Digital Twins: Telecom Industry’s Next Strategic Advantage

What is a Telecom Digital Twin?

Twins have always had a certain flair for mischief—confusing neighbours, swapping places in the class, and pulling off more pranks than the rest of us combined. It’s easy to get caught up in the guessing games they invite, but their real magic lies in being almost identical, yet delightfully unique. Picture one twin advising, “Skip that shortcut— or you trip over a rock,“ only to be spectacularly ignored by the other and regret it moments later. That, in essence, captures the spirit of a digital twin and its real-world counterpart—only without the birthday cake and matching sweaters! For Digital Twin, the game is not deception, but precision and clarity.

Digital twins are virtual representations of physical entities, operational processes, or datasets. Their purpose is to simulate the behaviour of these entities by capturing real-time performance data, predicting problems, and helping identify remedies and optimisation actions. In the future, shaped by digital twins, the line between imagination and reality will blur, empowering service providers to predict, optimise, and innovate like never before.

Key Enablers: Domain-specific knowledge, IoT, connectivity, cybersecurity, data fabric, cloud computing, processes and practices, visualisation tools, UI, and AI/ML.

Digital Twin in Telecom Ecosystem

The telecom industry presents unique challenges that make digital twins particularly valuable. Unlike other industry sectors with relatively static infrastructure, telecom networks are dynamic, heterogeneous, and constantly evolving.

Unlike digital twins in other industries, telecom digital twins deliver competitive differentiation by:

Managing the dynamic complexity of connectivity across heterogeneous environments
Continuously adapting to reflect ever-expanding infrastructure elements, RANs, edge computing sites, and millions of connected devices
Providing comprehensive visibility across the entire telecom stack to identify opportunities invisible to competitors

Digital twins can automatically reconfigure network assets based on real-time data. For example:

Example scenario: A router’s temperature sensor reports “85°C.” → Data is sent via MQTT to the cloud → Digital twin updates show the router “heating up.” → Alert is triggered, recommending cooling or load balancing.

Strategic Benefit: Predictive intelligence enables operators to foresee outages, simulate peak traffic loads, and test technology rollouts while minimising risks and customer-impacting downtime.

Strategic Advantage: How Telco BSS Vendors Capitalise on Digital Twin Technology

While modelling physical infrastructure and supporting OSS functions is valuable, for BSS vendors like us at Covalense Digital, the true strategic advantage of telecom digital twins emerges through concrete applications that extend beyond infrastructure to deliver measurable business outcomes across the entire telecom value chain:

1. Customer Journey Simulation with Real-Time Insights

Abstract Concept: Mapping customer behaviours across billing, support, and services.

Actionable Implementation:

Create a dynamic twin of 100,000 postpaid customers across multiple segments, simulating their interactions with self-service portals, app usage patterns, and payment behaviours.
Feed the twin with live data from CRM, billing systems, app analytics, and NPS scores to forecast experience friction points

2. Billing System Optimisation Through Scenario Testing

Abstract Concept: Testing new plans and simulating billing scenarios.

Actionable Implementation:

Build a billing system twin that ingests 1% of production traffic to simulate new tariff structures before full deployment.
Create “what-if“ environments where rating and charging components can be stress-tested with fabricated usage data simulating 150% of peak traffic volumes.

3. Predictive Customer Support Through AI-Enhanced Simulation

Abstract Concept: Predicting customer needs to drive personalised offers.

Actionable Implementation:

Deploy a support centre digital twin that correlates network incidents, billing cycles, product launches, and historical support volumes.
Simulate call centre load with 96% accuracy by up to 14 days in advance, allowing optimal agent scheduling and training.

4. Product Launch Simulation with Market-Tested Results

Abstract Concept: Modelling new service uptake and provisioning needs.

Actionable Implementation:

Create a comprehensive launch twin incorporating provisioning systems, billing platforms, CRM impacts, and network capacity requirements.
Simulate customer adoption curves based on pricing elasticity, competitive offerings, and targeted marketing spending across different segments.

5. Revenue Assurance & Fraud Detection Through Pattern Recognition

Abstract Concept: Identifying potential revenue leaks and fraud points.

Actionable Implementation:

Deploy a financial twin that models the entire revenue pipeline from order to cash, continuously monitoring for anomalies, leakage points, and fraud signals.
Process 100% of billable events through parallel validation algorithms that flag potential issues before they impact financial systems.

6. Service Personalisation at the Individual Customer Level

Abstract Concept: Predicting customer needs to drive personalised offers.

Actionable Implementation:

Build a personalisation twin that creates a virtual profile for each high-value customer, simulating their response to different offer combinations, timing, and communication channels.
Continuously refine offer algorithms based on customer responses, creating a feedback loop that improves prediction accuracy.

7. Risk-Free Change Management Through Virtual System Testing

Abstract Concept: Testing upgrades and migrations in a safe environment.

Actionable Implementation:

Create system twins of critical BSS components replicating production environments with absolute data volumes and transaction loads.
Simulate 12-18 months of accelerated operations in the twin environment to identify the long-term impacts of changes, especially in rating, charging, and bill calculation modules.

8. SLA Monitoring and Optimisation with Predictive Intervention

Abstract Concept: Simulating service delivery to strengthen SLA compliance.

Actionable Implementation:

Deploy an operations twin that continuously monitors service quality metrics against contracted SLAs, flagging potential breaches before they occur.
Create predictive maintenance schedules based on historical performance patterns and real-time system health indicators.

From Infrastructure to Business: How BSS Vendors Turn Digital Twin Insights into Competitive Advantage

Let’s examine another powerful real-life application where digital twins create a decisive market advantage: A telecom operator preparing to launch enterprise 5G private networks uses its infrastructure digital twin to model how the service will perform technically across various deployment scenarios. Simultaneously, their BSS digital twin simulates how the same service would be billed, bundled, and supported across different enterprise customer segments—from manufacturing to healthcare to retail.

Strategic Result: The operator achieves faster, risk-free service rollouts tailored to specific customer needs, capturing market share while competitors are still in technical testing phases. This unified approach, where telecom digital twins inform business operations, represents the next frontier in competitive advantage.

Case Studies: Digital Twins Delivering Strategic Advantage in Telecom

Ericsson: Reduced design time by 50% and improved maintenance, reducing the need for site revisits from one in ten to one in one thousand¹; AR-enabled twins allow technicians to “see through walls.“
Nokia: Reduced deployment cycle time by 30%² and decreased unplanned downtime with AI-driven predictive maintenance.
AT&T: Processed 2.8 trillion data rows 3x faster at 60% lower cost; improved throughput by 18% without additional spectrum³.
Vodafone UK: Mapped 40 million environmental features, reducing site acquisition costs by 31% with 2-meter accuracy⁴.
Amdocs: Has been active in using AI coupled with machine learning to create customer experience digital twins with an eye on customer journey simulations and proactive issue detection and resolution.

Challenges and Considerations

Digital twins are certainly game changers, but not without challenges. Let’s look at some key factors to consider before committing:

Data Integration & Accuracy: Poor quality inflates maintenance costs by 25%⁵; requires rigorous governance frameworks.
Cybersecurity & Privacy: 57%⁶ of IoT devices are vulnerable to man-in-the-middle attacks, demanding security by design.
Legacy Systems and Fragmented Infrastructure: Retrofitting 20+⁷ year-old systems requires modernisation; start with high-value assets.
Cost vs. ROI: Organisations report 19% cost savings and 22% ROI annually; the phased approach accelerates value⁸.

The Future Outlook: How Digital Twins Will Transform Telecom Strategy

Now that we’ve explored how digital twins are revolutionising telecom operations today, let’s envision the next evolution of this strategic technology and how it will reshape the competitive landscape:

Autonomous Networks: Telecom will leverage live simulations to create self-optimising, self-healing networks, minimising manual interventions.
Smart City: As cities get smarter, telecom operators with digital twins in their arsenal will be the backbone, seamlessly managing connectivity, traffic, energy, and public services.
Ecosystem Twins: Cross-industry “systems of systems“ connecting telecom twins with the automotive and utility sectors to optimise autonomous vehicles and balance power consumption.
Quantum-Enhanced Twins: Leveraging quantum computing for unprecedented simulation complexity, enabling advanced RF optimisation and massive-scale customer behaviour modelling.

Based on industry analysis, strategic implementation of digital twins will increase operational efficiency in telecom organisations by 30-40%, and we expect it to improve customer satisfaction metrics by 20-25% compared to competitors without this capability. Unsurprisingly, the digital twin market in the telecom sector will reach $1.6 trillion by 2031, growing at a CAGR of 22.0% over the forecast period as operators recognise the competitive necessity of this technology⁹.

Conclusion: Digital Twins as the Telecom Industry’s Strategic Imperative

Digital twins have evolved from a futuristic concept to a strategic imperative for the telecom industry. As 5G, edge computing, and IoT complexities grow, virtual simulation capabilities have become essential for competitive advantage. Market leaders will be those who create virtual replicas that not only mirror reality but shape it through predictive intelligence. The question isn’t whether to adopt digital twins, but how quickly you can implement and integrate them before they transition from differentiator to industry standard.

Ready to explore how digital twins can transform your telecom operations? Contact our digital transformation experts at reachus@covalensedigital.com for a strategic consultation.

Author

Agniv Banerjee, Associate Solution Engineer

A passionate chronicler of innovation in the telecommunications sector. Agniv, an avid reader and a tech enthusiast, maintains a strong interest in the evolving landscape of Cloud Engineering, TM Forum Standards, 4G LTE networks, and Billing.