasis on sustainability. This involves not only mitigating direct environmental impacts but also embracing broader responsibilities in the social and governance dimensions. These three interconnected areas—Environmental, Social, and Governance (ESG)—are recognized as the main pillars of sustainable development [1,2,3,4]. While today ESG factors are clearly a reputational factor, it is estimated that in a few years, they will become a strict business parameter influencing every company. One of the key internal processes where businesses implement sustainable development principles is Customer Relationship Management (CRM). It utilizes data analysis about customers’ history with the company to improve business relationships, specifically focusing on customer retention and ultimately driving sales growth. Effective CRM is a prerequisite for successful business management, and supporting responsible customer relations is an important aspect of the social pillar of ESG [5,6,7].

In the current globalized market environment, it is essentially impossible for a business that does not pay any attention to CRM to be successful and competitive [8,9,10,11]. Many authors agree that within the CRM, it is appropriate to respect industry specifics because individual CRM models cannot always be fully implemented across different sectors [12,13,14]. For this reason, it is necessary for studies in the field of CRM to reflect industry specificities [15].

The primary objective of this study is to create and apply a model for the segmentation of corporate customers in the postal market in line with the principles of sustainable development. This model is designed for the segmentation of corporate customers based on their regular utilization of postal services.

Because combining all three ESG pillars into a single customer segmentation model is still a new idea (especially in regulated industries), this study takes an exploratory approach. Instead of testing existing theories, it focuses on creating and demonstrating a new tool that can guide strategy and resource use in complex service sectors, where traditional methods often fall short.

While the model is based on the Slovak postal market, it lays the groundwork for broader use in other regulated sectors like telecommunications or utilities. The insights gained could also help shape policy discussions around sustainability and support future international comparisons in customer management focused on ESG goals.

The text of this paper is structured as follows: Section 2 provides a comprehensive literature review on sustainability in CRM, customer segmentation, and the specifics of the postal services sector. Section 3 details the methodology employed for developing and applying the three-dimensional customer segmentation model. Section 4 presents the application of this model to the Slovak Post dataset. Section 5 introduces the implications of these findings, acknowledging methodological limitations and proposing avenues for future research. Finally, Section 6 concludes the paper by summarizing its main contributions and highlighting its theoretical and managerial relevance.

2. Literature Review

2.1. Issue of Sustainability in Relation to CRM

In today’s globalized marketplace, businesses must also pay attention to all aspects of their activities that contribute to the overall image of customer perception of the particular business. A satisfied customer is a prerequisite for guaranteeing success in most industries in which businesses operate [16,17,18]. One of these aspects is also the aspect of sustainable development, which has been gaining in importance over the last few years [19,20]. This is due, among other things, to the fact that issues related to the issue of sustainable development are being discussed more and more both within the professional as well as general public, i.e., the development that meets the needs of today’s generations without compromising the ability to meet future generations’ needs. Authors Verles et al. [21] state in their work that 81% of millennials (born in 1980–2000) believe that businesses play a key role in guaranteeing sustainable development. Based on this, it is clear that if the businesses want to have representatives of a generation of millenniums as their customers, they must consistently respect the principles of sustainable development. Newell states that respecting the principles of sustainable development in a business can attract new customer segments [22]. However, the organization has to responsibly communicate its engagement in sustainability and may present results achieved in this field by creating and publishing corporate social responsibility (CSR) reports or ESG reports [23]. Another authors [24,25] discuss issues of sustainable development in the context of Sustainable Competitive Advantage. According to these authors, in order to ensure a sustainable competitive advantage, it is necessary to collect information on customers and to implement CRM, then use this information to produce unique products and services and to create more customer interactions through multiple channels across the entire customer lifecycle [26]. Nowadays, these CRM systems can use new techniques such as Artificial Intelligence (AI), Computational Intelligence (CI), Machine Learning (ML), and Data Mining (DM), and facilitate operational interactions and provide sophisticated analytical support to help marketing and business teams understand customer needs and campaign effectiveness [11,27,28,29,30].

Current CRM research works agree that effective CRM is one that is based on an individual customer approach [31,32,33,34]. An inevitable prerequisite for an individual customer approach to the issue of CRM is a thorough customer segmentation. Despite this imperative, a comprehensive customer segmentation model that fully integrates all three pillars of sustainable development (Environmental, Social, and Governance) into its core criteria, particularly for regulated industries, remains an underexplored area in the existing CRM and sustainability literature. This constitutes a critical research gap, as conventional segmentation models typically prioritize economic factors, failing to offer the holistic framework necessary for truly sustainable business practices. There are currently a number of segmentation models. The individual models differ mainly in the different segmentation criteria that are taken into account. Authors [35,36,37,38] in their works consistently describe a general customer segmentation model based on the four core pillars. This is also confirmed by the extensive literature review by [39]. The literature [35,36,37,38,40] consistently describes a general customer segmentation model based on four core pillars: Geographic, Demographic, Psychographic, and Behavioral. These pillars encompass a range of specific criteria: Geographic criteria relate to location, such as continent or city density; Demographic criteria include attributes like age, gender, and income; Psychographic criteria involve lifestyle, values, and attitudes; and Behavioral criteria focus on aspects like purchasing usage and loyalty. A comprehensive overview of these criteria is presented in

Table 1. General Customer Segmentation Model [35,36,37,38,40].

Basic Customer Segmentation Pillars
Geographic	Demographic	Psychographic	Behavioral
Continent	Age	Social status	Degree of loyalty
Country	Gender	Lifestyle	Purchasing usage
Country region	Family size	Personal values	Likes
City	Occupation	Attitudes	Mode of payment
Density	Income	Hobbies	Engagement
City area, etc.	Education	Concerns	Interests, etc.
	Nationality	Political views, etc.
	Language, etc.

.

Beyond these general frameworks, other segmentation models often focus on customer value, such as variants of the RFM (Recency, Frequency, Monetary) model, which classify customers based on past and future behavior to estimate customer lifetime value. However, these conventional approaches primarily focus on immediate economic benefits. They inherently fall short of achieving broader sustainability goals because they lack sufficient integration of social and environmental dimensions into their core criteria. They provide an incomplete view necessary for holistic, sustainable business practices.

However, these conventional segmentation models, while effective for general market understanding and tactical marketing, primarily focus on consumer characteristics and purchasing behavior without inherently integrating the multifaceted dimensions of sustainable development. They fall short in providing a complete view of a customer’s total impact or potential within a sustainability framework.

At present, most businesses typically use software or applications to manage their in-house processes, especially for time savings and human error elimination. In the context of customer segmentation activities, it is also possible to use modern software tools, for example, when categorizing information about customers or looking for closer links in this information [40,41,42,43,44,45]. Given the amount of data available to businesses on their customers, it is advisable not to limit themselves to one segmentation criterion in the segmentation process. There have been several customer segmentation models in the related literature, and these models often consider customer value, especially customer lifetime value. They can be classified into two groups: past customer behavior models (variants of RFM Model, RE-RFME, LRFM, PCV Model, SOW Model, etc.), and future-past customer behavior models, which take the future behavior of customers into consideration [40,45,46]. A very good overview of models and methods for segmentation can also be found in the work [47]. The model created by Turnbull and Zolkiewski, experts in marketing and management from the Manchester Business School, also appears in the literature. Their model lies in creating a three-dimensional matrix of customer classification [48,49]. The result is then a 3D segmentation model that classifies customers into eight quadrants (see Figure 1). To address this critical limitation and fill the identified research gap, this study introduces an advanced 3D segmentation model explicitly designed to align with the three pillars of sustainable development. A key advancement of our approach is the replacement or supplementation of Turnbull and Zolkiewski’s original criteria (relationship value, net price, and operating costs) with the criteria specifically selected to directly reflect the content of the three pillars of sustainable development. This innovative re-conceptualization provides a novel and comprehensive framework for sustainability-oriented customer management, particularly relevant for regulated sectors where a broader societal impact is expected.

The selection of the segmentation criteria to be taken into account within the segmentation should be dependent on the goals the business wants to achieve. These goals may vary from business to business, for example, gaining more market share, gaining a new market (generally achieving higher profitability). In addition, these may be objectives that are not primarily related to an effort to increase profits, such as increasing customer loyalty [48,50].

Many companies are currently confronted with the up-to-date challenge of setting up in-house processes to be consistent with sustainable development policies [51,52]. As stated in the introductory part of this chapter, the principles of sustainable development are categorized into three pillars: the social pillar, the environmental pillar, and the governance (economic) pillar (see Figure 2).

In the event that a business is interested in managing customer processes in accordance with sustainable development principles, it is appropriate to apply the 3D segmentation model within the framework of customer segmentation, following the defined principles of sustainable development. The 3D segmentation model will allow you to approach customer segmentation using three segmentation criteria that will reflect the basic principles (pillars) of sustainable development. By incorporating these criteria, the 3D segmentation model offers a practical means for businesses to manage customer processes in accordance with sustainable development principles, allowing for a strategic approach to customer segmentation that transcends purely economic considerations.

To strengthen the theoretical foundation of this study, the principles of the Triple Bottom Line (TBL) framework—focusing on environmental, social, and economic dimensions—are integrated into the proposed model. In addition, Stakeholder Theory provides a useful lens through which customer segmentation can be aligned with broader stakeholder accountability, especially in regulated sectors. These frameworks help reframe CRM not only as a profit-driven tool but as a mechanism for sustainable value exchange among all parties involved.

Despite the growing body of literature linking CRM and sustainability, a comprehensive segmentation model that explicitly incorporates all three TBL pillars remains rare. This gap is particularly evident in regulated and semi-public service sectors, where organizations must balance financial performance with societal and environmental responsibilities. The proposed model addresses this gap by embedding sustainability criteria directly into segmentation logic.

2.2. Customer Segmentation for Postal Services

In the context of postal services—a sector marked by its public-service obligations and regulatory constraints—segmentation strategies face unique challenges. Unlike commercial sectors, postal operators must account not only for market dynamics but also for policy directives, infrastructural legacies, and social equity considerations. These factors complicate traditional segmentation and highlight the need for a systems-based model that can manage interdependencies and advance sustainability objectives.

Customer segmentation is a very significant strategy for organizations, and the significance is particularly felt when the companies operate in highly competitive industries. The postal operators can break down customer groups based on their preferences, needs, consumption, behavior, and other attributes to fashion the services more suitably towards the needs of a particular segment. This customization not only adds more value to the customers but also boosts the market position of the company [40]. Use of customer segmentation for postal services possesses several key benefits:

• Improved customer satisfaction and loyalty—Increased understanding of diverse customer needs enables postal operators to offer more tailored solutions, thereby improving satisfaction, inducing repeat business, and creating positive word of mouth.
• Better financial results—By concentrating on segmentation, companies are in a position to recognize new cross-selling opportunities and direct their efforts toward high-value client segments, maximizing earnings and reducing expenses [54].
• More differentiation in competition—By strong identification of their customers’ needs, postal corporations have been able to create special offerings and revive their brand name, thus differentiating themselves from their competitors.
• Improved resource utilization through segmentation, postal operators can focus on high-value segments and utilize their efforts where they would have the greatest effect, continuously refining their methodology according to results.

One of the key steps for any enterprise in the postal industry is to identify and categorize its target customers. There are several methods and techniques available to conduct customer segmentation in the postal industry, with some of the most common ones already discussed in Section 2.1. Recent empirical research, such as the customer segmentation model for postal services based on customer requirements developed by Zhang et al., 2022 [55], further highlights the practical application and benefits of tailored segmentation approaches in this sector.

Customer segmentation is a very important business strategy for postal businesses since it allows them to target and identify the most profitable and loyal customers and to tailor their services and products to meet their individual needs and desires. Customer segmentation is never a one-size-fits-all approach, and there are many factors that need to be considered whenever customers in the postal industry are being segmented [47,56].

Recent research continues to highlight the importance of systems thinking for managing complexity in organizational and service contexts [7,30]. A systems approach enables integration of social, environmental, and economic factors when designing and evaluating customer segmentation models. In the context of public services such as postal markets, this perspective supports a holistic understanding of how various segmentation criteria interact and contribute to sustainable outcomes. By applying systems principles to customer segmentation, organizations can better manage interdependencies and feedback loops that impact both operational efficiency and long-term sustainability. This systematic integration of sustainability pillars into the segmentation framework, viewed through a systems lens, represents a key theoretical and practical contribution of this study, particularly for regulated industries where the postal market functions as a complex socio-technical system.

While several empirical studies have explored segmentation in logistics or e-commerce, comparative insights across regulated service sectors such as telecommunications, utilities, or public transportation are largely absent. This paper attempts to bridge that gap by examining how segmentation logic in the postal sector—characterized by both public service and commercial imperatives—can serve as a blueprint for sustainability-oriented customer management in other complex service ecosystems.

2.3. Specifics of Access to Customer Segmentation in the Postal Services Sector in Slovakia

The market for postal services differs in many ways from other markets and sectors of industry. Compared to the ordinary market, specific regulatory levels are significant here, i.e., tariffs and quality requirements for the universal postal service. It belongs to the public services. Another specificity of the postal market is the high number of customers (corporate customers, state and public administration bodies, private persons, etc.), the number of competing companies, not only in the territory of the Slovak Republic but also in the multinational scope. In the postal market in the territory of the Slovak Republic, Slovak Post, plc. (hereinafter referred to as SP) has the largest market share, which is also a provider of the universal postal service. Currently, SP provides postal services to all customer segments. Where only the economic benefits of customers are taken into account, the current customer care is implemented within these segments [57]:

• Key customers—Of extraordinary importance in terms of revenue for relocation and procurement activities. This group includes multinational business companies, banks, insurance companies, and forwarding and energy companies. These are corporate customers.
• Big business customers—Posting regularly or extraordinarily. This category includes, in addition to trading companies, public administration, and state and local government institutions. These are corporate customers.
• Middle and small business customers—A high-potential customer group, especially legal persons who use post to communicate with their partners. These are corporate customers.
• Sole traders—Legal and natural persons who, through post, primarily provide standard communication with their partners and institutions without entering into a business relationship with SP. Individually, they do not make a big difference, but they have a significant share of revenue based on their number. There are customers, both corporate and non-corporate, in this segment of customers.
• Consumers—A group of persons (all inhabitants of the Slovak Republic) who use postal services for private communication or to communicate with other groups. These are not corporate customers.

The customer orientation of the SP is provided at several levels of the company in order to maintain a dominant position on the postal market. SP customers are currently segmented based on revenue share (see

Table 2. Revenue distribution by customer segments in Slovak Post in 2024. [57].

Segment
Key customers	41%
Big business customers	13%
Medium and small business customers	11%
Sole traders	10%
Consumers	25%
Total	100%

).

This revenue-based segmentation primarily serves to categorize customers by their economic contribution, informing general customer care approaches and resource allocation for market position maintenance, though it does not explicitly integrate comprehensive sustainability objectives. These traditional models inherently fall short of achieving broader sustainability goals because they lack sufficient integration of social and environmental dimensions into their core criteria. They provide an incomplete view necessary for holistic, sustainable business practices.

However, revenue-based segmentation in such regulated environments fails to capture the multidimensional value of customers, particularly their alignment with sustainability goals. Postal operators operate within a constrained policy and operational environment that demands not just economic efficiency, but also equitable access, environmental impact mitigation, and systemic integration. Recognizing these sectoral characteristics is essential for tailoring segmentation frameworks that go beyond traditional market logic.

Referring to the above-mentioned authors and their works, it would be appropriate for SP to respect the principles of sustainable business development in customer process management. Due to the fact that customer segmentation is one of the first CRM steps, it is appropriate for the process to respect the principles of sustainable development.

2.4. Theoretical Concept of Customer Segmentation in the Postal Market in Slovakia, Respecting the Principles of Sustainable Development

In order to integrate the principles of sustainable development into the customer segmentation process in the postal market, it is necessary to define segmentation criteria that will be in line with the defined principles of sustainable development (the three pillars). At the same time, it is necessary to select criteria that can be expressed by a certain indicator so that selected customers can be classified through these criteria.

To create a comprehensive segmentation model, this study adapts and extends the three-dimensional (3D) segmentation model proposed by Turnbull and Zolkiewski [48] (see Figure 1). Unlike their original model, which used relationship value, net price, and operating costs, our approach innovatively replaces or supplements these with criteria specifically chosen to align directly with the three pillars of sustainable development. This fundamental re-conceptualization is a core advancement, directly addressing the identified gap for a sustainability-oriented segmentation model. According to several authors, this model is suitable for the postal sector as well as for the telecommunications and ICT sectors [54,58].

The social pillar: The activities of this pillar lie in balancing inequalities between individual social groups or individuals. In order for differences to be balanced, it is necessary to classify individual groups or individuals [59,60]. As individuals in the postal market, specific customers of businesses undertaking operations in the postal market can be marked. For the needs of the classification of individual customers, it is possible to select the criterion “Customer Development Potential”. This criterion directly addresses the social pillar’s objective of balancing inequalities among social groups and individuals. By classifying customers based on their growth and development potential, the model facilitates targeted strategies to support diverse customer groups, thereby fostering equity and balanced market development.

The environmental pillar: It is based on the assumption that limited growth is not possible in a limited system [61]. For this reason, it is necessary to respect the fact that there is a limited amount of natural resources. The fundamental prerequisite for the environmental pillar is the protection of biodiversity in all of its forms and aspects, together with the protection of the environment [62]. Transport is undoubtedly an integral part of the functioning postal market, but it also has a great impact on the environment. For this reason, the criterion “Customer Operating Costs” is an appropriate segmentation criterion. Selecting ‘Customer Operating Costs’ as a criterion directly reflects the environmental pillar’s emphasis on resource efficiency and minimizing ecological impact. In the postal sector, these costs serve as a proxy for transport-related emissions and overall resource consumption associated with servicing customers. Utilizing this criterion allows for the classification of customers based on the environmental footprint required to meet their service needs.

The economic pillar: This pillar is based on the need to express the value of natural resources consumed by companies for the production of goods or the provision of services. Only on the basis of the knowledge of the value of these resources is it possible to take measures to ensure the preservation of the maximum possible quality of the environment for future generations [63]. In terms of segmentation of customers in the postal market, it is a necessity to assess individual customers. Many studies suggest that retaining an existing customer is more than ten times cheaper than gaining a new customer [64,65]. By respecting the given paradigm, therefore, the selection criterion “Customer Relationship Value”, which is determined by the length of the relationship with the customer, is an appropriate segmentation criterion in the given area. This criterion fundamentally embodies the economic pillar’s focus on long-term value and operational efficiency, recognizing that retaining existing customers is significantly more cost-effective (over ten times cheaper) and contributes more sustainably to profitability than continually acquiring new ones.

The above-mentioned facts are also related to the objective of this paper, namely, to create a model of segmentation of corporate customers in conditions of the postal market that is in line with the principles of sustainable development. The segmentation model is intended for segmentation of corporate customers on the grounds that these customers have concluded a service contract with companies operating in the postal market, which makes it clear that these corporate customers utilize postal services very often. For postal service providers, it is much more effective to customize customer service for corporate customers than for customers who use services only to a very limited extent. The segmentation model of corporate customers will be demonstrated in the conditions of SP, with the possibility of its subsequent use in other companies operating in the postal market. The benefit of the proposed model is particularly noticeable from the point of view of its use, specifically as a strategic tool that is part of a complex CRM in the postal market, providing a holistic framework for sustainable customer management and strategic decision-making in complex environments.

3. Methodology

This section presents the methodology for developing and applying a three-dimensional (3D) customer segmentation model from a systems perspective. The proposed approach integrates social, environmental, and economic criteria, reflecting the pillars of sustainable development within regulated service sectors such as the postal market. The methodology is designed to be adaptable to various contexts and organizations. To demonstrate the model’s practical application, the approach is illustrated through a case study involving the universal postal service provider in Slovakia. The segmentation approach presented in this paper is of an exploratory nature, as it aims to identify structural patterns among customers without relying on predefined hypotheses. This decision is based on the absence of previously validated ESG-based segmentation models in regulated postal services. The model was constructed to discover potential groupings of customers according to sustainability criteria, offering a foundation for future confirmatory research and model refinement in similar service contexts.

A modified three-dimensional customer classification matrix will be applied in order to create a 3D segmentation model for corporate customers. Within the model, three segmentation criteria reflecting the content definition of the three pillars of sustainable development will be respected, namely, the Customer Development Potential, Customer Operating Costs (Cost-to-serve), and Customer Relationship Value (see Figure 3).

From Figure 3, it is clear that the theoretical model will allow customers to be classified in up to eight quadrants (Q), which are further specified in

Table 3. Quadrant values specification of the 3D theoretical model of the corporate customer segmentation (Source: Authors).

Quadrant	Customer Development Potential (X)		Customer Operating Costs (Y)		Customer Relationship Value (Z)
Q1	low	Q1.1	Low	Q1.2	low	Q1.3
Q2	low	Q2.1	High	Q2.2	low	Q2.3
Q3	low	Q3.1	Low	Q3.2	low	Q3.3
Q4	low	Q4.1	High	Q4.2	low	Q4.3
Q5	high	Q5.1	Low	Q5.2	low	Q5.3
Q6	high	Q6.1	High	Q6.2	low	Q6.3
Q7 (!)	high	Q7.1	Low	Q7.2	high	Q7.3
Q8	high	Q8.1	High	Q8.2	high	Q8.3

.

In the Q7 (marked by “!”) quadrant, there is a key segment of corporate customers, i.e., customers with high development potential, low operating costs, and a high relationship value. In general, it can be argued that businesses in the postal market should try to take measures that will lead to an increase in the number of customers that can be identified in the given quadrant.

In the next step, it is necessary to precisely specify individual segmentation criteria. Segmentation criteria must be specified to allow individual customers to be classified. The criteria can be expressed both quantitatively and qualitatively. In the case of a qualitatively expressed criterion, it is necessary to arrange its conversion to the selected numerical scale.

Customer Development Potential (the social pillar): This criterion is difficult to express using internal or other data of a quantitative nature. It is a criterion that can be expressed through the experience of business executives in relation to the sector in which their customer performs his/her activity. This criterion is quantified on a scale of 1–4, with 1—low potential of the customer development with respect to the market in which he/she operates, and 4—high customer development potential with respect to the market in which he/she operates.

Although the segmentation criterion of Customer Development Potential relies on expert judgment, it was applied systematically. The evaluation was based on three practical indicators: the customer’s activity trend in postal transactions, the scope and frequency of service modifications, and their sectoral role or influence. To minimize subjectivity, each customer was independently evaluated by two experienced managers. Where discrepancies occurred, consensus was reached through a structured comparison of rationales. While no statistical inter-rater reliability measure was calculated, the consistency of the evaluation process was supported by shared internal guidelines and prior alignment on interpretation.

Customer Operating Costs (the environmental pillar): This criterion must be specified so that it takes into account all the costs incurred in a company in connection with servicing the corporate customers. The cost of operating the corporate customers is as follows:

• The number of visits to the customer per unit of time (year);
• The distance of the company from the customer’s home (km).

The ratio of the individual cost items is set to 0.7 (the number of visits to the customer per unit of time) and 0.3 (the distance of the company from the customer’s home). The choice of weights (0.7 for the number of visits and 0.3 for the distance) was determined in consultation with senior managers of Slovak Post who are directly responsible for operational budgeting and logistics planning. Their assessment reflects the operational reality that visit frequency typically accounts for a greater share of variable service costs than geographic distance. Although these values are based on expert estimates rather than empirical optimization, they provide a transparent and operationally justified foundation for the model. The weights may be fine-tuned in future applications, depending on the availability of more detailed cost-tracking data or through sensitivity analysis. The final formula for quantification of corporate customers for a given criterion takes the form:

$$\mathrm{o}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{c}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{s}=0.7\times number of visits+0.3\times distance of the company$$

(1)

Both cost items of the Formula (1) can be accurately quantified based on available internal data. This ratio of cost items was determined on the basis of an expert estimate of the SP’s financial manager.

Customer Relationship Value (the economic pillar): This criterion can be expressed through the internal data. Specifically, these are data that indicate the length of the customer relationship in years. The longer the customer relationship lasts, the higher its value. The given criterion may take values of $⟨0,\left.\infty \right)$.

By putting individual customers (objects) that reflect the values of the selected segmentation criteria (X, Y, Z) (variables) into the 3D matrix, we create clusters. Referring to the theoretical model of the 3D matrix described above, it is possible to identify eight clusters consisting of different objects. Individual objects are more or less distant from each other in the 3D matrix. The distance of the single objects is given by the differences in the values of the selected variables.

In order to calculate the distance of the objects in the 3D matrix, one needs to know the type of variables through which the objects have been classified.

The variables “Customer Operating Costs” and “Customer Relationship Value” are interval variables. Interval variables take up a large number of numerical values on a linear scale, for which two values can be calculated by how much one is greater (or smaller) than the other. The distance between objects and the interval type variable is calculated according to the following measures:

The most frequent measure of interval variables is the Euclidean distance:

$$D\left(x_i,x_j\right)=\sqrt{{\displaystyle \sum _{l=1}^d{\left|x_{il}-x_{jl}\right|}^2}}$$

(2)

It is also possible to use the Manhattan distance (city block distance):

$$D\left(x_i,x_j\right)=\sqrt{{\displaystyle \sum _{l=1}^d\left|x_{il}-x_{jl}\right|}}$$

(3)

Both of these distances can be converted to a general shape known as Minkowski distance:

$$D\left(x_i,x_j\right)={\left({\displaystyle \sum _{l=1}^d{\left|x_{il}-x_{jl}\right|}^p}\right)}^{{\displaystyle \frac{1}{p}}}$$

(4)

where

d … indicates the number of the variables of the x_k object.

Note: By entering a number 1 in a parameter in the Minkowski distance, we get the Manhattan distance. The same applies to the Euclidean distance after entering the number 2.

The “Customer Development Potential” variable is an ordinal variable. An ordinal variable gets a finite and small number of discrete values over which an arrangement can be created (e.g., small, middle, large—this list can then be replaced by values … 1 … M_l). For the x_i object, then the value of the variable in the position l is denominated as r_il. These values are then recalculated into the interval $〈0;1〉$ using the formula:

$$z_{il}={\displaystyle \frac{r_{il}-1}{M_l-1}}$$

(5)

The distance of the objects is then calculated by any distance function, e.g., by the Euclidean distance (Formula (2)).

Although no formal assumption testing was conducted, the segmentation criteria were selected to represent distinct operational dimensions. In future applications, it would be appropriate to examine the distributional properties of interval variables and check for possible multicollinearity. This would enhance the model’s empirical robustness and support replication across broader datasets.

The calculation of the distance of individual objects according to the formulas described above can be performed through selected analytical software, for example, through the STATISTICA analytical software (version 14).

Due to the fact that clusters are identified through eight defined quadrants of the 3D model, cluster validation is not required. In this case, traditional cluster validation techniques such as silhouette scores or statistical fit indices were not applied, as the segmentation does not result from algorithmic clustering but rather from a structured quadrant-based allocation rooted in a theoretical 3D framework. This framework defines explicit cut-off points along the axes, corresponding to the three ESG-aligned segmentation criteria. Nevertheless, descriptive cross-checks were carried out to verify the internal consistency of customer distribution across quadrants, which supports the practical robustness of the model. The segmentation approach applied does not utilize clustering algorithms such as k-means or hierarchical methods. Instead, the structure is based on a theoretically defined three-dimensional matrix, where quadrant boundaries reflect operationally meaningful cut-off values for each criterion. These boundaries were determined in cooperation with SP management based on experience and internal data trends. Although statistical validation techniques such as silhouette scores or distance-based cluster optimization are not applicable to this model structure, the predefined matrix ensures interpretability and practical relevance for managerial decision-making.

The segmentation criteria used in this study were evaluated for their independence and practical differentiation. Informal tests indicated that the three dimensions—development potential, operating costs, and relationship value—represent distinct characteristics and are not collinear. Since the model does not rely on parametric inference or prediction, formal testing for multivariate assumptions such as normality or homoscedasticity was not essential for the application of the quadrant model. Nonetheless, the authors acknowledge that such diagnostics may be required in future extensions where statistical generalization or predictive modeling is intended. The 3D segmentation model will be demonstrated on a sample of 187 SP corporate customers. Specifically, these are corporate customers (key customers, big business customers, medium and small business customers) located in the eastern part of Slovakia. These are the three customer segments of the corporate customers that account for 65% of total SP revenues.

Table 4. List of Towns where Corporate Customers Are Located (Source: Authors).

Bardejov	Lipany	Stará Ľubovňa
Čierna nad Tisou	Medzilaborce	Strážske
Dobšiná	Michalovce	Stropkov
Gelnica	Moldava nad Bodvou	Svidník
Gemerská Hôrka	Prešov	Šarišské Michaľany
Humenné	Rožňava	Trebišov
Košice	Sabinov	Turňa nad Bodvou
Kráľovský Chlmec	Snina	Vranov nad Topľou
Krompachy	Sobrance
Levoča	Spišská Nová Ves

lists the towns where the 187 SP corporate customers are located, through whom the 3D segmentation model will be demonstrated. The sample of 187 corporate customers includes clients from three principal customer segments—key customers, large business customers, and small and medium-sized enterprises—based in the eastern region of Slovakia. This region was selected based on the availability of complete CRM and operational data and includes entities representing approximately 65% of corporate revenue for Slovak Post. Although the sampling was not probabilistic, the business relevance and financial weight of the selected customer base provide a sound basis for initial model testing and allow for operational insights into how the segmentation model can be applied in practice.

Although the study focuses on customers from the eastern region of Slovakia, the segmentation logic was designed with broader applicability in mind. The criteria and classification matrix can be replicated across other regions or sectors with similar service structures. While the current sample was selected due to data completeness, future research may involve a more extensive dataset at the national level, which would allow for additional robustness checks and comparative analysis across postal regions or even other regulated industries.

As the quadrant-based segmentation logic prioritizes managerial interpretability over statistical abstraction, its strength lies in strategic applicability. Future studies applying the model to other sectors or broader customer datasets may consider complementing this structure with statistical validation techniques or comparative clustering approaches to reinforce its generalizability.

4. Results

The following results illustrate the application of the proposed three-dimensional customer segmentation model from a systems perspective. While the model is designed to be broadly applicable to organizations operating in regulated service sectors, its effectiveness and practical insights are demonstrated here using data from the Slovak universal postal service provider. This case study serves to validate the model’s potential for supporting sustainable development and customer management strategies in similar systemic contexts.

Table 5. Segmentation criteria classified into quadrants of the 3D systems model (Source: Authors).

Quadrant	Customer Development Potential (X)		Customer Operating Costs (Y)		Customer Relationship Value (Z)
Q1	1–2.5	Q1.1	0–390	Q1.2	0–11	Q1.3
Q2	1–2.5	Q2.1	390.01–779	Q2.2	0–11	Q2.3
Q3	1–2.5	Q3.1	0–390	Q3.2	12–22	Q3.3
Q4	1–2.5	Q4.1	390.01–779	Q4.2	12–22	Q4.3
Q5	2.51–4	Q5.1	0–390	Q5.2	0–11	Q5.3
Q6	2.51–4	Q6.1	390.01–779	Q6.2	0–11	Q6.3
Q7 (!)	2.51–4	Q7.1	0–390	Q7.2	12–22	Q7.3
Q8	2.51–4	Q8.1	390.01–779	Q8.2	12–22	Q8.3

lists the classification scales of the segmentation criteria (variables) for individual quadrants. The key Q7 quadrant is made up of corporate customers who are classified for the X variable with values in the 2.51–4 interval, at the same time, for the Y variable, there are values in the 0–390 interval, and at the same time for the Z variable values are in the interval of 12–22. The individual corporate customers were rated according to the criteria that are expressed using the X, Y, and Z variables.

The segmentation thresholds applied in Table 5 were derived from empirical distributions and practical consultations with SP’s CRM specialists. Each criterion was divided into two operationally meaningful intervals (low and high) based on internal benchmarks and observed customer performance patterns. This ensured that quadrant allocation was not only analytically consistent but also reflective of how postal operators interpret customer value, effort, and future potential in real conditions.

To confirm the internal coherence of the segmentation outcomes, the classification results were examined with respect to each axis individually. The variable distributions correspond well with the defined quadrant intervals and reflect expected real-world distinctions across the SP customer portfolio. This supports the logical structure of the segmentation framework and indicates that the applied model is sufficiently sensitive to differentiate customers in alignment with their actual behavioral and operational profiles.

Table 6. Distribution of corporate customers across variables in the 3D systems-based model (Variables) (Source: Authors).

	Range of Values of Variables		The Value Interval on the Axis for a Given Quadrant; the Number of Identified Corporate Customers in Each Quadrant out of a Total of 187 Corporate Customers; Share of Corporate Customers in Each Quadrant in (%)
Variables	Min.	Max.	Quadrant of low values	Quadrant of high values
Customer Development Potential (X)	1	4	1–2.5; 112/187; 60%	2.51–4; 75/187; 40%
Customer Operating Costs (Y)	0	779	0–390; 151/187; 81%	390.01–779; 36/187; 19%
Customer Relationship Value (Z)	0	22	0–11; 91/187; 49%	12–22; 96/187; 51%

provides us with information on the total range of values that individual variables can acquire. For the X variable, it is a range of values 1–4, for the Y variable, it is a range of values 0–779, and for the Z variable, it is a range of values 0–22. The values of the X, Y, and Z variables were then divided into two halves—a quadrant of low values of the variables and a quadrant of high values of the variables. Further, the table above provides information on the total number of corporate customers who were identified in the individual quadrants of the X, Y, and Z variables. For the X variable, 112 corporate customers out of a total of 187 were identified in the low-value quadrant, in the high-value quadrant there were 75 corporate customers identified out of a total of 187. For the Y variable, 151 corporate customers out of a total of 187 were identified in the low-value quadrant, and in the high-value quadrant, there were 36 corporate customers out of a total of 187. For the Z variable, in the low-value quadrant, there were 91 corporate customers identified out of a total of 187, and in the high-value quadrant, there were 96 corporate customers out of a total of 187. The number of corporate customers identified in the individual quadrants is subsequently expressed in percentages.

Figure 4 demonstrates individual objects (corporate customers) whose mutual distance is calculated using Formulas (2)–(5). The 3D model of the SP corporate customers’ segmentation is the output of modelling in the STATISTICA analytical software. The identification of the individual customers has been implemented with respect to the eight defined quadrants. Figure 4 highlights quadrant Q7 (in red color), in which 31 corporate customers out of 187 were identified. These 31 corporate customers form one of the eight identified clusters.

It is clear from Figure 5 that only 17% of corporate customers out of a total of 187 were identified in the Q7 key quadrant. This quadrant is characterized by high development potential, low operating costs, and high value of the relationship. The Q7 key segment consists, in particular, of 31 corporate customers, out of which the following was found:

• The average value of the X variable is 3.42 points out of 4 possible (value 4 is the ideal state).
• The average value of the Y variable is 75.25 points out of 779 possible (value 0 is the ideal state).
• The average value of the Z variable is 18.10 points out of 22 possible (value 22 is the ideal state).

Figure 5. Customer distribution by quadrant in the systems-based segmentation matrix (Source: Authors).

Figure 5. Customer distribution by quadrant in the systems-based segmentation matrix (Source: Authors).

The relatively low share of Q7 customers (17%) indicates that while the model successfully identifies high-potential, low-cost, long-term partners, these customers form only a limited portion of the existing portfolio. From a strategic management perspective, this insight underscores the opportunity to increase this group by targeting selected customers in adjacent quadrants (particularly Q5 and Q3) for development and retention interventions. These efforts can be supported by aligning marketing, logistics, and contract customization to gradually transition suitable customers into the optimal Q7 profile.

From a systems perspective, quadrant Q7 represents a state of balance between organizational effort and strategic return. The presence of customers in this quadrant reflects effective alignment of SP’s service model with clients who offer long-term relationship value without excessive resource demands. However, quadrants such as Q5 or Q3 suggest partial alignment where either potential is high, but relationship depth is underdeveloped (Q5), or relationship value exists despite low development outlook (Q3). Understanding these structural imbalances allows managers to tailor interventions not only by customer type, but also by expected sustainability return.

In addition to static interpretation, the segmentation model also allows for dynamic tracking of customer movement across quadrants over time. For instance, customers currently located in Q3 or Q5 may shift into Q7 through improved contract performance, digital adoption, or cost optimization. Recognizing these trajectories can inform customer development programs and allow SP to proactively manage its corporate client base with sustainability-aligned targets in mind.

The largest share of the corporate customers, i.e., 28%, was identified in the Q3 quadrant. The Q3 quadrant includes customers with low development potential, low operating costs, and high relationship value. Twenty-one percent of the corporate customers out of a total of 187 were identified in the Q1 quadrant. The Q1 quadrant is a quadrant with low customer development potential, low customer operating costs, and low customer relationship value.

Based on the 3D segmentation model of the corporate customers, it can be stated that a significant part of SP corporate customers have been identified in other quadrants rather than in the Q7 key quadrant.

From an operational viewpoint, a more favorable distribution would exhibit a greater concentration in Q7, as this quadrant combines cost efficiency, long-term relationship strength, and development outlook. The current state, where Q3 and Q1 account for nearly half of the customers, suggests that a significant portion of the base remains underleveraged in terms of strategic potential. This result calls for closer examination of service offerings, pricing structures, and contact policies for these quadrants to identify pathways toward improving customer quality in ESG terms. For example, customers in Q3 may be considered “stable but static, cost-effective, and loyal”, but unlikely to expand without external stimuli. These segments may still contribute positively to environmental and economic objectives, but offer limited social development value. By contrast, Q5 customers might represent untapped growth with weak relational foundations, where strategic engagement could improve both customer loyalty and long-term profitability. This type of cross-dimensional insight is critical for managing constrained public service resources in line with evolving sustainability mandates.

Furthermore, each quadrant can be interpreted through the lens of the three sustainability pillars. For example, Q7 embodies alignment with all three: economic (high relationship value), environmental (low cost-to-serve), and social (strong development potential). On the other hand, Q1 lacks performance in all dimensions and may require a reassessment of value delivery or even contract rationalization. This ESG-based lens gives postal managers an additional criterion when prioritizing service redesign or investment allocation across customer groups.

From a systems perspective, this distribution reflects the underlying dynamics and interdependencies within the postal service’s customer base. The model’s multidimensional structure enables identification of systemic leverage points—areas where targeted interventions can optimize overall system performance, enhance sustainability, and support strategic management decisions in complex service environments. The results demonstrate that the segmentation model does not merely classify customers; it reveals the structural logic of the service network and its sustainability posture. By interpreting quadrant composition through both operational and systemic lenses, postal operators gain the ability to target meaningful shifts in portfolio quality. In this context, the quadrant matrix functions not just as an analytical framework, but as a decision-support tool for long-term transformation and adaptation in regulated service ecosystems.

5. Discussion

Interpreting the results through a systems perspective provides additional insights into the broader implications of the customer segmentation model for sustainable management in complex service organizations.

In order to guarantee the maximum degree of objectivity of the presented results, it is necessary to discuss the limitations that can be perceived within the 3D methodology of the corporate customer segmentation model used. These partial steps of the procedure will be discussed in the first part of this chapter. Due to the fact that the issue of segmentation of the customers in the postal market has not yet been given enough attention within the scientific public, the second part of this chapter will also discuss possible measures that could be implemented in connection with the presented results in the SP conditions. Specifically, these will be the measures, the implementation of which under the SP conditions could subsequently ensure that more corporate customers will be identified in the key quadrant. This part of the discussion can also be seen as an attempt by the authors to define areas of further research that will be built on the results of this paper.

5.1. Limitations of the Methodology for Creating a 3D Segmentation Model of Corporate Customers Respecting the Principles of Sustainable Development

To create the 3D segmentation model, three segmentation criteria were selected to reflect the content of the three pillars of sustainable development. The segmentation criteria (variables) were also selected with respect to the need to quantify corporate customers via these criteria. Selected segmentation criteria in individual areas do not correspond to any of the commonly used segmentation criteria, which can be seen as a limiting factor in terms of the perceived relevance of the results of the paper. On the other hand, the selected segmentation criteria are consolidated with the actual work of authors dealing with the issue of sustainable development in the context of individual principles of sustainable development. The “Customer Development Potential” segmentation criterion, which represents the social pillar, is a criterion that could not be expressed using analytical data. The analytical data refers to the data that would be derived, for example, from financial statements, economic indicators, or other in-house indicators. This criterion was expressed through the experience of business executives in relation to the sector in which their customer does business. This fact can be perceived as a weak point within the research methodology, on the grounds that it is only a subjective expression of the value of a segmentation criterion. However, this fact can be justified in relation to the existence of a number of scientific works, which are based on, among other things, expert estimates or subjective evaluations of the employees of the businesses [66,67,68].

The “Operating costs” segmentation criterion representing the environmental pillar is expressed using data on the number of visits to the customer’s premises and the distance of the customer’s business. This data is available from the in-house information system. This is data that was not obtained using expert estimates. However, in an effort to take into account the share of the two cost items through which the segmentation criterion was expressed, it was necessary to use the expert estimate. Authors [69] discuss the inequality in the cost structure between transport companies in their works. The authors confirm the thesis in three cases. The authors analyze, on a case-by-case basis, what measures the businesses take when they perform their business activities under different competitive conditions. The authors conclude that the responses of the companies, i.e., the implemented measures due to the given competitive environment, are in most cases reflections of the set corporate goals. These goals are, however, business-to-business dependent. Based on this fact, the authors cannot assume that all companies operating in the transport sector will have the same cost structure. Referring to the conclusions of [69], it can be assumed that the businesses operating in the postal market will not have the same cost structure, and therefore, when determining the share of each cost item, it is necessary for each of the cost items to be determined by a particular business. Based on this issue, the use of expert estimates for the determination of the share of individual cost items can be considered appropriate.

Further, it is also necessary to discuss the structure of the customer sample on which the 3D segmentation model was demonstrated. The resulting 3D segmentation model of SP customers was demonstrated on a sample of 187 corporate customers from the East Slovakia region, with no customers from the “sole traders” segment (see Table 2). The given segment was not included in the sample being surveyed because not all customers in that segment are corporate customers. The aim of the article is to create a 3D model of the segmentation of the corporate customers, and it would lead to an unnecessary decrease in the objectivity of the results if a part of a segment that also includes non-corporate customers were included in the research.

A certain limitation can also be seen from the perspective of the selection of the 3D segmentation model itself. Currently, there are several concepts in the customer segmentation approach, such as RFM segmentation, which consists of segmentation of customers based on when they recently purchased a product or service (Recency), how often they purchase (Frequency), and how much they have spent (Monetary value) [70,71]. The selected segmentation criteria respecting the individual pillars of sustainable development could also be confronted with the above-mentioned concept of RFM segmentation; however, based on the literature research, the authors decided to confront the selected segmentation criteria in relation to the three pillars of sustainable development. In particular, the RFM segmentation is used when a business is interested in improving its campaign targeting or increasing customer loyalty [72,73]. However, the aim of this article is to create such a model that would allow, for example, reducing customer operating costs, which would also have an impact on reducing the environmental impact. However, the business can achieve this through better organization of its own logistics processes than through marketing campaigns.

A certain limitation of the presented results lies in their descriptive form. Although the segmentation logic is structured along three quantitative dimensions, the outputs are not evaluated through algorithmic cluster analysis, but rather interpreted within a predefined quadrant matrix. This approach is consistent with the aim of the paper, which is not to explore latent groupings but to demonstrate a 3D model that facilitates the classification of corporate customers—especially those in the key Q7 quadrant. Understanding the number of customers in each quadrant enables the application of tailored CRM strategies. For example, if most customers fall into Q2, the recommended strategic approach will differ from one applied when the majority are found in Q5. From this perspective, descriptive interpretation is not only justified, but essential for strategy design in a constrained service environment.

It should also be stated here that the paper was prepared as a case study of Slovakia, to be precise, Slovak Post, plc. This fact justifies some steps within the selected methodology in the creation of the 3D segmentation model.

While the segmentation model was demonstrated on a specific regional case within Slovak Post, the conceptual logic is generalizable. The structure of the three-dimensional matrix, rooted in the ESG pillars, offers a scalable approach for other regulated service providers with similar constraints—such as utility companies, public transit agencies, or telecommunication operators. The quadrant-based results provide a replicable means to assess customer value and sustainability contribution simultaneously, without reliance on marketing-centric frameworks like RFM. This strengthens the model’s potential for informing broader organizational strategies in diverse public service ecosystems.

5.2. Discussion on Possible Areas of Further Research

In the Q7 key segment, 17% of the SP corporate customers were identified. This result indicates potential for designing and implementing measures aimed at increasing the number of corporate customers identified in the given segment.

In the scope of the “Customer Development Potential” segmentation criterion, there is an attempt to move as many corporate customers as possible from the low-value quadrants (Q1.1, Q2.1, Q3.1, Q4.1) to the quadrant of high values (Q5.1, Q6.1, Q7.1, Q8.1). In the low-value quadrant, corporate customers who are active in areas such as public and state administration, self-governing bodies, executors, judiciary and prosecution, construction, and health care have been identified. Increasing the development potential in these areas is very difficult to achieve because the implementation of possible measures requires a change in the legislation in the postal market. The current legislation in the postal market does not allow SP to proceed with the offer of products or services that the customers operating in the above-mentioned areas would like to use. In terms of this segmentation criterion, SP currently has only very limited possibilities to implement measures that could then move corporate customers from the Q1.1, Q2.1, Q3.1, and Q4.1 quadrants to the Q5.1, Q6.1, Q7.1, and Q8.1 quadrants.

In the “Customer Operating Costs” segmentation criterion, there is an attempt to move corporate customers from the Q2.2, Q4.2, Q6.2, and Q8.2 quadrants (quadrants of high values of the segmentation criterion) into the Q1.2, Q3.2, Q5.2, and Q7.2 quadrants (quadrants of low values of the segmentation criterion). In terms of this segmentation criterion, SP has many more opportunities to implement measures that will lead to corporate customers moving to the required quadrants. In addition, these are measures that are not tied to legislative changes in the postal market. The reduction in operating costs can be accomplished through several ways of optimizing personal meetings of the sales managers:

• Stating/limiting the number of personal visits at the customer’s premises on a monthly, quarterly, or annual basis;
• Establishing a different number of personal visits of the customers near the headquarters of sales managers (Košice, Prešov) and customers at a greater distance;
• Stating minimum/no number of personal visits of those customers in the public and state administration segment, self-governing bodies, executors, judiciary and prosecution, construction and health care, i.e., in the low development potential segment;
• Distributing sales managers to other locations in the Eastern Slovakia region where the concentration of customers is greater, in this case: Humenné, Košice, Michalovce, Prešov, Rožňava, Spišská Nová Ves.

However, some of the above measures may be considered as measures that could negatively affect the customer relationship value. In this context, for example, it is a measure of stating or limiting the number of personal visits of the customers per month, quarter, or year. A measure that does not have a negative impact on the value of the relationship with a customer, and at the same time will be a measure that SP is able to implement itself without the necessity of changing the legislation in the postal market or the cooperation of other entities, is the measure consisting of the distribution of sales managers to other places in the Eastern Slovakia where customer concentration is greater. In this case: Humenné, Košice, Michalovce, Prešov, Rožňava, and Spišská Nová Ves. At present, the customers are served by the sales managers from the headquarters in Košice and the headquarters in Prešov (see Figure 6).

The effect of the distribution of sales managers to other towns such as Humenné, Michalovce, Rožňava, and Spišská Nová Ves may lead to a reduction in operating costs, which will result in a larger number of corporate customers moving to the Q1.2, Q3.2, Q5.2, and Q7.2 quadrants. In this context, it is possible to define the area of further research, which is to solve the problem of the allocation of six service centers. Within such research, it is possible to apply a network allocation analysis model (p-median allocation model).

Another promising direction for future research lies in tracking quadrant transitions over time. As customer behavior, regulation, or service models evolve, the relative positioning of customers may shift. Mapping these dynamics longitudinally could provide critical insight into the effectiveness of strategic interventions. In addition, future research could investigate how customer clusters in Q3 or Q5 might be nurtured toward Q7, using CRM indicators, behavioral analytics, or even predictive modeling techniques tailored to regulated markets.

Although the results presented are based on a single-case analysis, they provide a strong foundation for more detailed quantitative follow-up studies. Future research could focus on testing the validity of the quadrant model across larger and more diverse datasets, ideally covering other national regions or regulated industries such as energy, telecommunications, or waste management. Comparative testing would allow researchers to evaluate whether the defined segmentation thresholds retain predictive value across different customer structures or organizational models.

Moreover, future research should also investigate the dynamic impact of external variables—such as evolving regulatory frameworks, rapid digitalization trends across industries, or significant shifts in the broader economic climate—on the model’s long-term applicability and the stability of customer segments. Understanding these external influences will be crucial for maintaining the model’s relevance and strategic utility over time. To enhance the accessibility and practical utility of the segmentation model for a broader audience, including non-technical managers, future studies could focus on developing more intuitive visualizations of customer profiles per quadrant. Practical illustrations, such as composite ‘personas’ for each segment, would also improve the immediate interpretability and applicability of the findings, fostering a deeper understanding of the customer base’s strategic implications.

In this context, further research could also explore how individual quadrants function under different regulatory or operational conditions. For instance, Q3 and Q5 reflect distinct forms of partial alignment—where one axis aligns with sustainability goals, while others lag behind. Understanding these differences can help target more precise interventions. In Q3, the organization may face high relationship value but low development outlook, which suggests stability without strategic growth. In contrast, Q5 may show promise for development but suffer from weak relational foundations. These internal trade-offs are critical to grasp when allocating limited resources in systems constrained by policy or infrastructure.

From a systems perspective, the multidimensional segmentation model demonstrates how organizations can manage complexity by identifying key leverage points—such as the allocation of customers to specific quadrants—to optimize both sustainability and performance. This systems-based approach moves beyond traditional segmentation by considering the interactions and feedback loops among social, environmental, and economic factors. While this study demonstrates the methodology using data from the Slovak postal sector, the model is broadly adaptable and can be applied to other regulated service systems seeking to enhance resilience and strategic decision-making. Future research could explore further system-wide optimization, for example, through advanced network allocation models or dynamic simulation approaches, extending the value of the systemic framework introduced here.

In addition, further refinement of the model could benefit from integrating time-based dynamics. For instance, by assessing how quickly or slowly customers shift between quadrants in response to specific interventions, it would be possible to measure not only segmentation status but also organizational responsiveness. The incorporation of time as a fourth dimension would further strengthen the model’s analytical power and allow public organizations to monitor systemic adaptation over time.

Expanding on this, the quadrant model can support organizational learning by highlighting how specific service interventions—such as cost rationalization, service bundling, or relationship strengthening—affect ESG balance across customer types. These patterns offer not only a snapshot of customer status but also a feedback mechanism for evaluating the impact of organizational strategies in complex environments.

The contribution of this model lies not only in segmenting customers based on sustainability-informed variables but also in offering actionable insight into transforming service structures. Unlike traditional segmentation tools focused solely on marketing metrics, this model captures the interaction between operational effort and stakeholder value in a complex system. For public service providers facing competing priorities, the model serves as both an analytical framework and a basis for decision-making that aligns short-term efficiency with long-term societal goals.

6. Conclusions

The paper deals with a very relevant issue that is at the center of interest of many companies applying CRM. The up-to-date nature of this issue is all the more binding when the principles of sustainable development are respected within the segmentation of customers (one of the primary CRM steps). The postal market is in most European member states a sector that is increasingly more or less regulated. The aim of this paper is to draw attention to the fact that, even under conditions of a partially regulated market, it is possible to approach customer segmentation as it is currently becoming more observable in several successful businesses operating in the unregulated market, namely, in order to ensure sustainable development. During the paper elaboration, a literary overview of the current trends of access to customer segmentation was presented, with respect to the current phenomenon, which is undoubtedly sustainable development. At the same time, in the introductory chapter, the specifics of the postal market were mentioned. The chapter also identified possible areas of segmentation criteria that are in line with the basic principles of sustainable development. The main output of the article is a 3D segmentation model tailored to the structure of SP’s corporate customer base. The generated model provides current information on the actual number of corporate customers in the key segment. The key segment is the segment in which there are customers with high development potential, low operating costs, and high relationship value. Application of the model to a sample of 187 SP corporate customers from the eastern part of Slovakia revealed that only a limited share of clients met all three strategic criteria and were classified in the key segment. This result was, among other issues, the subject of discussion, in which the area of further research consisting of the application of a network allocation analysis model (p-median allocation model) was defined. Research results help reduce customer operating costs while retaining the current number of contact with corporate customers. This reduction in operating costs contributes to shifting customers toward more favorable sub-quadrants (Q1.2, Q3.2, Q5.2, Q7.2), aligned with greater operational efficiency. It is also clear from the content of chapter 4.1 that the authors are fully aware of the possible limitations concerning the selected methodology in particular. Given the diversity of approaches currently used in CRM and customer segmentation, it is often very difficult to state that only one methodological concept is the right one in a given context. The established methodological concept also respected the fact that Slovak Post, plc., in the conditions in which the 3D segmentation model was presented, was interested in how to answer the question of how many corporate customers can be identified in the Q7 key quadrant, or in the remaining quadrants. Based on this knowledge, Slovak Post, plc. managed to implement certain measurements so that more corporate customers can be identified in the Q7 key quadrant. This granular understanding of segment distribution enables Slovak Post, plc. to strategically prioritize efforts, such as targeted resource allocation and differentiated service offerings, to shift customers towards the optimal Q7 quadrant or manage other segments more effectively in line with sustainability objectives. This provides a clearer framework for managerial action and investment sequencing across customer groups.

However, successful adaptation and full realization of the model’s benefits in real-world settings also depend on addressing potential operational and institutional barriers. These may include challenges in data integration, internal resistance to new methodologies, and the necessity for robust cross-departmental collaboration to effectively implement segment-specific strategies.

The main contribution of the paper can be seen in two levels. First, at the level of application, i.e., the actual creation of a 3D segmentation model for segmenting corporate customers within the postal sector. This is particularly valuable given that no comparable model has yet been established within the postal sector. The demonstrated model was established in response to the segmentation needs of a European national postal operator—specifically, Slovak Post, plc. In view of this, it can be assumed that the proposed model could also be applied under the conditions of other national postal operators, especially those operating in geographically close territory (The Czech Republic, Hungary, Poland). Secondly, there is a theoretical contribution, again in relation to the 3D segmentation model created. Individual segmentation criteria were selected with regard to respecting the three basic pillars of sustainable development and simultaneously confronting the scientific works of other authors. The created theoretical framework of the paper can be considered beneficial, especially from the point of view of providing a reasonable theoretical background for further work of authors dealing with the given issue within the postal services sector.

In terms of broader applicability, the model’s implementation under the conditions of Slovak Post offers insight into how other national postal operators—particularly those operating under partial regulation—may adopt similar frameworks. However, successful adaptation requires access to operational data, a clear understanding of customer service structures, and organizational interest in integrating sustainability metrics into CRM systems. The model’s flexibility lies in its conceptual design, which allows for context-specific recalibration of segmentation thresholds while preserving alignment with the three sustainability pillars.

From a systems science perspective, this research demonstrates how multidimensional segmentation models can serve as effective tools for managing complexity and supporting sustainable development in regulated service environments. The systems-based approach integrates economic, social, and environmental criteria, providing a holistic framework that is broadly adaptable beyond the postal sector. By adopting a systems perspective, organizations can better understand and optimize the interdependencies within their customer base, contributing to more resilient and sustainable management strategies.

Future research could also examine how the quadrant-based segmentation logic performs when applied across a longer time horizon or under different service scenarios. This would allow for testing the stability of segment membership over time, and for measuring whether CRM interventions lead to meaningful transitions between quadrants. Such studies would enhance the evidence base for strategic CRM planning in regulated markets.

In practice, the quadrant-based approach also functions as a strategic feedback tool, allowing decision-makers to detect structural imbalances in their customer base and evaluate the effects of targeted actions over time. For instance, while Q3 and Q5 may reflect partial alignment with sustainability goals, their distinct profiles require different managerial responses. Monitoring shifts between quadrants enables postal operators to assess whether customer engagement, pricing, or service modifications are producing the intended outcomes. This supports iterative learning in a regulated environment, where resources and flexibility are often constrained.

Looking ahead, the model can be further developed into a decision-support tool by integrating it with operational dashboards, route planning systems, or CRM modules. This would enable real-time monitoring of customer quadrant shifts and allow organizations to adjust their strategies dynamically. Ultimately, the long-term impact of this work will depend on its ability to inspire similar segmentation logic in other regulated industries where balancing cost, service quality, and social responsibility is both a challenge and a strategic imperative. In this regard, incorporating the segmentation framework into predictive analytics or customer churn models could further increase its operational value and support more responsive and data-driven customer management. Such integration would enable dynamic monitoring and foster strategic alignment of CRM efforts within complex, hybrid public–private, and multi-stakeholder ecosystems, enhancing accountability for sustainable outcomes beyond traditional profit metrics.