Jose Alejandro Espindola Michel How Customer Success Managers Operationalise Segmentation Strategies in B2B SaaS Companies Vaasa 2025 School of Marketing and Communication Master’s thesis in International Business 2 The author sincerely thanks Mia-Kristina Lager for her support and constant feedback during this process. A further thank you goes to the interview participants, whose names cannot be mentioned without violating their guaranteed anonymity, for their effort, time and openness. The author also wants to thank Laura Valderrama Castillo and Almuth Müller for their invaluable support during the thesis process. 3 UNIVERSITY OF VAASA School of Marketing and Communication Author: Jose Alejandro Espindola Michel Title of the thesis: How Customer Success Managers Operationalise Segmentation Strategies in B2B SaaS Companies Degree: Master of International Business Administration Supervisor: Mia-Kristina Lager Year: 2025 Pages: 92 ABSTRACT: This thesis investigates how Customer Success Managers (CSMs) leverage customer segmentation strategies to prioritize client engagement and allocate resources in mid-sized B2B Software-as-a-Service (SaaS) companies. While segmentation is a foundational concept in strategic marketing, its operational implementation by CSMs in resource-constrained environments remains underexplored. The central research question guiding this study is: How do CSMs leverage customer segmentation strategies to prioritize client engagement and allocate resources in mid-sized B2B SaaS companies? To address this gap, the study adopts a qualitative, interpretivist, single-case embedded design. Drawing on segmentation theory (Payne & Frow, 2005), Customer Lifetime Value (Kumar & Reinartz, 2016), and resource allocation models (Reinartz et al., 2004), twelve semi-structured interviews with CSMs were conducted and analysed using thematic analysis (Braun & Clarke, 2006). The findings reveal that segmentation frameworks are often ambiguously defined and inconsistently applied. CSMs interpret and adapt segmentation logic based on client behaviour, internal pressures, and resource limitations. Formal segmentation is used as a starting point but frequently adjusted through practitioner discretion. Key challenges include informal re- segmentation, role ambiguity, misalignment with Account Executives, and inadequate system integration. Automation supports scaled engagement for low-tier clients but creates friction when client expectations are mismatched. This study contributes to strategic marketing literature by demonstrating that segmentation is not only a top-down planning tool but also an interpretive and negotiated practice. It offers actionable guidance for SaaS leaders seeking to embed dynamic, context-sensitive segmentation frameworks into Customer Success operations and to align resource prioritization with customer value across the lifecycle. KEYWORDS: Customer Segmentation, Customer Success Management (CSM), SaaS (Software- as-a-Service), Resource Allocation & Client Profitability 4 Contents 1 Introduction .............................................................................................................. 8 1.1 Background of the study .................................................................................. 8 1.2 Research Problem & Justification ..................................................................... 9 1.3 Research Objectives & Questions................................................................... 12 1.4 Scope and Delimitations ................................................................................. 14 1.5 Structure of the Thesis ................................................................................... 16 2 Theoretical Background .......................................................................................... 17 2.1 The Strategic Role of Customer Segmentation in B2B SaaS ........................... 17 2.2 Segmentation Logic, Customer Lifetime Value, and Resource Allocation ..... 19 2.2.1 Segmentation and Customer Lifetime Value ......................................... 19 2.2.2 Resource Allocation in Customer Success .............................................. 20 2.2.3 Service Tiering and SaaS Operational Complexity .................................. 22 2.2.4 Integration Gaps and Organisational Misalignment .............................. 24 2.2.5 Interpretive Segmentation and Practitioner Discretion ......................... 25 2.3 Conceptual Framework and Research Gap .................................................... 27 2.3.1 Conceptual Synthesis .............................................................................. 27 2.3.2 Research Gap .......................................................................................... 29 2.3.3 Relevance to the Research Question ..................................................... 31 2.3.4 Conceptual Framework Summary .......................................................... 32 3 Methodology .......................................................................................................... 33 3.1 Research Philosophy and Approach ............................................................... 33 3.2 Research Design.............................................................................................. 36 3.3 Data Collection ............................................................................................... 38 3.4 Data Analysis Approach .................................................................................. 40 5 3.5 Validity and Reliability .................................................................................... 43 4 Findings ................................................................................................................... 44 4.1 Interview Sample Overview and Case Context .............................................. 44 4.2 Understanding Segmentation in Practice ....................................................... 47 4.2.1 Clarity and Use of Formal Segmentation Models................................... 47 4.2.2 Informal Re-segmentation and Discretion in Practice ........................... 49 4.2.3 Automation and Scaled Engagement for Low-Tier Clients ..................... 52 4.3 Challenges and Enablers in Practical Segmentation Use ............................... 56 4.3.1 Barriers to Segmentation Execution ....................................................... 56 4.3.2 Organizational Misalignment and Role Friction ..................................... 58 4.3.3 Enablers of Effective Segmentation ....................................................... 60 4.3.4 CSM Skills and Judgment as Differentiators ........................................... 61 4.4 Synthesis: Addressing the Research Questions .............................................. 62 4.5 Summary of Findings ...................................................................................... 64 5 Discussion and Conclusion ..................................................................................... 65 5.1 Theoretical Interpretation of Findings ........................................................... 66 5.1.1 Segmentation Theory in Practice ........................................................... 67 5.1.2 Customer Lifetime Value and Strategic Prioritization ............................ 68 5.1.3 Resource Allocation and Organizational Alignment ............................... 69 5.2 Practical and Strategic Implications of the Findings ...................................... 71 5.2.1 Strategic Implications for SaaS Leadership and Cross-Functional Design 71 5.3 Contributions to Research and Practice ......................................................... 74 5.4 Limitations of the Study ................................................................................. 75 5.5 Suggestions for Future Research .................................................................... 77 6 6 Conclusion .............................................................................................................. 79 7 References .............................................................................................................. 81 8 Disclosure of Research Tools .................................................................................. 87 9 Appendices ............................................................................................................. 89 9.1 Appendix 1: Glossary of Key Terms ................................................................ 89 9.2 Appendix 2: Semi-Structure Interview Guide for CSMs ................................. 89 9.3 Appendix 3: Semi-Structure Interview Guide for Leadership ........................ 91 7 Tables Table 1. Interview Participant Overview 44 8 1 Introduction 1.1 Background of the study In recent years, customer segmentation has taken on a strategic role in the Software-as- a-Service (SaaS) industry, where growth increasingly depends on the ability to balance limited internal resources with a rapidly expanding client base. As companies scale, the challenge is no longer just about acquiring customers but about delivering meaningful value across a varied portfolio of accounts. Research by McKinsey suggests that well- structured segmentation practices can significantly influence business outcomes, including reductions in churn and increased revenue contributions from existing customers (Atkins et al., 2018). In contrast to traditional business models that prioritise point-of-sale value capture, SaaS firms operate on the basis of recurring revenue. This means that customer value must be continuously maintained and nurtured (Gupta & Lehmann, 2003; Kumar & Reinartz, 2016). Within this framework, segmentation can be viewed not only as a means of tailoring services, but also as a way to allocate Customer Success efforts in line with each account’s growth trajectory and strategic importance. This is particularly relevant for mid-sized SaaS firms, which often operate without robust automation, mature systems, or fully staffed success teams. Despite its strategic importance, the practical implementation of segmentation within Customer Success Management (CSM) remains underexamined. While marketing theory presents segmentation as a means to optimise resource allocation and customer value delivery (Payne & Frow, 2005), CSMs face operational constraints that make consistent application difficult (Eggert et al., 2020a; Hilton et al., 2020). Their role has evolved from reactive account management to proactive value enablement, requiring ongoing interpretation of segmentation logic and responsiveness to client signals. CSMs are expected to balance service standardization with contextual judgment, particularly when managing portfolios that span multiple tiers of customer value. Moreover, segmentation frameworks are often designed at the leadership level using criteria such as Annual Contract Value (ACV), Customer Lifetime Value (CLV), or product 9 usage. Yet these models are frequently applied inconsistently across functions or poorly integrated into Customer Relationship Management (CRM) systems (Eggert et al., 2020b; Lacity & Willcocks, 2015a). CSMs, operating at the intersection of sales, service, and product enablement, often encounter ambiguity around role expectations, engagement boundaries, and prioritization mandates. This is especially true when handovers from Account Executives (AEs) lack clear alignment or when promises made during the sales process contradict tier-based service delivery (Lopez, 2024). The growing use of automation to support scaled engagement for low-tier clients adds another layer of complexity. While automation is intended to enable efficiency, it may also result in disengagement or misalignment with client expectations if segmentation logic is not actively maintained and contextualized (Khare & Arora, 2024; Truss & Boehm, 2024). In many cases, segmentation becomes a flexible guide rather than a fixed rule, interpreted by CSMs based on judgment, experience, and internal negotiation. The interpretive dimension of how practitioners enact, adapt, or challenge formal segmentation frameworks remains under-explored in strategic marketing and SaaS literature. Given these challenges, there is a clear need to investigate how segmentation strategies are operationalized by Customer Success Managers in the specific context of mid-sized B2B SaaS firms. Understanding how CSMs navigate segmentation logic in resource- constrained environments offers critical insight into the effectiveness of current frameworks and the organizational factors that enable or hinder their application. 1.2 Research Problem & Justification Customer segmentation is widely considered a cornerstone of strategic marketing. It offers a structured way to align engagement efforts and resource allocation with customer needs, behaviours, or projected value (Payne & Frow, 2005; Kumar & Reinartz, 2016). Within B2B SaaS companies, this approach becomes even more relevant due to the recurring revenue model, where sustained engagement, retention, and expansion are critical to long-term profitability (Gupta & Lehmann, 2003; Verhoef & Donkers, 10 2001). While the theoretical rationale for segmentation is well established, considerably less is known about how these frameworks are actually applied by customer-facing roles, particularly Customer Success Managers (CSMs) in mid-sized SaaS firms. These professionals often work in fast-changing environments, where internal processes, systems, and team structures are still evolving. Although segmentation strategies are typically defined using clear indicators such as contract value, product usage, or lifetime value projections, their real-world application tends to be more complex. In practice, segmentation is often introduced at the leadership level but not fully embedded into daily workflows, decision-making routines, or internal tools (Eggert et al., 2020b; Hilton et al., 2020). As a result, CSMs may need to interpret these models on their own, relying on experience, judgment, or informal norms to prioritise their time and attention. This creates a potential gap between strategic intent and operational execution one that is rarely examined in empirical research. While segmentation is frequently discussed in the literature as a lever for strategic focus and efficiency, much of this work assumes a level of system integration and cross- functional alignment that is not always present in mid-sized organisations. Few studies examine how segmentation is adjusted or even questioned by those tasked with applying it under real-world constraints. This thesis addresses that gap by focusing on how segmentation frameworks are experienced and enacted by CSMs, rather than simply assumed to be followed. In doing so, it contributes to both strategic marketing and Customer Success literature by exploring segmentation not as a fixed framework, but as a situated practice shaped by discretion, resource limitations, and organisational realities. This thesis lies at the intersection of segmentation theory, customer value modelling, and frameworks for resource prioritisation. While segmentation and value-based planning tools are often presented in the literature as mechanisms to enhance strategic efficiency, there has been limited empirical investigation into how these tools are implemented under conditions of organisational ambiguity or constraint. Many existing studies focus on the structure and rationale of segmentation frameworks from a top- 11 down perspective, with less attention given to the procedural and human elements that ultimately determine whether such models function effectively in practice (Hilton et al., 2020; Zoltners et al., 2019). This issue is particularly relevant in the context of Customer Success, where managers frequently operate across departmental boundaries and must reconcile standardised engagement models with individual client needs often without reliable access to real-time data or clear escalation procedures (Prohl-Schwenke & Kleinaltenkamp, 2021). From a managerial standpoint, examining how segmentation frameworks are interpreted and applied by CSMs is especially relevant in mid-sized SaaS companies, where limited headcount and constrained resources make scalable engagement a constant challenge. While segmentation may offer a theoretically sound basis for prioritising internal effort, its effectiveness depends heavily on how meaningfully it is operationalised by those implementing it. In the absence of clear workflows or ownership structures, segmentation risks functioning more as a symbolic concept than a practical tool. Understanding how segmentation is experienced and adapted by CSMs can help expose inefficiencies such as effort misalignment, variability in service quality, and resistance to automation strategies designed to support low-touch accounts (Lacity & Willcocksa). Although segmentation and customer value models are well established in theory, the interpretive space in which CSMs work has received little empirical attention. Much of the academic literature implicitly assumes a level of organisational maturity and technological integration that is rarely present in mid-sized SaaS environments (Eggert et al., 2020b; Hilton et al., 2020). As a result, the ways in which segmentation frameworks are adapted, simplified, or contested due to internal frictions, evolving roles, or client dynamics remain poorly documented. Some recent studies have begun to explore how practitioners mediate the space between strategic planning and execution, yet further research is needed to understand how segmentation plays out in the day-to-day realities of SaaS operations (Prohl-Schwenke & Kleinaltenkamp, 2021; Zoltners et al., 2019). 12 In response to this gap, the present study adopts a qualitative, interpretivist approach, using a single-case embedded research design to investigate how segmentation is enacted in a mid-sized SaaS organisation. This methodology enables exploration of variation across account segments and role types, offering deeper insight into how segmentation frameworks are translated into practice. By focusing on the lived experiences of CSMs, the study captures how segmentation logic is shaped by organisational ambiguity, resource constraints, and discretionary decision-making. In doing so, it contributes to the strategic marketing and Customer Success literature by extending segmentation research into operational contexts. It also offers practical insights for leaders aiming to improve service design, clarify role responsibilities, and strengthen the link between segmentation strategy and engagement execution within growth-focused yet resource-limited environments. 1.3 Research Objectives & Questions Customer segmentation is a well-established concept in strategic marketing. It is typically used to support differentiated engagement and internal resource prioritisation by identifying relevant differences across customer groups (Payne & Frow, 2005; Kumar & Reinartz, 2016). In mid-sized B2B SaaS firms, this logic becomes particularly important, as these organisations often face limited Customer Success capacity, a lack of robust automation infrastructure, and considerable variability within their client portfolios. As a result, segmentation is increasingly used not only for marketing strategy but also as a tool to inform how internal effort is distributed across accounts. This is especially true for Customer Success Managers, who play a central role in coordinating engagement, onboarding, and retention in complex post-sale environments (Eggert et al., 2020a; Hilton et al., 2020). Although segmentation theory and Customer Lifetime Value models have been extensively developed in the academic literature, there is still limited understanding of how these are applied in practice. Much of the existing research focuses on strategic- level segmentation logic, offering little insight into how frameworks are adapted under operational constraints. In particular, there is a gap in knowledge about how Customer 13 Success Managers use segmentation to manage competing priorities across their accounts, shift between reactive and proactive tasks, or make context-sensitive decisions when formal models fall short. These issues are highly relevant in SaaS contexts, where segmentation misalignment can directly impact customer retention, expansion, and ultimately profitability (Gupta & Lehmann, 2003; Hilton et al., 2020). This thesis responds to this gap by examining how segmentation models are interpreted and enacted in mid-sized SaaS organisations. It places particular emphasis on how segmentation logic is used to guide engagement priorities and balance resource constraints. The central research question is: How do Customer Success Managers (CSMs) leverage customer segmentation strategies to prioritize client engagement and allocate resources in mid-sized B2B SaaS companies? To explore this question in greater detail, two sub-questions have been developed. These reflect the operational nature of segmentation in post-sale functions and are aligned with the theoretical perspectives presented in Chapter 2: 1. How do CSMs apply segmentation to prioritize accounts and focus their time and resources? This sub-question explores the extent to which segmentation criteria, such as contract value, lifecycle stage, or retention risk, shape how effort is allocated. It also considers whether segmentation tiers are consistently applied in daily workflows or adjusted based on situational judgement. 2. What challenges and enablers influence the practical use of segmentation in daily CSM workflows? This question investigates both structural and behavioural factors that support or constrain segmentation execution. It includes role clarity, tooling integration, internal collaboration between Customer Success and Account Executives, and the level of 14 discretion available when segmentation logic does not reflect on- the-ground realities (Eggert et al., 2020b; Prohl-Schwenke & Kleinaltenkamp, 2021). Together, these questions position the study at the intersection of strategic segmentation theory and the interpretive, relational nature of Customer Success work. Rather than treating segmentation as a static design framework, the research examines how it is brought into practice, often under pressure, with incomplete data, or in tension with other organisational priorities. In doing so, the study contributes to a more practice-oriented understanding of segmentation as a dynamic and context-sensitive process embedded in the everyday work of post-sale professionals. 1.4 Scope and Delimitations This study investigates how segmentation strategies are interpreted and applied by Customer Success Managers within a mid-sized B2B SaaS company. It focuses particularly on how these frameworks influence decisions around resource prioritisation, engagement intensity, and account management under conditions of operational constraint. The research is anchored within the field of strategic marketing, drawing on segmentation theory (Payne & Frow, 2005), customer lifetime value frameworks (Gupta & Lehmann, 2003; Kumar & Reinartz, 2016), and resource allocation logic (Reinartz et al., 2004). These concepts form the analytical foundation for understanding how firms align internal efforts with client value potential. The study adopts a qualitative, interpretivist case design centred on a single organisation where segmentation is used with varying degrees of structure and discretion across different client portfolios. The embedded design makes it possible to explore differences across account tiers and engagement models without expanding the external scope of the research. While broader organisational and strategic influences are acknowledged, the analysis deliberately focuses on the Customer Success function. This decision reflects the critical role that Customer Success Managers play in translating theoretical segmentation frameworks into practical action. Input from adjacent departments, such 15 as sales, product, or leadership, is considered only in terms of how these functions shape the operational environment in which segmentation takes place. To maintain conceptual focus, several adjacent topics are excluded from the study. It does not examine CRM systems, algorithmic segmentation, or predictive modelling tools, as these fall outside the study’s human-centred orientation and would require a more technical and data-driven lens (Hilton et al., 2020; Lacity & Willcocks, 2015a). The research also does not evaluate segmentation outcomes through quantitative metrics such as churn or revenue growth, given the absence of reliable account-level data. Although a mixed-methods design was originally considered, access restrictions within the case company ruled out quantitative data collection, which further confirmed the decision to adopt a qualitative and narrative approach (Braun & Clarke, 2006). The study treats segmentation not as a fixed classification system but as an interpretive and operational logic. It does not assess tiering systems, pricing strategies, product configurations, or customer journey mapping in technical detail, except where these directly influence how segmentation is used by Customer Success Managers. While automation and tooling are acknowledged, the emphasis remains on human discretion, judgement, and cross-functional alignment as core variables shaping segmentation in practice. Additional delimitations also help to define the study’s boundaries. The research does not compare multiple organisations, does not address segmentation practices across industries, and does not provide longitudinal data on how segmentation evolves over time. It presents a snapshot of segmentation as experienced in one firm, at one point in time, within a specific organisational context. The aim is not statistical generalisation, but rather analytical generalisation by identifying patterns and challenges that may be transferable to other mid-sized SaaS firms operating under similar constraints. The study does not impose geographical limits, recognising that SaaS companies typically serve clients across regions through global delivery models. This openness enhances the potential transferability of the findings, while acknowledging that local 16 regulatory or cultural factors may influence segmentation elsewhere (Loukis et al., 2019). Taken all together, the study’s boundaries are intentionally narrow. Its single-case focus, emphasis on practitioner perspective, and theoretical grounding in strategic marketing are deliberate. These parameters are designed to support analytical depth, empirical relevance, and theoretical coherence in examining how segmentation is implemented by customer-facing professionals in scaling SaaS environments. 1.5 Structure of the Thesis This thesis is organised into five chapters, each building upon the previous to explore how segmentation strategies are interpreted and applied within Customer Success Management in mid-sized B2B SaaS firms. The structure moves from theoretical framing toward practical insights, combining conceptual, methodological, and empirical perspectives. Chapter 1 introduces the study, outlining its background, research motivation, objectives, and scope. It also defines the conceptual boundaries that shape the inquiry. Chapter 2 reviews the relevant literature on customer segmentation, value-based prioritisation, and engagement strategy. This chapter identifies key theoretical foundations while highlighting gaps in current research that this study seeks to address. Chapter 3 explains the methodological approach, describing the interpretivist research orientation and justifying the use of a qualitative, single-case embedded design. It details how participants were selected, how data were collected and analysed, and how issues of validity and reliability were addressed. Chapter 4 presents the findings from the empirical analysis, structured around the two research sub-questions. It examines how segmentation is applied in operational practice and what constraints or adaptations emerge in day-to-day work. 17 Chapter 5 discusses the implications of the findings in relation to the theoretical framework. It considers both academic contributions and managerial relevance and concludes with recommendations for future research and practical improvements in segmentation strategy. Together, these chapters offer a structured progression from conceptual exploration to practitioner experience. The thesis aims to provide an integrated understanding of segmentation as both a strategic framework and a lived practice within Customer Success team. 2 Theoretical Background This chapter explains the main ideas that support the research and help analyse how customers are grouped in the SaaS industry. It brings together three interrelated theoretical lenses: customer segmentation theory, Customer Lifetime Value, and resource allocation logic. These frameworks shape how the study interprets segmentation as both a strategic intention and a practical activity. The chapter begins by situating segmentation within the broader context of B2B SaaS, where recurring revenue models and post-sale engagement make client prioritisation essential. It then examines how segmentation frameworks operate in resource-constrained environments, where Customer Success professionals must apply, adapt, or even challenge formal models in the course of their daily work. Through this review, the chapter establishes the analytical basis for understanding segmentation as a situated practice that depends on human judgment, organisational systems, and internal alignment. 2.1 The Strategic Role of Customer Segmentation in B2B SaaS Customer segmentation has served as a marketing method for aligning offerings with customer needs by grouping clients based on shared behaviours, characteristics, or value potential (Payne & Frow, 2005). In B2B contexts, segmentation becomes more complex due to multiple stakeholders, long-term contracts, and diverse implementation 18 requirements (Cooil et al., 2008; Bowden, 2009). The growth of SaaS models has further transformed the role of segmentation, shifting the focus from point-of-sale transactions to sustained engagement, expansion, and retention across the customer lifecycle (Gupta & Lehmann, 2003; Kumar & Reinartz, 2016). In SaaS organisations, segmentation serves a strategic role that extends beyond marketing. It forms the basis for client tiering systems, which inform how companies allocate Customer Success resources and shape internal workflows. Criteria such as annual contract value, product usage, industry vertical, or onboarding complexity are often used to determine whether accounts receive high-touch support or a digital-led experience (Ahmad & Buttle, 2001; Atkins et al., 2018). As Bain & Company (2024) note, many SaaS firms now update segmentation dynamically using behavioural data and usage metrics to reflect changes in customer needs and business priorities. For mid-sized SaaS firms, segmentation offers a structured approach to managing complexity and maintaining scalability. It helps teams prioritise limited time and resources toward accounts with the greatest growth potential. However, rigid application of tiers can create mismatches between service delivery and actual client needs, particularly when segmentation logic is not regularly revisited or operationalised through flexible systems (McKinsey & Company, 2022; Hilton et al., 2020; Prohl- Schwenke & Kleinaltenkamp, 2021). These challenges are especially pronounced in firms where capacity is limited and customer-facing teams must operate with discretion. Segmentation frameworks also influence internal culture and coordination. They shape how Customer Success teams define value, collaborate across departments, and structure decision-making. Poorly integrated segmentation can result in unclear roles, inconsistent customer experience, and internal friction especially when the division between Sales and Success functions is blurred (Eggert et al., 2020a). Conversely, well- designed frameworks support alignment and help organisations shift from reactive service to proactive account management (Dempsey & Kelliher, 2018). Segmentation should not be viewed exclusively as a classification exercise; rather, it can be conceptualized as a tool for aligning internal capacity with external client value. It 19 supports strategic planning while also guiding day-to-day engagement decisions. In the SaaS context, where client relationships evolve continuously, segmentation must remain adaptable and embedded in systems that allow for responsive service. This section has outlined how segmentation operates as both a strategic and operational lever. The next section explores how segmentation interacts with Customer Lifetime Value and resource allocation models, providing further theoretical grounding for the study. 2.2 Segmentation Logic, Customer Lifetime Value, and Resource Allocation 2.2.1 Segmentation and Customer Lifetime Value Customer Lifetime Value plays an increasingly important role in guiding segmentation decisions, especially within subscription-based business models where revenue is accumulated over time rather than realised at the point of sale (Gupta & Lehmann, 2003; Kumar & Reinartz, 2016). Unlike demographic or firmographic segmentation approaches, value-based models incorporate future revenue potential, churn risk, and expansion likelihood. This makes them particularly attractive in the software-as-a- service sector, where high acquisition costs and delayed profitability require firms to make informed decisions about where to focus their engagement efforts (Bejou et al., 2013; Lemon & Mark, 2006). Segmentation frameworks built on future value are designed to match account potential with the level of service provided. Accounts that demonstrate high current value or strategic growth potential typically receive more intensive support, while lower- potential clients are often directed toward scaled or digital engagement models. Ideally, this allows customer-facing teams to optimise resources and drive greater return on effort. However, applying this logic in real-world settings is far from straightforward. Estimating long-term value accurately can be difficult, particularly in mid-sized firms with limited historical data or rapidly evolving client needs. Common proxies, such as contract size or user volume, may not fully capture an account’s potential over time. As 20 a result, segmentation decisions may overlook promising accounts or misclassify clients with non-linear growth trajectories (Kumar & Reinartz, 2016; Gupta & Lehmann, 2003). A further complication arises from the gap between financial modelling and operational reality. While segmentation frameworks suggest which accounts should receive priority, they do not always provide actionable guidance for practitioners. Customer Success teams often face situations where client behaviour deviates from predictions. A seemingly low-value account might suddenly require high-touch support due to internal restructuring, while a high-value account may disengage despite qualifying for premium service. In such cases, segmentation logic alone may be insufficient, and professional judgement becomes essential. These tensions highlight the interpretive challenges faced when navigating between predictive models and day-to-day account dynamics. Although value-based segmentation offers a strong theoretical foundation, its practical implementation depends on more than financial forecasting. Effectiveness requires flexibility, contextual awareness, and regular recalibration. Kumar and Reinartz (2016) caution that models can quickly lose relevance without ongoing adjustment. Bowden (2009) similarly stresses the importance of operational adaptability when applying segmentation theory. Prohl-Schwenke and Kleinaltenkamp (2021) further argue that segmentation frameworks must be maintained actively and embedded within organisational routines to remain effective. These perspectives suggest that the successful use of value-based segmentation relies not only on modelling sophistication but also on how well organisations support its application through adaptive systems, internal communication, and practitioner insigh. 2.2.2 Resource Allocation in Customer Success Segmentation is not only a way to classify customers, but also a mechanism for guiding how firms distribute internal resources. In the context of Customer Success, this means balancing time, staffing, and digital infrastructure across accounts with varying levels of importance. Resource allocation theory supports this approach by suggesting that engagement intensity should reflect a customer’s potential contribution to value 21 creation. When implemented effectively, this alignment can improve profitability and reduce inefficiencies (Ahmad & Buttle, 2001; Hilton et al., 2020). Many software-as-a- service companies apply this logic by using tiered segmentation frameworks that define which accounts receive high-touch, hybrid, or digital-led service. In theory, segmentation provides a structure for focusing engagement where it delivers the greatest impact. Yet in practice, applying this logic consistently is often difficult. While formal service levels may be assigned, actual time allocation by Customer Success teams frequently diverges from these frameworks. Hilton et al. (2020) highlight that such inconsistencies are common, particularly in firms without clear escalation procedures or integrated tooling. When systems are lacking, professionals must rely on personal judgement to manage tasks, which can lead to inconsistent prioritisation. Dwyer (1997) also notes that resource allocation tends to fragment when service tiers are not fully embedded into daily routines or reinforced by governance structures. A further complication arises from overlapping responsibilities between Sales and Customer Success. As noted by Prohl-Schwenke and Kleinaltenkamp (2021) and Eggert et al. (2020a), the success of segmentation depends not only on the structure of the model but also on how clearly roles are defined and coordinated across departments. In many organisations, segmentation is weakened by unaligned expectations, undocumented service boundaries, or Sales commitments that stretch beyond the assigned tier. These misalignments can pressure teams to over-service accounts or ignore segmentation logic to maintain relationships and satisfaction. The situation becomes even more complex when client behaviour changes unexpectedly. A customer initially classified as low-touch may require intensive support due to leadership changes, new adoption patterns, or expansion opportunities. In the absence of responsive systems, teams must handle these shifts informally. Ahmad and Buttle (2001) caution that while discretionary support can be helpful, it can also undermine the efficiency gains segmentation is meant to achieve. Without regular review and system-level adjustments, segmentation can drift from a planning tool to a loose guideline, applied inconsistently across teams. 22 These challenges are particularly visible in mid-sized software organisations, where limited automation, lean staffing, and fluid account dynamics place additional strain on resource decisions. For segmentation to support effective resource allocation, it must be backed by operational clarity, cross-functional alignment, and systems that adapt to change. Without these foundations, even well-designed segmentation frameworks may struggle to translate into practical value. 2.2.3 Service Tiering and SaaS Operational Complexity In the software-as-a-service industry, segmentation models are frequently translated into tiered service structures that guide how accounts are managed after the initial sale. These frameworks typically differentiate between enterprise clients, who receive high- touch engagement, and smaller accounts, which are supported through digital-first or automated strategies (Atkins et al., 2018; McKinsey & Company, 2022). The intended goal is to deliver scalable value while maintaining service consistency and efficient use of resources. When implemented effectively, tiering helps align internal capabilities with the varying needs of different client segments. However, these benefits are often difficult to realise in mid-sized organisations, where service boundaries tend to overlap, exceptions are common, and internal systems do not always support formal tier governance. One recurring issue is the misalignment between predefined tiers and the unpredictable nature of customer behaviour. Accounts placed in low-touch segments may still demand personalised support, for example due to leadership changes, increased executive visibility, or onboarding gaps (Goldberg, 2012). Conversely, clients assigned to high- touch service may underutilise the resources available to them, resulting in inefficient allocation of time and personnel. This inconsistency reflects a broader tension between static service models and the dynamic needs of customers over time. Recent work by Wulf and Meierhofer (2024) highlights the growing prevalence of hybrid accounts that do not fit neatly into one category. These clients often receive a combination of digital 23 and personal engagement, which introduces variability and complicates planning. Although this flexibility may be necessary, it can also reduce predictability and undermine the original intent of segmentation. Operational challenges are further intensified when service tiers are not embedded into internal systems. McKinsey & Company (2022) note that high-performing SaaS firms incorporate tiering logic directly into customer relationship management platforms, internal playbooks, and prioritisation workflows. When these structures are absent, segmentation risks becoming a conceptual tool rather than a practical guide. Prohl- Schwenke and Kleinaltenkamp (2021) observe that without systems to reinforce tiering logic, execution becomes inconsistent across teams. Manual tier changes, informal overrides, and unclear escalation paths place additional pressure on Customer Success professionals, especially in firms with limited automation or reporting capabilities. Tiering also influences collaboration between teams, particularly between Sales and Customer Success. Problems arise when commitments made during the sales process exceed what the designated tier allows, or when service levels are not clearly communicated across functions. These disconnects can lead to a mismatch between client expectations and actual delivery. Researchers have noted that such misalignments are particularly problematic during onboarding or strategic account transitions, when expectations are most visible and coordination is most critical (Eggert et al., 2020a; Prohl-Schwenke & Kleinaltenkamp, 2021). When segmentation frameworks are not supported by shared understanding and clear operational boundaries, they may lose credibility and hinder team performance. Service tiering remains a valuable mechanism for translating segmentation into operational practice. However, its effectiveness depends on a firm’s ability to adjust for situational needs, integrate segmentation into daily tools and routines, and ensure alignment between functions. In mid-sized SaaS organisations, where capacity is limited and account complexity can shift rapidly, these challenges are especially pronounced. Without clear structures and shared accountability, tiering may add to inconsistency rather than reduce it. 24 2.2.4 Integration Gaps and Organisational Misalignment Many software companies embrace segmentation frameworks as part of their strategic plans. Yet in practice, translating these frameworks into operational routines often proves difficult. One common challenge is the gap between high-level segmentation logic and the systems that are supposed to support its implementation. As Eggert et al. (2020a) note, segmentation is frequently communicated as a strategic priority, but lacks clear ownership or enforcement mechanisms in daily work. This gap is particularly noticeable in mid-sized firms, where Customer Success teams are asked to follow service tiers without having the tools or processes needed to do so consistently. Segmentation can appear in internal presentations or planning documents, yet remain disconnected from customer relationship management systems, engagement tools, and reporting workflows. Without integration into these systems, segmentation loses its ability to shape daily decisions. Another issue arises when Customer Success professionals must rely on manual effort or informal judgement due to the absence of automation or unclear decision rules. Lacity and Willcocks (2015a) argue that process automation and system-triggered workflows are critical for maintaining consistency in scaled service models. When these supports are missing, teams often fall back on ad-hoc decisions, which may serve immediate client needs but weaken overall coherence. This challenge is particularly evident with digital-first accounts. Although these clients are meant to be managed through automated channels, the lack of functional tooling often pushes the work back onto individual team members. Lacity and Willcocks (2015b) emphasise that automation only adds value when it is integrated in ways that support rather than complicate human judgement. A related obstacle comes from organisational misalignment. When teams do not share a common understanding of segmentation or interpret service models differently, execution becomes inconsistent. Bowden (2009) and Dempsey and Kelliher (2018) argue that without clear roles and shared interpretation, segmentation frameworks may be implemented unevenly or disregarded entirely. Misalignment between Sales and 25 Customer Success is especially problematic. Differing incentives, vague handovers, and unclear expectations about client potential can make it difficult to maintain service boundaries. Prohl-Schwenke and Kleinaltenkamp (2021) stress that segmentation requires not only clear design but also ongoing support through training, role clarity, and interdepartmental routines. Without these supports, individual contributors are left to interpret the framework on their own, often with conflicting results. If segmentation is not a part of the system, if it doesn't have clear ownership, or if it is not applied consistently, its impact on daily decisions quickly diminishes. Eggert et al. (2020b) describe this as a failure to connect strategy with the micro-decisions that shape customer relationships. In such environments, segmentation often becomes more of a guideline than a planning tool. This weakens internal alignment, leads to inconsistent customer experiences, and undermines confidence in the framework itself. 2.2.5 Interpretive Segmentation and Practitioner Discretion Segmentation frameworks are often designed at the strategic level, intended to create structure, predictability, and consistency across customer engagement. However, their effectiveness depends on how they are interpreted and applied by the professionals responsible for executing them. In many organisations, especially mid-sized software firms, segmentation is not experienced as a fixed set of instructions but as a flexible guide that must be adapted to changing contexts. Customer Success teams frequently make situational decisions about when to follow the model, when to adjust, and when to override it based on client expectations, internal constraints, or limited system support. Bowden (2009) notes that discretion is not simply a departure from strategy, but an essential form of judgement in complex service environments. This interpretive role becomes even more significant in firms where Customer Success professionals operate across multiple touchpoints, including sales, onboarding, and retention. In such settings, engagement decisions are shaped not only by strategic models but also by organisational ambiguity and limited resources. Prohl-Schwenke and Kleinaltenkamp (2021) argue that segmentation frameworks often lack ongoing 26 feedback loops, which makes it difficult for them to reflect the realities of day-to-day customer interactions. As a result, teams develop their own informal practices to bridge the gap between theoretical categories and actual customer needs. These practices may take the form of prioritisation shortcuts, workarounds, or negotiated compromises with other departments. While these adaptations are often necessary, they highlight the limits of rigid segmentation models. One of the reasons this gap persists is the limited involvement of practitioners in segmentation design. Eggert et al. (2020a) observe that segmentation strategies are typically developed by senior leadership without input from those who are expected to apply them. When models are imposed without adequate communication or integration, Customer Success professionals are left to reconcile strategy with operational demands. This often results in improvisation, where frontline staff modify service delivery to suit what they perceive as client reality. While this may meet short- term needs, it can undermine consistency across the organisation. These challenges become more difficult to manage in the absence of governance mechanisms that support adaptation over time. Without processes for updating segmentation models or capturing feedback from the field, frameworks quickly lose relevance. Kumar and Reinartz (2016) emphasise that effective segmentation is not only about initial design but also about ongoing responsiveness. That responsiveness rarely happens automatically. It depends on whether organisations are willing and able to recognise how segmentation is being adapted in practice, and whether they are prepared to reflect those adaptations back into the model. Understanding segmentation as a lived and interpreted process highlights the importance of practitioner judgement, internal communication, and organisational flexibility. Rather than viewing segmentation as a static system to be enforced, it becomes more useful to see it as a framework that evolves through its interaction with daily work. As this study will show, the ability of Customer Success professionals to adapt segmentation frameworks is both a strength and a challenge. It allows teams to respond 27 to complexity, but also raises questions about strategic coherence and operational consistency. 2.3 Conceptual Framework and Research Gap This section draws together the theoretical insights developed throughout the chapter and outlines the foundation for the research. Although segmentation has been widely discussed in marketing and strategic management literature, its use in post-sale environments, particularly within subscription-based business models, remains insufficiently examined. The earlier sections have shown that segmentation is not simply a strategic framework applied at the planning stage. Rather, it operates across multiple levels of practice. It connects predictive thinking, resource allocation, internal structure, and the judgement of those responsible for maintaining ongoing client relationships. Based on this understanding, the study introduces a conceptual framework that captures the complexity of segmentation as both a structured model and a dynamic process. The framework considers how segmentation is applied in environments where resources are limited and system integration is often incomplete. It also recognises the role of professionals working directly with clients, whose decisions frequently shape how segmentation is translated into operational routines. This perspective allows the research to examine where and how segmentation strategies succeed, and where they begin to fall short of their intended purpose. 2.3.1 Conceptual Synthesis The literature reviewed in this chapter suggests that segmentation operates across multiple dimensions. One prominent perspective is based on customer value over time. This approach prioritises accounts according to their potential contribution to future growth, considering factors such as expected product adoption, risk of discontinuation, and influence on other purchasing decisions (Gupta and Lehmann, 2003; Kumar and Reinartz, 2016). This forward-looking orientation has gained particular relevance in recurring revenue models, where long-term engagement is more important than the initial sale. Yet despite its appeal, value-based segmentation is often difficult to apply. 28 Many firms, particularly those in growth stages, lack the data or systems required to make these predictions reliably. As a result, segmentation based on future value often remains an ambition rather than a dependable guide. Another common justification for segmentation is its potential to improve how internal resources are distributed. Assigning effort in proportion to a client's importance is intended to help firms maintain focus while improving efficiency. This logic has been well established in the literature on resource allocation (Ahmad and Buttle, 2001; Hilton et al., 2020). However, putting it into practice often proves challenging. Research has shown that actual engagement patterns frequently diverge from planned models (Dwyer, 1997; Eggert et al., 2020a). These divergences are rarely caused by weak planning alone. Instead, they reflect daily capacity pressures, inconsistent tools, and the need to respond to rapidly changing customer situations. One way that firms attempt to apply segmentation in practice is through tiered service structures. These frameworks assign different types of support to different client groups, often based on contract value, technical needs, or the scale of implementation. Ideally, this structure supports scalable delivery by matching service intensity to client complexity. However, rigid structures can quickly become limiting. Clients sometimes require more support than their assigned tier allows, or they fail to use the resources allocated to them. Recent research suggests that firms with more adaptable practices often revise service models over time, using real-world usage patterns to guide decision- making (Goldberg, 2012; Wulf and Meierhofer, 2024; McKinsey and Company, 2022). Without this adaptability, service tiers may become disconnected from client expectations, increasing the risk of dissatisfaction or disengagement. 29 The ability of segmentation to guide practice also depends on how well it is embedded into the systems that teams use. A model may exist on paper, but unless it is reflected in workflow tools, reporting systems, and internal routines, it remains disconnected from daily operations (Prohl-Schwenke and Kleinaltenkamp, 2021). This gap often leads to uneven application. When professionals are not given clear access to segmentation logic or do not have tools that support its consistent use, decision-making becomes fragmented and reactive. Finally, much of the literature points to the importance of interpretation. Even well- designed segmentation frameworks are subject to adaptation by those implementing them. Client-facing professionals often rely on their own judgement to determine when and how segmentation applies. This can be beneficial when flexibility is needed, but it can also weaken consistency across teams (Bowden, 2009; Eggert et al., 2020a). Without shared guidelines, regular feedback, or cross-functional alignment, segmentation can drift from its original intent. Taken together, these perspectives suggest that segmentation cannot be understood purely as a technical or strategic exercise. It must also be seen as a process shaped by daily constraints, human judgement, and organisational context. The research presented in this thesis builds on this understanding by exploring how segmentation is applied, interpreted, and adapted by professionals working within environments that face both structural limitations and operational complexity. 2.3.2 Research Gap Although segmentation has been widely examined in marketing, strategic planning, and customer relationship research, much of the existing literature concentrates on the logic behind segmentation models or the outcomes they are intended to produce. Most studies assume that once a segmentation model is designed, it will be implemented 30 consistently and generate reliable results across customer-facing teams (Payne and Frow, 2005; Gupta and Lehmann, 2003; Kumar and Reinartz, 2016). However, more recent studies have begun to question this assumption. Research has shown that segmentation often fails to hold up in complex service environments, particularly where account needs are evolving and customer relationships are managed over time (Eggert et al., 2020a; Prohl-Schwenke and Kleinaltenkamp, 2021). These studies point to a range of operational obstacles, including unclear roles, fragmented tools, and inconsistent interpretation. Yet few have explored how segmentation is experienced by those responsible for applying it in everyday work. This gap is especially visible in the context of post-sale account management in subscription-based software businesses. While tiered service structures, prioritisation based on long-term value, and automated engagement systems are now central elements of Customer Success strategy, there is limited research on how these models are applied in practice. Unlike Sales and Marketing teams, which typically work with segmentation models during the early stages of the customer lifecycle, post-sale teams are expected to manage ongoing relationships and adapt to changing circumstances. This means that segmentation is rarely followed passively. It is interpreted, adjusted, and sometimes contested in response to client behaviour, internal pressure, or resource limitations (Bowden, 2009; Eggert et al., 2020a). Despite the expanding role of post-sale engagement in recurring revenue models, segmentation research has yet to fully account for this interpretive and adaptive use. Most studies continue to treat segmentation as a structured planning tool or a technical classification system. As a result, the ways in which segmentation is shaped by individual judgement, internal coordination, and organisational infrastructure remain underexplored. In particular, there is a lack of research on how segmentation is applied 31 in mid-sized firms where systems may be underdeveloped, roles less clearly defined, and internal resources more constrained. These gaps point to the need for a different approach. Rather than evaluating segmentation solely as a strategic model, it is important to understand how it works in everyday organisational settings. This study responds to that need by investigating how professionals responsible for post-sale engagement apply and adapt segmentation in a mid-sized software company. The research builds on interpretive theory to explore how segmentation is shaped by the people, systems, and decisions that sustain it over time. In doing so, it contributes to both segmentation theory and practitioner-focused research on post-sale engagement, offering a perspective grounded in how strategic models are used under real-world conditions. 2.3.3 Relevance to the Research Question The previous sections have shown that while segmentation is built on structured models such as long-term value forecasting, resource-based planning, and tiered service delivery, it often proves difficult to implement in consistent ways. These difficulties become especially visible after the sale, where client relationships are ongoing and where professionals must adapt segmentation frameworks to changing conditions. In this environment, segmentation is not treated as a fixed model. It functions more like a flexible tool that is interpreted and, at times, adjusted or even set aside to meet practical demands. This pattern is especially common in mid-sized software firms, where internal systems, role definitions, and cross-functional coordination are often still evolving. Here, those working directly with clients must make decisions within a complex and often ambiguous setting. Across both academic and practitioner literature, there is growing recognition that segmentation outcomes depend not only on how models are designed, but also on how they are understood and used in practice. While many studies focus on design features, performance metrics, or leadership perspectives, relatively little is known about how 32 segmentation works for the individuals responsible for making it happen. These individuals must often navigate tensions between standard frameworks and the unpredictable nature of account behaviour. Whether reassigning clients based on new signals, coordinating expectations with Sales colleagues, or shifting focus between proactive and reactive support, their work reflects a broader set of influences that lie outside formal strategy. For these reasons, this study takes a different approach. It focuses on how segmentation is experienced by those who apply it daily and explores how the model is shaped by organisational routines, client signals, and internal communication. The research is guided by the following central question: How do Customer Success professionals use segmentation to prioritise client engagement and allocate resources in mid-sized software companies? This main question is supported by two more specific inquiries: 1. How is segmentation applied in daily decision-making about account focus and resource distribution? 2. What factors support or limit the practical use of segmentation in post-sale engagement work? Together, these questions reflect the study’s interest in segmentation as a social and operational process, not just a technical design. Rather than testing model efficiency or comparing frameworks, the research asks how segmentation is adapted in real-time and how it influences, and is influenced by, everyday work. In doing so, it offers a deeper view into segmentation as it is practised, and aims to inform both theory development and the design of practical tools for those working in customer-facing roles. 2.3.4 Conceptual Framework Summary This study draws on five related ideas that, together, shape its conceptual foundation. First, long-term customer value offers a strategic perspective for deciding which clients should receive more time and attention. Second, resource allocation theory highlights 33 the importance of matching effort with value in a way that supports both efficiency and impact. Third, tiered service delivery provides a structure for applying segmentation through different engagement models, though this structure often breaks down when clients behave unpredictably or when roles and systems are misaligned. Fourth, the framework considers the role of internal integration. When segmentation logic is not embedded into the systems and workflows that teams use daily, its influence is often limited. Models may exist in planning documents, but if they are not visible within actual tools and processes, they are unlikely to shape behaviour in a consistent way. Finally, the framework includes the interpretive role of those who apply segmentation. These individuals make situational decisions, adjust service levels, and adapt models based on internal demands and client signals. Their judgement plays a critical role in how segmentation functions in practice. Together, these five strands provide a structured way to analyse segmentation as it is enacted in real organisational settings. They also inform how the findings of this study are interpreted, linking individual experience to broader questions of strategy, systems, and internal alignment. 3 Methodology 3.1 Research Philosophy and Approach This study is grounded in an interpretivist philosophical stance, which assumes that reality is not fixed or objectively observable, but rather constructed through the situated meanings, interactions, and contextual experiences of individuals. In this way of thinking, researchers don't try to find general rules. Instead, they try to understand how people understand what organizations are like from their own point of view (Saunders et al., 2009). This orientation is particularly appropriate for exploring how Customer Success professionals engage with segmentation strategies in real-world settings, where formally designed models must be translated into day-to-day practice under conditions of role ambiguity, cross-functional friction, and dynamic customer expectations. In mid- 34 sized B2B SaaS firms, where segmentation frameworks are often introduced to rationalise service delivery and optimise resource allocation, the actual implementation of such models is rarely standardised. Instead, it involves judgment, interpretation, and context-sensitive decision-making by CSMs operating at the interface between strategy and client engagement. The interpretivist approach provides a suitable foundation for a research design that prioritises context, depth, and the subjective construction of meaning over objectivity, replicability, or numerical precision. Since segmentation is embedded within routines, workflows, and cross-departmental dependencies, a philosophical approach that acknowledges the situated and constructed nature of practice is necessary. Customer Success professionals often operate under constraints of time, clarity, and alignment, meaning that their application of segmentation logic reflects not only the formal model itself, but also the organisational tensions, client relationships, and implicit norms within which they work. Interpretivism thus enables an inquiry into how these professionals navigate the interplay between strategic intent and operational reality. In alignment with this philosophical stance, the study adopts an inductive approach to theory development. Induction, in contrast to deductive reasoning, builds understanding from the ground up by identifying patterns and insights in the empirical data rather than testing predefined hypotheses. This approach supports the objective of this study, which is not to assess whether segmentation strategies are effective in general, but to explore how such strategies are interpreted, adapted, and enacted within the daily workflows of CSMs. While theoretical concepts such as Customer Lifetime Value, resource prioritisation, and service tiering inform the study’s design and sensitise the analysis, they are not imposed as fixed analytical categories. Instead, they provide a conceptual scaffolding that supports the emergence of grounded insights during data collection and interpretation. This use of inductive logic, as articulated in qualitative management research by Eisenhardt (1989), enables the researcher to remain open to unexpected themes, contradictions, and meaning structures that may arise from participants’ narratives. 35 This philosophical and methodological coherence justifies the selection of a single-case qualitative research design. The case organisation, a mid-sized European B2B SaaS firm, was chosen not only due to the researcher’s access but also for its relevance as a typical case within the SaaS sector. The firm operates with a formally defined segmentation framework, structured around variables such as Annual Contract Value, lifecycle stage, and product maturity. At the same time, it exhibits common features of mid-sized firms undergoing scale, including reliance on manual processes, emerging digital infrastructure, and internal variation in how segmentation is interpreted across roles. These characteristics make the organisation analytically valuable, offering a rich setting to explore the operationalisation of segmentation under resource constraints and evolving organisational conditions. While this is not a critical or extreme case, it is theoretically significant as it reflects challenges that are broadly generalisable to comparable SaaS environments (Yin, 2017). The researcher’s own background as a former CSM with industry-specific knowledge further positions this study within a reflexive interpretivist tradition. While this familiarity supported contextual understanding and analytical sensitivity, efforts were made to mitigate bias through structured reflections, coding annotations, and the consistent documentation of interpretive decisions during transcription and analysis. Reflexivity was treated not as a procedural requirement but as a methodological tool to examine and regulate the researcher’s influence on data interpretation. These practices are consistent with recommendations for ensuring credibility in qualitative inquiry (Saunders et al., 2009), particularly in single-case designs where immersion can both enable insight and introduce subjectivity. In essence, this study employs an interpretivist philosophy and an inductive approach to examine how segmentation is perceived and executed by Customer Success professionals. This methodological alignment supports a qualitative case study design that privileges the experiences, perspectives, and discretionary practices of those responsible for carrying out strategic segmentation within a real organisational context. It offers a framework through which the nuances, tensions, and adaptations of 36 segmentation can be analysed in light of their operational and interpretive complexity. By situating segmentation within the lived realities of CSMs, this approach enables a theoretically informed yet empirically grounded contribution to research on strategic resource allocation and Customer Success in SaaS environments. 3.2 Research Design This study adopts a qualitative, single-case embedded design to investigate how segmentation strategies are interpreted and applied by Customer Success professionals within a mid-sized B2B SaaS firm. The case study approach is particularly well-suited for research that seeks to examine contemporary phenomena within real-world contexts, where the boundaries between the phenomenon and its environment are not clearly defined (Yin, 2017). In this case, the phenomenon under investigation is not the company per se, but the way segmentation models are operationalised, adapted, or resisted in practice by the individuals responsible for customer engagement and success. A case study methodology allows for an in-depth, contextually grounded examination of these dynamics, especially where the focus is on understanding processes, interactions, and role-based variation rather than establishing causal relationships or statistical generalisation. The rationale for selecting a single mid-sized SaaS firm is both practical and theoretical. On a practical level, access to the organisation enabled close engagement with participants across multiple roles and hierarchical levels within the Customer Success function, which is often difficult to obtain in multi-firm designs. From a theoretical standpoint, the organisation exemplifies many of the structural and operational features that are commonly observed in mid-sized SaaS companies navigating growth and scale. These include a formal segmentation framework based on variables such as Annual Contract Value, lifecycle stage, and product configuration, combined with limited automation, diverse client portfolios, and evolving internal role clarity. The case was therefore selected as a theoretically typical instance of a firm attempting to bridge strategic intent and operational implementation within a resource-constrained environment (Eisenhardt, 1989; Yin, 2017). While the findings are not statistically 37 generalisable, they offer strong potential for analytical generalisation by identifying patterns that may be transferable to similar firms with comparable challenges and structures (Saunders et al., 2009). The design is classified as embedded because it incorporates multiple units of analysis within a single case boundary. While the organisation’s Customer Success department serves as the overarching unit, embedded sub-units are defined by individual roles, levels of seniority, and account segmentation responsibilities. These include professionals managing high-value enterprise clients with high-touch engagement strategies, mid-market accounts with hybrid servicing models, and lower-tier clients typically supported through digital or scaled programs. This structure allows for intra- organisational comparison, making it possible to detect both consistent patterns and divergent interpretations of segmentation logic across functional boundaries and account types. Such embeddedness strengthens the internal robustness of the case design by capturing the heterogeneity of segmentation application within a shared strategic framework, without compromising the depth and coherence that the single- case format affords. Participants were selected using purposive sampling based on their involvement with customer segmentation, strategic prioritisation, or decision-making related to account management and resource allocation. The objective was to recruit individuals who could offer first-hand, experience-based insights into how segmentation frameworks are applied in the context of Customer Success. A total of twelve semi-structured interviews were conducted with participants holding roles ranging from mid-level CSMs to senior managers and executives, all of whom were actively engaged in operationalising segmentation in their daily workflows. Sampling decisions were guided by theoretical rather than statistical logic, aiming to support the development of rich, transferable insights grounded in variation across roles, segments, and organisational responsibilities. The inclusion of participants from different levels and functional scopes enabled a form of internal triangulation, enhancing the credibility of the findings by 38 comparing narratives across embedded units within the case (Eisenhardt, 1989; Saunders et al., 2009). The embedded case design, combined with the interpretivist and inductive foundations of the study, supports the goal of developing context-sensitive theoretical insights into segmentation as a lived organisational practice. Rather than testing a predetermined model, the research seeks to uncover how segmentation frameworks are understood, appropriated, or adapted by Customer Success professionals navigating complex internal environments. The design enables not only the exploration of segmentation as a strategic logic, but also the investigation of how it intersects with day-to-day decisions, account variation, role expectations, and systemic constraints. Thus, the case study methodology serves as a research strategy and conceptual framework that supports the study's broader aim of illuminating how segmentation frameworks are enacted within the interpretive space between formal policy and organizational practice. The selected company is a European B2B SaaS provider offering professional development and HR technology solutions through a subscription-based platform. It delivers its services via annual license contracts bundled with onboarding and progress tracking features, aimed at enterprise and mid-market clients. At the time of the study, the company employed approximately 250 staff and served over 500 client organisations, which positions it within the mid-sized range of SaaS firms according to established industry classifications (McKinsey & Company, 2022). The organisation had recently implemented a tiered customer segmentation model based on Annual Contract Value and service complexity but was still developing the internal infrastructure needed to fully automate service delivery or enforce segmentation governance. This combination of formalised segmentation strategy, resource constraints, and organisational fluidity makes the firm a theoretically suitable case for examining how segmentation models are interpreted and applied in daily Customer Success operations within a growing SaaS context. 3.3 Data Collection 39 The primary data source for this study consists of twelve semi-structured interviews conducted with Customer Success professionals within a mid-sized B2B SaaS firm. The choice of semi-structured interviews as the core data collection method reflects the study’s interpretivist orientation and its aim to explore how segmentation strategies are subjectively interpreted and enacted in context. Semi-structured interviews offer a balance between thematic consistency and conversational openness, making them particularly well-suited for uncovering individual narratives, role-specific perspectives, and context-bound decision-making processes (Saunders et al., 2009). The interview format allowed for coverage of core topics related to segmentation application while also providing flexibility to pursue unanticipated insights, contradictions, or illustrative examples that emerged during the conversations. Participants were selected using purposive sampling, guided by the principle of theoretical relevance rather than statistical representativeness. The sampling criteria required that individuals be actively involved in segmentation implementation, client portfolio management, or strategic decision-making related to resource prioritisation. To ensure depth and diversity of perspective, participants were drawn from a range of roles and account responsibilities, including CSMs managing high-touch enterprise clients, hybrid mid-market portfolios, and low-touch digital segments. The sample also included individuals occupying senior and executive-level positions, providing insight into how segmentation is understood and communicated across hierarchical levels. The diversity of roles supported internal triangulation and comparative analysis across embedded units within the single-case design (Eisenhardt, 1989; Yin, 2017). Recruitment was facilitated through professional referrals and targeted outreach within the organisation. No incentives were offered, and participation was entirely voluntary. Each participant received a detailed explanation of the study’s purpose, confidentiality protocols, and their right to withdraw at any point. The interviews were conducted over secure video conferencing platforms and lasted between 35 and 60 minutes. All sessions were recorded with participant consent and transcribed with the help of an AI note taker, the accuracy of the transcription was 40 compared word by word with the recordings. The interviews followed a flexible guide that was developed based on the study’s conceptual framework, with questions grouped around core themes such as segmentation logic, prioritisation of engagement, and the challenges and adaptations associated with implementation. While the guide ensured thematic coherence across participants, it was also subject to iterative refinement. As interviews progressed, emergent themes and patterns informed subtle changes in emphasis, follow-up questions, and the framing of prompts. This iterative approach allowed the data collection process to remain grounded in the inductive logic of the study while accommodating the unique trajectories of individual interviews (Saunders et al., 2009). To enhance the credibility and transparency of the research process, the interviewer maintained structured reflections during data collection, noting interpretive challenges, thematic shifts, and contextual impressions directly within the working transcript matrix. These annotations helped track the evolution of the interview guide and supported critical reflection on the researcher’s background and its potential influence on participant interaction. Reflexivity is particularly important in interpretivist research, where the researcher is understood as a co-constructor of meaning rather than a neutral observer. By maintaining awareness of prior assumptions and systematically documenting interpretive decisions, the study aimed to ensure that findings were grounded in participant narratives rather than shaped by pre-existing biases. All transcripts were anonymised, and pseudonyms were assigned to each participant to preserve confidentiality. The interview data formed the empirical core of the study and were subsequently analysed through thematic coding, as outlined in the following section. Ethical protocols were upheld throughout the research process in accordance with established guidelines for organisational research, ensuring participant dignity, voluntary consent, and data protection. 3.4 Data Analysis Approach 41 The data collected through semi-structured interviews were analysed using thematic analysis, a flexible and systematic method well-suited to interpretivist, qualitative research. Thematic analysis facilitates the identification, examination, and interpretation of patterns of meaning (themes) across a dataset, providing a structured yet adaptable framework for engaging deeply with participant narratives (Braun & Clarke, 2006). Given the study’s inductive orientation and the focus on how Customer Success professionals interpret and apply segmentation strategies, thematic analysis was particularly appropriate for uncovering insights that were both theoretically grounded and emergent from the data itself. The analysis followed the six-phase model proposed by Braun and Clarke (2006), encompassing data familiarisation, initial code generation, theme development, theme review, theme definition, and the final construction of the analytic narrative. During the familiarisation phase, transcripts were read repeatedly to develop a holistic understanding of participant accounts and to identify recurring themes. Early interpretive reflections were noted directly within the transcript matrix as margin comments and in-code annotations. While no formal analytic memoing protocol was employed, these annotations served to capture context-sensitive interpretations and potential thematic patterns as they emerged. This process enabled the researcher to document the evolving sense-making around segmentation practices across different participant roles and segments. Coding was conducted manually using a hybrid approach that combined inductive (data- driven) and deductive (theory-informed) strategies. Inductive coding enabled the identification of themes that were grounded in participants’ language, priorities, and sense-making processes. At the same time, deductive attention was given to theoretical constructs such as resource allocation logic, engagement tiering, and the strategic– operational interface of segmentation. Open codes were initially generated to capture fine-grained aspects of participant statements, which were then grouped into axial codes and higher-order themes through iterative comparison and constant refinement. This process reflected the goal of thematic coherence while maintaining sensitivity to 42 variation across participant roles, client segments, and organisational conditions (Eisenhardt, 1989; Saunders et al., 2009). Themes were developed by clustering related codes and reviewing them against both the full dataset and the study’s conceptual framework. Candidate themes were evaluated for internal consistency, external distinction, and explanatory value. Particular attention was paid to instances where participant accounts diverged, contradicted each other, or revealed underlying tensions within the same organisational setting. These variations were treated as analytically valuable rather than anomalous, as they helped reveal the interpretive flexibility and practical challenges associated with implementing segmentation frameworks. The use of embedded units within the case design (Yin, 2017) further supported the comparative analysis of how segmentation was understood and applied across different roles and segments. Throughout the analytical process, reflexivity was actively maintained to ensure that the themes developed were genuinely grounded in participant narratives and not shaped by the researcher’s prior assumptions or professional background. Reflections were recorded throughout analysis, although no formal memoing protocol was employed. Notes were made directly alongside codes and themes to maintain transparency and ensure participant narratives remained the central analytical focus. This self-reflective practice contributed to transparency and analytical rigor, aligning with the study’s interpretivist epistemology and qualitative research standards (Saunders et al., 2009). While data analysis software was not employed, visual mapping techniques and iterative code categorisation were used to support analytical clarity and theme coherence. The final thematic structure was reviewed in relation to the study’s research questions and theoretical underpinnings. It was also validated through partial member checks, in which selected participants were invited to comment on preliminary interpretations to confirm their alignment with lived experience. These checks contributed to the study’s credibility by ensuring that findings resonated with those directly involved in segmentation implementation, thereby enhancing the trustworthiness of the empirical account. 43 In short, the way we looked at the big picture in this study gave us a strong and flexible way to look at complicated, detailed data. By combining inductive openness with deductive theoretical awareness, the analysis captured both shared patterns and contextual variation in how segmentation strategies are interpreted and applied within a mid-sized B2B SaaS organisation. The results generated from this process provide a grounded empirical foundation for the discussion of findings and their theoretical and practical implications in the chapters that follow. 3.5 Validity and Reliability This study applies qualitative research standards appropriate to its interpretivist and inductive approach. Instead of focusing on statistical validity, trustworthiness was addressed through credibility, transferability, dependability, and reflexivity (Saunders et al., 2009). Credibility was supported through repeated review of interview data, iterative theme development, and selected member validation. Patterns were compared across roles and account segments to strengthen internal consistency (Yin, 2017). Transferability was enhanced by providing detailed descriptions of the case organisation, segmentation logic, and participant roles, allowing readers to assess the relevance of findings to their own contexts (Eisenhardt, 1989). Dependability was supported by systematically documenting adjustments made to the interview guide and coding structure as the research progressed. Although coding was conducted manually, the use of structured annotations and in-text reflections helped maintain consistency and transparency throughout the analysis process (Braun & Clarke, 2006). Reflexivity was actively sustained through ongoing critical reflection on the researcher’s prior professional background and its potential influence on data interpretation. These steps helped ensure that emerging themes were grounded in participant narratives rather than shaped by researcher assumptions, even in the absence of a formal audit trail or dedicated memoing protocol. 44 4 Findings 4.1 Interview Sample Overview and Case Context To protect the anonymity of participants while providing sufficient contextual detail, all interviewees are presented using pseudonyms. Each pseudonym corresponds to a unique individual whose role, segment focus, and seniority level were relevant to the scope of this case study. Table 1 summarizes participant roles and backgrounds as reported or inferred at the time of the interview, offering clarity on the variation of experience and engagement levels among the Customer Success Managers included in the study. This anonymization approach follows ethical recommendations for qualitative research outlined by Saunders et al. (2009). Industry background is included where relevant to reflect the professional diversity of participants, which in some cases informed their perspective on segmentation logic and prioritisation. Table 1. Interview Participant Overview Pseudonym Role/Title Segment (Low, High, Mixed) Seniority Years of Experience Industry Background / Notes Anna Senior Customer Success Manager High Senior 15+ Coaching, Digital L&D Beatrice Senior Customer Success Manager Mixed Senior 20+ Digital learning, eLearning, Commercial Ops Carlos Senior Customer Success Manager High Senior 15+ Sales, Hospitality, Coaching Dalia Customer Success Manager Mixed Mid-level 8+ Digital coaching, Project Management Eva Customer Success Manager Mixed Mid-level 10+ Psychology, Coaching 45 Felix Customer Success Manager Low Junior 3 Business Coaching, Graduate-level Greta Customer Success Manager Mixed Mid-level 10+ International SaaS, CS/Marketing Hannah VP Global Customer Success Company- wide Executive 15+ Strategic leadership in coaching Isabel Customer Success Manager High Mid-level 10+ HR Consulting, Coaching Jonas Senior Customer Success Manager (Team Lead) Mixed Senior (Team Lead) 12+ SaaS, B2B Ops, Automotive Klara Senior Customer Success Manager Mixed Senior 10+ Digital coaching, Wellbeing Lukas Enterprise Customer Success Manager High Senior 8+ Enterprise SaaS, Analytics The empirical foundation of this study consists of twelve semi-structured interviews conducted with Customer Success Managers (CSMs) from a single mid-sized B2B SaaS company. These interviews form the primary data source in this interpretivist, single- case embedded study, which seeks to understand how segmentation strategies are operationalized by CSMs within their daily workflows. Consistent with the methodological rationale outlined in Section 3.1, this interpretivist lens emphasizes the subjective meaning-making processes of participants and enables rich, context-specific insights into the ways segmentation is applied, adapted, or resisted in practice (Saunders et al., 2009). The interview sample reflects substantial internal variation in terms of role seniority, client portfolio characteristics, and degree of exposure to segmentation logic. Of the twelve participants, ten held mid- or senior-level CSM roles directly managing customer accounts, while two occupied leadership positions with greater strategic oversight. This intra-organizational diversity aligns with the embedded case study design https://www.zotero.org/google-docs/?V7TYqV https://www.zotero.org/google-docs/?V7TYqV 46 described in Section 3.2; wherein multiple units of analysis are situated within a unified organizational context to facilitate comparative insight (Yin, 2018). Participants collectively managed a wide range of customer segments. Some were responsible for high Annual Contract Value enterprise clients requiring personalized engagement, while others oversaw lower-tier accounts typically served through digital- first or scaled models. Several interviewees worked across mixed portfolios, navigating both high- and low-touch engagement strategies simultaneously. This range of client segments including low-tier, high-tier, mixed, and company-wide responsibilities ensured that the data captured both ends of the segmentation spectrum and the tensions in between. Such variation supports the pursuit of theoretical saturation, where themes were observed to recur and deepen across functionally distinct participant roles (Eisenhardt, 1989). Importantly, this variation in account responsibility also surfaced differences in how segmentation was interpreted and enacted. CSMs supporting high-tier clients often described strategic customization and high stakeholder involvement, whereas those managing scaled accounts highlighted automation, content-based support, and capacity challenges. These contrasting experiences not only reflect the diversity of Customer Success practices in a segmented environment but also provide rich empirical grounding for addressing both sub- questions of the study. The first sub-question focusing on how CSMs prioritize accounts and allocate time was illuminated through accounts of day-to-day decision-making and the operational logic behind engagement choices. The second sub-question examining challenges and enablers was supported by insights into segmentation clarity, tool usage, role boundaries, and internal alignment. As discussed in the sampling strategy (Section 3.2), participant selection was purposive and aimed at capturing meaningful variation within a single organizational setting. The shared company context ensured consistency in segmentation structure and strategic objectives, while the diversity of roles allowed for intra-case comparisons that enhance the interpretive depth and credibility of findings. This design is methodologically aligned with the study's epistemological stance, allowing the research to explore not only what https://www.zotero.org/google-docs/?GgXl9X 47 segmentation means in theory but how it is operationalized by professionals navigating real-world trade-offs in resource-constrained environments. Overall, the sample composition provides a robust foundation for exploring the research question and its sub-components. It allows for a multi-faceted view of segmentation in action, balancing strategic intention with operational realities, and contributes meaningfully to the theoretical and managerial insights developed in the chapters that follow. The following sections present the findings thematically, beginning with how segmentation is applied in practice and progressing through prioritization strategies, automation models, and operational challenges. 4.2 Understanding Segmentation in Practice This section is divided into two parts. Section 4.2.1 explores how Customer Success Managers understand and apply the company’s formal segmentation models. Section 4.2.2 examines how they adjust or reinterpret these models in response to practical demands and contextual pressures. 4.2.1 Clarity and Use of Formal Segmentation Models Customer segmentation is theoretically designed to group clients by shared attributes that indicate service needs, potential value, or strategic importance. In the case of this mid-sized B2B SaaS company, segmentation is formally implemented using thresholds such as ACV, account maturity, and in some instances, product usage or implementation complexity. However, interviews revealed significant variation in how these segmentation principles are understood, internalized, and applied by Customer Success Managers. While all participants recognized that segmentation exists in some form, the clarity, relevance, and operational value of the model varied considerably across roles, portfolio types, and seniority levels. Some CSMs could clearly articulate the thresholds that determined whether an account was considered enterprise, mid-market, or low tier, while others only vaguely referenced tier names or expressed confusion about the criteria being used. This inconsistency points to a misalignment between the strategic 48 intent of segmentation and its practical communication and execution. This misalignment can have significant consequences, potentially leading to inconsistent customer experiences and inefficient resource allocation. If CSMs lack a clear understanding of segmentation, they may struggle to apply it effectively in their daily workflows, resulting in ad-hoc decision-making and a diluted impact of segmentation on overall customer management. As Dalia noted, “I feel the segmentation is maybe not the key focus of the company anymore... it's become less rigid”, indicating a potential drift from strategic intent. This highlights the importance of clearly communicating the segmentation strategy and providing adequate training to CSMs, ensuring that they understand its purpose and how to apply it consistently (Payne & Frow, 2005). Several CSMs working with high-tier portfolios demonstrated greater awareness of segmentation thresholds, often citing ACV figures such as 50K or 70K as boundaries for tier assignments. These participants tended to report a stronger understanding of their account categorization and its implications for engagement strategy. For example, one senior CSM, Greta, noted, “My tier one clients... I have the most internal and external touchpoints with by far”, illustrating how segmentation directly informs the allocation of intensive resources to high-value accounts. Conversely, CSMs managing scaled or low-tier clients reported less clarity about the segmentation model, fr