A New Way of Designing Services
The Trace-Based Services Management approach
In today's complex service ecosystems, organizations constantly search for more effective approaches to understand, manage, and improve their service offerings.
Traditional methods like Customer Journey Management (CJM) is a trend now, focusing on predefined touchpoints and idealized linear paths.
However, these approaches often fall short in capturing the true complexity and emergent nature of modern service interactions.
This article introduces Trace-Based Service Management (TBSM), developed by the consultant and professor Ricardo Martins, as a superior alternative that fundamentally transforms how we conceptualize, monitor, and enhance service experiences. TBSM leverages knowledge coming from Lean Thinking, Six Sigma, Theory of Constraints, Process Mining, Handoff Networks, Network Analysis, Theory of Signaling, Digital Twins, Internet of Things and Visual Computing to fill the gaps left by Journey Management approach.
By focusing on the actual evidence and traces left during service delivery rather than preconceived notions of how services should unfold, TBSM offers a more authentic, adaptive, and data-driven approach to service excellence. When further empowered by digital twin technology, TBSM creates unprecedented opportunities for service innovation and optimization that traditional journey management approaches simply cannot match.
What is Trace-Based Service Management (TBSM)?
Trace-Based Service Management represents a paradigm shift in service management philosophy. While traditional approaches attempt to design and control customer journeys along predetermined paths, TBSM acknowledges the inherent complexity and unpredictability of service systems. Instead of imposing idealized journey maps, TBSM focuses on the collection, analysis, and utilization of traces—the tangible and intangible evidence left behind during service interactions.
This approach recognizes that every service experience generates a wealth of data points and signals that can be captured and analyzed. Rather than assuming how customers should move through a service, TBSM examines how they actually navigate, interact, and experience service offerings in reality. It embraces the messiness and non-linearity of authentic service experiences, providing insights based on empirical evidence rather than idealized models.
TBSM works bidirectionally—it not only captures what has already happened but also enables predictive capabilities to anticipate service outcomes based on emerging trace patterns. This creates a dynamic learning system that continuously improves based on real-world service evidence rather than theoretical journey frameworks.
Understanding service traces: the foundation of TBSM
Service traces constitute the fundamental elements that enable TBSM. These traces exist in multiple dimensions and provide rich insights into service execution and experience:
Temporal traces
Timestamps, duration measurements, waiting periods, response times, and service completion metrics that reveal the chronological aspects of service delivery.
Quantitative traces
Volume indicators, frequency measurements, repetition patterns, and numerical evidence of service utilization and delivery.
Experiential traces
Gap indicators between expectations and actual delivery, satisfaction signals, emotional responses, and perception markers throughout the service experience.
Communication traces
Channel selections, communication patterns, message content, frequency, and tone of interactions between providers and recipients.
Behavioral traces
Navigation patterns, decision points, abandonment signals, and other indicators of how people interact with service systems.
Physical traces
Movement patterns, space utilization, environmental changes, and tangible artifacts generated during service delivery.
Transactional traces
Financial records, resource allocation signals, exchange documentation, and value transfer evidence.
Digital traces
Click patterns, dwell time, interface interactions, and other electronic footprints left in digital service environments.
Social traces
Word-of-mouth indicators, recommendations, social media mentions, and influence patterns related to service experiences.
Relational traces
Trust indicators, loyalty signals, relationship development patterns, and connection evidence between service providers and recipients.
These traces provide a multidimensional view of service reality that far exceeds the insights available through conventional journey mapping. By systematically capturing, analyzing, and interpreting these traces, organizations gain unprecedented visibility into their service operations and customer experiences.
TBSM empowered by digital twins
The integration of digital twin technology with Trace-Based Service Management creates a powerful synergy that exponentially increases the value of both approaches. Digital twins—virtual replicas of physical entities, processes, or systems that can simulate, predict, and optimize performance—provide the ideal platform for operationalizing TBSM at scale.
When service traces feed into digital twin environments, organizations can:
Capture and visualize traces in real-time
Digital twins transform abstract traces into visible, intuitive representations that reveal patterns and anomalies that would otherwise remain hidden.
Conduct predictive simulation
By analyzing historical trace patterns, digital twins can forecast service outcomes under various scenarios, enabling proactive optimization.
Enable dynamic adaptation
Service configurations can be automatically adjusted based on emerging trace patterns, creating self-optimizing service systems.
Integrate multidimensional traces
Digital twins can synthesize diverse types of traces into coherent models that reveal complex interdependencies within service systems.
Facilitate safe experimentation
New service concepts can be tested virtually using trace-based simulations before implementation, reducing risk and accelerating innovation.
Create continuous feedback loops
Real-world trace data constantly refines the digital twin, which in turn generates insights to improve the actual service, creating an ever-evolving improvement cycle.
This powerful combination creates a service management approach that is simultaneously data-driven and future-oriented. The digital twin becomes both a repository of trace knowledge and a laboratory for service innovation, providing capabilities far beyond traditional management frameworks.
Why TBSM is superior to Customer Journey Management
The superiority of Trace-Based Service Management over traditional Customer Journey Management becomes evident when comparing their fundamental approaches and capabilities:
Evidence-based vs. Assumption-based
While CJM relies heavily on predetermined assumptions about how customers should move through service touchpoints, TBSM builds understanding from actual evidence left during service interactions. This grounding in reality rather than idealized models provides more accurate insights for decision-making.
Complexity-embracing vs. Linearity-focused
CJM typically simplifies service experiences into linear, sequential journeys with defined beginnings and endings. TBSM acknowledges the non-linear, complex, and sometimes chaotic nature of real service interactions, capturing loops, deviations, and unexpected pathways that journey maps miss.
Emergent vs. Predetermined
Journey management approaches often attempt to design experiences in advance and then measure compliance. TBSM recognizes that many service aspects emerge organically through interaction and focuses on understanding these emergent patterns rather than imposing predefined structures.
Holistic vs. Touchpoint-centric
While CJM concentrates on optimizing discrete touchpoints along a journey, TBSM captures the interconnections, ripple effects, and systemic aspects of service experiences that exist between and beyond formal touchpoints.
Adaptive vs. Static
Journey maps quickly become outdated as service realities evolve. TBSM continuously updates understanding based on new trace evidence, creating an adaptive knowledge base that evolves with changing service patterns.
Invisible-revealing vs. Surface-focused
CJM primarily focuses on visible aspects of service interactions. TBSM uncovers hidden dimensions like emotional responses, cognitive processing, and subtle behavioral signals that don't appear on traditional journey maps.
Predictive vs. Descriptive
While journey management is primarily descriptive and retrospective, TBSM with digital twins enables predictive capabilities that anticipate service outcomes based on emerging trace patterns.
Individualized vs. Generalized
Journey maps typically represent generalized customer experiences. TBSM can capture individual-level trace variations, enabling more personalized service optimization.
This superiority translates into tangible benefits: more accurate service insights, faster adaptation to changing conditions, more authentic customer understanding, and ultimately more effective service improvement initiatives.
Conclusion
Trace-Based Service Management represents a fundamental advancement in how organizations understand and enhance service experiences. By shifting focus from idealized journey maps to actual service traces, TBSM provides a more authentic, adaptive, and effective approach to service excellence. The integration with digital twin technology further amplifies these advantages, creating unprecedented capabilities for service simulation, prediction, and optimization.
As service ecosystems grow increasingly complex and customer expectations continue to evolve rapidly, the limitations of traditional journey management approaches become more apparent. TBSM offers a powerful alternative that embraces complexity, builds on empirical evidence, and enables dynamic adaptation to emerging service realities.
Organizations seeking to transcend the limitations of conventional service management would be well-advised to explore the transformative potential of Trace-Based Service Management. By capturing, analyzing, and leveraging the multidimensional traces generated during service delivery, they can develop deeper understanding, make more informed decisions, and create more responsive and effective service systems that truly meet the needs of modern customers.