Creating customer value from data: foundations and archetypes of analytics-based services.

Digital transformation provides organizations with fresh avenues to broaden their service offerings for gaining market superiority. A frequent approach for generating novel customer benefits involves deploying services that utilize data-driven methods. These specific offerings empower clients to improve their choices and address intricate challenges. No prior work had resolved the need for a rigorous framework defining this emerging service category. That uncertainty drove the current investigation into the structural foundations of these data-centric solutions. Existing literature provides limited guidance on how firms should intentionally launch these services to enhance their portfolios. This gap motivated a deeper look at the underlying mechanics of service design in digital environments. Scholars have yet to establish a clear taxonomy for these modern business tools.

The primary aim of this study is to provide a profound conceptualization of analytics-based services within the modern market. Researchers seek to address the lack of actionable insights regarding the purposeful establishment of these services. The project investigates how organizations can effectively enrich their service portfolios using data-driven methods. This work addresses the uncertainty surrounding the structural foundations of this novel service category. The authors intend to identify generic archetypes that define the objectives and characteristics of these offerings. By doing so, they hope to assist firms in making informed decisions about their service strategies. The study also explores the factors that influence transitions between different service models. This effort aims to advance the theorizing process for data-centric business solutions.

The review approach involves a dual-method design to categorize and characterize data-driven service offerings. Researchers conducted a cluster analysis on a sample of 105 distinct service examples. This quantitative phase grouped services based on their functional attributes and underlying logic. A revelatory case study provided qualitative validation for the identified groupings. Investigators triangulated these two data sources to ensure robust findings. The process focused on unveiling the specific objectives and operational features of each category. Experts also examined the decision-making criteria for selecting and evolving these service models. This systematic investigation provides a clear framework for understanding how firms structure their data-centric portfolios.

Key findings from the literature reveal four generic archetypes that define the landscape of data-driven service offerings. The analysis of 105 examples demonstrates that these services vary significantly in their complexity and customer impact. Researchers identified specific factors that shape the choice of an appropriate archetype for a given market context. The study highlights how these factors influence the transition between different service models over time. Findings suggest that firms can use these archetypes to systematically enrich their existing service portfolios. The data indicates that clear service objectives are vital for successful implementation. The investigation clarifies the relationship between analytical methods and the resulting customer value. These results provide a foundation for theorizing about the evolution of digital service ecosystems.

The authors propose a classification system consisting of four distinct archetypes for data-driven service offerings. These categories provide a structured way for firms to evaluate their current market position. Managers can utilize these findings to align their service objectives with specific organizational capabilities. The study highlights the importance of strategic decision-making when selecting an appropriate service model. Transitions between different archetypes represent a viable path for long-term portfolio evolution. This research advances the theoretical understanding of how data creates value in professional settings. Practitioners gain a clearer roadmap for implementing these services within their existing business structures. The findings offer a systematic approach to identifying new opportunities for competitive growth.