telecom customer experience case study

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“We decided on Medallia because it was the best combination of the level of detail we wanted and the ability to integrate it into our systems.”

Timm Degenhardt

CMO Sunrise Communications

The Challenge

Prior to the implementation of Medallia, employees across the $2B revenue telecommunications company were flying blind in terms of the quality of the customer experience. Financial metrics provided some guidance, but these were trailing indicators—by the time they showed what had happened, it was already too late to do anything about it.

The Solution

Sunrise now gains a composite view of all key customers touchpoints, as well as the relationship status with customers that do not frequently interact with its employees. This view is empowering employees to identify areas needing improvement, take action to make those improvements, and the measure effec-tiveness of such changes. As Chief Customer Experience Officer Max Nunziata notes, “Medallia is like a compass. We use it in order to make decisions of the day or decisions in our strategic planning. The power of it is that you slice and dice it in the way you want.

Sunrise, now armed with data revealing valuable insights about its customers, is empowered to take action across the business in pricing, customer plans, marketing, network quality, and service to improve experiences.

Real-time analytics on eighteen different surveys empower Sunrise employees, from the frontline to the C-suite, to confi-dently prioritize areas to focus on for the biggest impact on the customer experience.

Service Innovation Guided by VoC

Sunrise also uses Medallia to adapt its service offerings to the customer’s needs. For instance, after analyzing initial CX feedback, the company decided to abolish contract durations, allowing customers to change or cancel their service whenever they like. Customer inflow soon increased by 30% year over year, and the NPS of the “Freedom” product is now 40 points higher than that of legacy contract offerings.

A further move Sunrise has made as a result of having VoC embedded in their organization is a shift in focus from giving the best offers primarily to new customers, to a philosophy that values loyalty. Existing customers not only get the same offers that new customers get when they renew a contract, but they also benefit from rewards that increase in value every year they stay—for example free video subscriptions or free roaming during holidays.

The Results

These moves have resulted in Sunrise’s NPS numbers skyrocketing across the board: the NPS of its call center has increased by 22 points, its new customer “Welcome” NPS has increased by over 30 points, and its relationship survey NPS has increased by 15 points.

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Reimagining Telefonica customer experience for growth

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Transformation

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BEST-IN-CLASS NO LONGER HAS A CLASS: Traditional telcos are almost indistinguishable—same services, different day—resulting in stagnant growth. Customers are constantly shopping around for what’s next, thanks to competition from born-digital market entrants and a growing demand for new services and immersive experiences. In an age of unprecedented disruption where brands cater to customers, telcos must adapt quickly or risk losing even long-time loyalists.

NOW OR NEVER: Telefónica has millions of subscribers. But to retain their business and attract new customers, the brand needed to understand them as individuals. Diving into their digital routines and creating a seamlessly personalized omnichannel experience based on their needs was imperative to growth. Rising to the challenge, Telefónica set out to become the telco of the future through digital transformation—from phone and data to smart services.

This enterprise transformation called for a commercial refocus: Investing in digital solutions and insight discovery would not only improve the customer experience, it would also help Telefónica identify cost efficiencies and build new services to keep customers coming back for more.

Strategy and solutions

ONE IN A MILLION: Analysis of the customer and prospect sales journey revealed an opportunity to optimize digital marketing and sales. Telefónica partnered with Accenture to design a secure digital environment that helped them anticipate customer expectations and quickly adapt to changing market conditions. Across every channel, brand offers and interactions are now customized to individual preferences.

Continuous adaptation is also key. End-to-end analytics allow for better understanding of the entire customer journey, from lead generation and website traffic to conversion, provision and service. This increased insight helps to convert leads into sales more efficiently, with high levels of customer satisfaction.

"This project shows how Accenture is collaborating with Telefónica to build capabilities that create remarkable digital experiences, resulting in an enhanced digital relationship between Telefónica and its clients. This is a big step in transforming the way we connect with the customer to compete in the market today." — NEREA IDIRIN , Managing Director, Accenture Consulting

Digital marketing

Focusing on SEO, SEM, social campaigns, and paid media, Accenture Interactive brought best-in-class digital tools and design to create a holistic customer experience across all touchpoints.

Digital experience

Using a service design approach, Accenture’s Fjord team aligned Telefónica’s digital properties with the customer journey to solve problems and improve the overall experience. Melding data and creativity, they employed analytical tools to understand user behavior and optimize design based on KPIs and real-time evaluation.

Personalized content

Content was optimized and personalized to increase qualified traffic. The centralization of assets and production expertise accelerated content rollout and streamlined global distribution, helping Telefónica complete the sales process more effectively.

Big-data-analytics

End-to-end analytics based on anonymized data monitored the customer journey from traffic generation to conversion, where sales were optimized through digital assistant platforms and call center support.

"Accenture collaborated with Telefónica in the development and transformation of our digital agenda. Its digital capabilities and expertise have been a clear catalyst for our digital strategy, helping us to get closer to our customers and to continue to make great strides in becoming the telco of the future." — MARIANO DE BEER , Chief Commercial Digital Officer, Telefónica S.A.

THE TELCO OF THE FUTURE: Today, Telefónica offers a leading customer experience: relevant, consistent, personalized, responsive, and agile, powered by state-of-the-art service and web design, as well as advanced data analytics.

Commercially, the transformation effort has reduced acquisition costs and increased website visits—particularly from new users—doubling the conversion rate from lead to new customer. It’s also driving brand growth: More than one million digital purchases were completed as Telefónica rolled out the new experience across the UK, Spain, Brazil, Mexico and Chile.

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The AI-native telco: Radical transformation to thrive in turbulent times

Artificial intelligence (AI) is unlocking use cases that are transforming industries across a wide swath of the world’s economy. From infrastructure that “self-heals” to radically reimagined (and touchless) customer service and experience; from large scale hyper-personalization to automatically created marketing messages and images leveraging Generative AI tools like ChatGPT—it is all a reality today. These AI solutions can powerfully augment and sometimes radically outperform most traditional business roles.

About the authors

This article is a collaborative effort by Joshan Abraham, Jorge Amar , Yuval Atsmon , Miguel Frade, and Tomás Lajous , representing views from McKinsey’s Technology, Media & Telecommunications Practice.

The impact from these solutions is becoming evident. AI leaders—the top quintile of companies that have taken the McKinsey Analytics Quotient assessment—have experienced a five-year revenue CAGR that is 2.1 times higher than that of peers and a total return to shareholders that is 2.5 times larger.

Given the numerous challenges the telecom industry has faced in recent years, such as flagging revenues and ROIC, one might expect the industry would have already adopted a full transition to this technology. Yet, based on our experience with operators across the world, telcos have yet to fully embrace AI and an AI-focused mindset. Instead, models are developed once and not enhanced as the business context evolves. Machine learning (ML) is in name only, limiting the ability of the system to improve from experience. Most regrettably, AI investments are often not aligned with top-level management priorities; lacking that sponsorship, AI deployments stall, investment in technical talent withers, and the technology remains immature.

Contrast this disjointed state of affairs with an AI-native organization. Here, AI is viewed as a core competency that powers decision making across all departments and organization layers. AI investments are required to enable most C-level priorities such as more personalized recommendations for customers and faster speed of answer in call centers. Top executives serve as champions of critical AI initiatives. Data and AI capabilities are managed as products, built for scalability and reusability. AI product managers, even those working on foundational products, are celebrated for the benefits they generate for the organization.

Reaching this state of AI maturity is no easy task, but it is certainly within the reach of telcos. Indeed, with all the pressures they face, embracing large-scale deployment of AI and transitioning to being AI-native organizations could be key to driving growth and renewal. Telcos that are starting to recognize this is nonnegotiable are scaling AI investments as the business impact generated by the technology materializes.

While isolated applications of the technology can help individual departments improve, it’s AI connected holistically at all levels and departments that will be key to protecting core revenue and driving margin growth in even the most difficult of environments. Imagine the following not-so-distant scenarios:

• Customer focused: Sarah, a New Yorker, is a high average revenue per user (ARPU) customer. Aware that Sarah spends half of her phone usage time on fitness apps, the AI creates an enticing customized upgrade offer that includes a six-month credit applicable to her favorite fitness subscription and NYC-specific perks, such as a ticket to an upcoming concert sponsored by the operator. Knowing Sarah’s high digital propensity, 1 Preference to transact and engage in digital channels, such as websites and mobile apps. the AI makes the offer available to her as a digital-only promotion.
• Employee focused: When Trevor, an associate in a telco mall store, logs in at the start of his shift, he receives a celebratory notification congratulating him on his high-quality interactions with customers the previous day. And because the AI detected that Trevor is underperforming peers in accessory and device protection attach rates, he receives a notification pointing him to coaching resources specifically created to enhance performance in those metrics.
• Infrastructure focused: Lucile, director of a capital planning team, uses AI to inform highly targeted network investment decisions based on a granular understanding of customer-level network experience scores strongly correlated to commercial outcomes (for example, churn). The AI provides tactical recommendations of what and where to build based on where customers use the network and on automatically computed thresholds after which new investments have marginal impact on experience and commercial outcomes for the operator.

How these possibilities could become reality is critical to consider, especially given that most telcos currently deploy AI in limited ways that will not drive sustainable, at-scale success.

Why now? The case for becoming AI native

Factors supporting this move for telcos include the following:

• Increasing accessibility of leading AI technology: AI-native organizations like Meta continue to grow the open-source ecosystem by making new programming languages, data sets, and algorithms widely available. In parallel, cloud providers have developed multiple quick-to-deploy machine-learning APIs like Google Cloud’s Natural Language API. Generative AI solutions, such as ChatGPT, that are capable of creating engaging responses to human queries are also accessible through API. These two factors, coupled with dropping costs of data processing and storage, make AI increasingly easier for organizations to leverage.
• Rapid explosion of usable data: Operators can collect, structure, and use significantly more data directly than ever before. This information includes dataflows from individualized app usage patterns, site-specific customer experience scores, and what can be purchased or shared from partners or third parties. To answer privacy fears raised by consumers and regulators, telcos must also invest in building digital trust , including actively managing data privacy and having a robust cybersecurity strategy and a framework to guide ethical deployment of AI.
• Proven use cases and outcomes: AI-Native organizations across industries have deployed AI to achieve four critical outcomes highly relevant to operators across the world: 1) drive revenue protection and growth through personalization, 2) transform the cost structure, 3) enable a frictionless customer experience, and 4) meet new workplace demands. Operators can learn from all of them. Streaming players, for example, have long been known for providing highly curated personalized content recommendations based on past user behavior. To optimize cost and deliver a seamless customer experience, one of the leading US insurance companies leverages AI assistants to reduce and even eliminate human interactions for users to obtain coverage or cancel policies with other carriers. In turn, some of the leading tech companies in the world are known for using AI to highlight the traits of great managers and high performing teams and use those insights to train company leaders.
• Technology investments recognized as a business driver: In a post-pandemic world, there is broad consensus among investors and executives that technology investments are not a mere cost center but a fundamental business driver with profound impacts on the bottom line. Despite prospects of economic turmoil and recessionary fears, IT spending is expected to increase by more than 5 percent in 2023, with technology leaders under growing pressure to demonstrate impact on company financials. 2 “2023 CIO and Technology Executive Survey,” Gartner, October 18, 2022.
• Operator bets need hyper charging: As networks and products converge, operators are making bets on becoming cost and efficiency focused, experience-centric, or ecosystem players. AI use cases that are more relevant for each bet can give them a better chance to hypercharge and leapfrog competition.

For the greatest payoff, this shift requires telcos to embrace the concept of the AI-native organization—a structure where the technology is deeply embedded across the fabric of the entire enterprise.

Using AI to reimagine the core business

Telcos have been under relentless pressure over the past decade as traditional growth drivers eroded and economic value increasingly shifted to tech companies. By using AI to its fullest extent, operators can protect their core business from further erosion while improving margins.

As the industry looks to leverage the power of AI, we see six themes gaining prevalence in strategic agendas based on our experience working with telcos across the world.

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Hyper-personalize and architect sales and engagement.

Leveraging the breadth and depth of user-level data at their disposal, operators have been increasingly investing in AI-enabled personalization and channel steering.

For example, a hyper-personalized plan and device recommendation for each line holder could leverage granular behavioral data—such as number of and engagement with apps installed and device feature usage—to create individualized plan recommendations (superior network speed or streaming service add-ons), promos (“Receive unlimited prepaid data to be used for a music streaming service for only $5 per month”), and messaging for specific devices, locations, and events (“Upgrade to the latest device featuring built-in VR”). Subsequently, using audience segmentation tools, customers can be guided to channels that offer an engaging experience while driving the most profitable sales outcome for the telco. A subscriber, for example, with low-digital propensity, 3 Someone who prefers to transact in, and use, assisted channels, such as retail stores and call centers. high ARPU, and high churn risk who is living within a few miles of a store, might be a good candidate to nudge to a device upgrade in-store, leading to better customer experience and potentially stronger loyalty for the operator. Or consider a different scenario: this subscriber uses an advanced 5G network in New York City and is a regular user of fitness apps who travels frequently outside the country. As a result, her telco offers a personalized plan recommendation with superior network access, top fitness app subscription perks, and an attractive international data plan.

Case study: An Asia–Pacific operator that launched a comprehensive customer value management transformation powered by AI (with personalization at the core) achieved a more than 10 percent reduction in customer churn and a 20 percent uptake in cross-sell.

Reimagine proactive service

Earlier investments in digital infrastructure combined with predictive and prescriptive AI capabilities enable operators to develop a personalized service experience based on autonomous resolution and proactive outreach.

With fully autonomous resolution, for example, the system can predict and resolve potential sources of customer dissatisfaction before they are even encountered. After noticing a customer is accruing roaming charges while traveling abroad, the AI system automatically applies the optimal roaming package to her monthly bill to minimize charges. It then follows up with a personalized bill explanation detailing the package optimization and resulting savings for the customer, leading to a surprising and positive CX moment.

Operators are also exploring the redesign of digital service journeys with the help of AI assistants serving as digital concierges. Generative AI technologies, including tools such as ChatGPT, have the potential to enhance existing bots through better understanding of more complex customer intents, more empathetic conversations, and better summarization capabilities (For example, when a bot needs to handover a customer interaction to a human rep). A single unified AI assistant will likely also represent a step change in speed, accuracy, and engagement compared to the interactive voice response systems of today.

An AI-powered service organization is a key ingredient to releasing the full capacity of specialized reps for high-value interactions while improving overall customer experience—one of the key battlegrounds for telcos around the world.

Case study: A leading telco is expected to achieve an approximately 10 percent decrease in device troubleshooting calls, powered by a proactive AI engine that considers the customer’s likelihood of calling and issue severity to decide whether to push the most effective resolution via SMS. This proactive engine is also a key element of the operator’s ambition to have the highest customer satisfaction scores among competitors.

Build the store of the future

In retail, AI is leading a revolution in the design and running of stores by streamlining operations and elevating the consumer experience.

Some telcos already use virtual retail assistants displayed on floor screens to conduct multiple transactions with customers, including adding balance to a prepaid account and selling prepaid cards and TV subscriptions. A leading European telco leverages AI tools for delivering more-accurate device grading and trade-ins in the store. The store of the near future includes the following components:

• Front of house: Aisle layout and product placement are optimized based on browsing patterns analyzed by machine vision. Digital signage is made relevant to individual customers who are in-store and identified through biometric or geofencing technology. Interactive kiosks serve up personalized promos, service assistance, and wait-time forecasts. Customers are matched with reps who are given nudges with personalized info likely to spark the best interaction and lead to a truly seamless customer experience.
• Back of house: Device SKUs are automatically managed to optimize inventory and sales. Stores stock curated assortments based on local preferences surfaced in sales analytics. AI tools such as computer-vision-based grading allows for immediate price guarantees on devices that are traded in.
• Outside: Consumers walking near the store receive text or push notifications with a personalized promotion and an invitation to check the product in-store.

Case study: An Asian telco launched a 5G virtual retail assistant in unmanned pop-up stores. The digital human communicates with customers in a personal and friendly way with engaging facial expressions and body language. She supports customers across multiple transactions, from buying prepaid cards to getting SIM card replacements.

Deploy a self-healing, self-optimizing network

The AI-native telco will leverage technology to optimize decision making across the network life cycle stages, from planning and building to running and operating. In the planning and building stages, for example, AI can be used to prioritize site-level capacity investments based on granular data, such as customer-level network experience scores.

In the running and operating phases, AI can prioritize the dispatching of emergency crews based on potential revenue loss or impact on customer experience. AI can also enable a self-healing network, which automatically fixes faults—for example, auto-switching customers from one carrier frequency to another because the former was expected to become clogged. This frees up engineering resources for higher-value-added activities.

Case study: A telecom operator developed an AI-based customer network experience “score” to improve its understanding of how customers perceive their network and to inform network deployment decisions. The AI engine leveraged granular network-level information for every line (e.g., signal strength, throughput) with an ML model to create the score tailored to each customer’s individual network experience and expectations. The operator used the score, which directly correlated with impact metrics such as customer churn or network care tickets, to monitor network performance trending (how the score fluctuated in different regions), to identify opportunities to refine its buildout plan, and to improve how it managed its customer base.

The state of AI in 2022—and a half decade in review

Improve frontline productivity.

The AI-native telco also uses AI-enabled tools to optimize workforce planning and coaching of frontline employees across multiple teams, including field force, customer service, and retail associates.

For workforce planning, AI tools enhance traditional applications by forecasting across supply-and-demand metrics for monthly, daily, and intraday time horizons with higher accuracy, more granularity, and full automation. Smart scheduling matches supply with demand, such as reps needed in a call center during particularly busy periods, to meet service level targets as well as customers’ expectations.

Acting as an intelligent coaching manager, an AI-enabled nudge engine provides personalized celebratory and improvement opportunity nudges to employees and their supervisors (Exhibit 1). Coupled with advancements in Generative AI, the impact of the AI nudge-engine might go even further by, for example, simulating customer responses under different scenarios to train reps.

Case study: A telco operator deployed an AI-enabled scheduling and coaching solution for technicians servicing copper and fiber customers. Resulting efficiency gains included 10 to 20 percent capacity generation and improved customer satisfaction scores.

Power intelligent internal operations

AI-powered insights will enhance decision making across business functions, beyond the automation of standardized or low-complexity tasks. In finance, for example, AI can flag outlier invoices for further inspection, while on the accounts receivable side it can predict customers likely to default, triggering mitigating actions. In HR, AI can help flag employees with high attrition or absenteeism risk and the respective drivers while also helping identify informal influencers who can lead change management efforts. Generative AI solutions can help with the development of product marketing copy, the synthesis of customer feedback for research purposes or even enable business users to write simple code to quickly adjust IT applications.

Overall, involving AI in decision making and execution results in higher speed and consistency. Its benefits can be felt everywhere, from contract management and supplier search to onboarding and IT maintenance.

Case study: A UK-based transportation company deployed AI to identify the main drivers of employee attrition and absenteeism. The company then developed targeted interventions for each of the drivers with an estimated 20 to 25 percent reduction in sick pay and attrition costs.

Success factors of AI-native transformation

The what of envisioning being AI native is the relatively easier part of this journey; the how of making the possibilities reality is the tougher challenge. Working on multiyear projects with operators across the world, we’ve identified critical best practices in three areas that are the hallmarks of a successful AI-native transformation: building AI, managing it, and driving its adoption.

Building AI best practices

Developing transformative AI requires a carefully-calibrated approach that follows these core guidelines:

• Build core AI capabilities in a modular fashion and with reusability in mind, with the potential to be deployed across multiple contexts in the operator. A core forecasting engine, for instance, can be deployed both in a call center and in a retail setting. This will drive higher ROI for AI investments by decreasing time to deploy and preventing duplication of work.
• Tightly integrate AI capabilities with one another based on a model architecture approach that interconnects different AI models to maximize value generation and promote reusability. For example, a digital propensity model will be built as a core model that becomes an input into multiple customer-facing models.
• Use digital twins as the foundation for all AI. Digital twins—virtual representations of a physical asset, person, or process with a data product at its core—are the key to unlocking reusable AI. The data in a digital twin is intentionally structured and modeled to enable easy, reusable consumption and governance across needs, and to serve as the single source of truth for all models (Exhibit 2).
• Implement machine learning operations (MLOps) best practices to shorten the analytics development life cycle and increase model stability. MLOps typically involve automating the integration and deployment of code underlying AI capabilities .
• Rethink the tech talent strategy holistically. Without a deep bench of engineering talent, an AI-native ambition will remain a mirage. Employers should consider expanding their sourcing net to a wider range of universities and learning environments. It’s also critical to improve conditions that developers work under, because developer experience is a top factor in determining an employer’s attractiveness. 4 David Gibson, “New data: What developers look for in future job opportunities,” Stack Overflow , December 7, 2021. Constraints on which programming languages and cloud providers’ tools can be used, for example, can have meaningful impact on a developer’s decision to recruit for and stay with an organization, as well as on the developer’s productivity . Because tech talent needs are multifaceted, operators should launch a comprehensive list of initiatives across the employee life cycle.

Managing AI best practices

Maintaining and improving AI capabilities depends on an experimental, iterative mindset focused squarely on product and tech innovation.

• Treat AI capabilities as true products by assigning dedicated product managers to oversee them. PMs act as translators between the technical and business teams and are mandated to own the product continuously and develop opportunities to improve it. They ensure that it’s never built as a onetime solution.
• Set up AI labs for fast experimentation. Dedicated teams of PMs and data scientists or engineers are granted expedited approval to experiment with new models, test for feasibility, and validate business value before scaling.
• Refresh the AI tech stack at least annually to take advantage of new developments. In recent years, there have been significant enhancements in tooling that drastically transformed AI workflows.
• Speed up IT and Data Modernization efforts (the complexity of which often slows down AI transformations) by leveraging reference architectures that have been road-tested in multiple transformations across industries. Moreover, build the target cloud-native data architecture following an iterative approach, focused on enhancing the components required for the priority AI use cases first (e.g., data streaming might be key to unlock fraud detection use cases).

Driving AI adoption best practices

Taking a comprehensive approach focused on both what goes into and comes out of models is critical for fostering growing usage of AI:

• Ensure AI solutions are considered trustworthy AI , including dimensions such as model explainability, accountability for the outcomes of AI models, and technical robustness.
• Make change management a day one focus. Operators need to involve end users of AI-enabled insights through all the stages of the model development life cycle and invest in formal and informal capability building. Operators will also need to take a hard look at replacing and revamping existing processes as well as management practices and roles to be centered around AI.

Next steps toward building the AI-native telco

In many industries, companies have used AI to make their operations more efficient, drive material enhancements in customer experience, and ultimately used it to bring innovative products and services to market more quickly. Operators can learn from these industries and invest in AI to improve their competitiveness in the coming years of economic uncertainty and competitive turmoil. Many operators have already started to do so.

Organizations that talk about adopting AI but move at a slow pace, hoping that a few innovation projects developed at the fringes of the organization and in silos that will come together to create a snowball effect to holistically change how technology informs business decision making, are likely to fail.

Ultimately, the biggest drivers of AI adoption will be CEO-level sponsorship and full executive alignment throughout the AI-native transformation. The art of the possible with the technology has long surpassed what companies have been able to absorb. Without active support from the top level to proactively address organizational inertia, communicate an engaging change story, model new behavior, promote capability building, and make commitments on the required long-term technological investments, AI-native transformation efforts will not succeed.

The journey to becoming AI native will require operators to create a strategic vision and road map that excites and mobilizes the organization, build priority AI capabilities to gain momentum, and bring everyone together to ensure operating model and change management are set up to drive adoption. Embracing large-scale AI deployment across the organization will follow.

The journey is long and requires commitment, but operators that embrace the path to becoming AI native are more likely to emerge as leaders in the next horizon of transformation.

This article was revised on December 19, 2023 to include a new case study on deploying a self-healing, self-optimizing network.

Joshan Abraham is an associate partner in McKinsey’s New York office, where Miguel Frade is a consultant and Tomás Lajous is a senior partner. Jorge Amar is a partner in the Miami office and Yuval Atsmon is a senior partner in the London office.

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Providing a better buyer experience in the telecommunications industry

Providing relevant offerings to buyers at every stage of their journey requires a granular understanding of the B2B customer experience. With new technologies and market shifts shaping buyer expectations, B2B organizations, such as technology providers, need to constantly evaluate their buyer journey to enhance customer satisfaction in an already competitive environment.

A leading global telecommunications provider facing this challenge was keen to understand the buyer journey more deeply to deliver a customer-centric experience. By uncovering the barriers in the buyer journey and the factors that influence how they choose products and suppliers, it aimed to provide customers with the right support they needed at each stage.

They partnered with Netscribes to conduct a B2B customer experience study analyzing buyer experiences across the US, Europe, and the Asia Pacific.

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STC rapidly adapts to deliver “new normal” digital experience

Saudi Telecom Company's Corporate Customer Experience Management program enabled the operator to overcome pandemic-related challenges and quickly meet changing customer needs in unprecedented circumstances.

Who: Saudi Telecom Company and Huawei Technologies

What: STC’s Corporate Customer Experience Management program enabled the operator to overcome pandemic-related challenges and quickly meet changing customer needs in unprecedented circumstances.

How: Leveraged TM Forum’s Business Process Framework, Information Framework, Open APIs, and best practice toolkits to create a 360-view of STC customers and adopt a customer-centric approach.

Results: Dramatically higher use of digital channels among customers; better engagement with customers via new Digital Community; 10.6% ARPU uplift; 15-point improvement in Net Promoter Score and 14% increase in STC’s brand value.

When the coronavirus pandemic escalated in 2020, telecom operators were under pressure to keep people, businesses, government and health services connected while pivoting to remote working and protecting their own employees, many of whom were essential frontline workers. For Saudi Telecom Company (STC), the largest operator in the Kingdom of Saudi Arabia, the pandemic raised many challenges that threatened to impact sales, customer service and its brand.

With international travel restrictions, the operator suddenly faced a significant shortfall in roaming revenue. The annual Hajj pilgrimage to Mecca attracts millions of visitors, most of whom use STC’s network, for example.

National lockdown measures meant that STC had to close its retail shops, which blocked its primary sales and service channel and hindered brand engagement with customers and local communities. The operator needed new ways to strengthen brand loyalty and serve its geographically dispersed and culturally unique customer base.

Customer requirements and behavior changed almost overnight and STC needed to adapt quickly to understand subscribers’ new needs. Working from home blurred the distinction between enterprise and consumer behaviors, which posed a customer experience dilemma and potentially lower service revenue.

Building on digital customer experience foundations

In partnership with Huawei, STC launched a major digital customer experience transformation program, called Corporate Customer Experience Management (CCEx), which began in 2019 and created the foundation for improving digital customer experience.

CCEx is a significant initiative that is overseen by the office of the Group CEO. It establishes a unified approach to customer experience measurement across the STC Group. At the heart of CCEx is Huawei’s SmartCare platform, comprising customer experience management, network performance management and service quality management. Leveraging TM Forum’s Business Process Framework (eTOM) to scope the project and the Customer Experience Maturity Model and toolkit to define capabilities, CCEx provides a 360-degree view of the customer and fosters a customer-centric culture at STC.

Previously, each of STC’s consumer, enterprise and wholesale business units operated in silos and in inconsistent ways. The operator wanted standard definitions and metrics across the units and a holistic customer view. Working with Huawei, STC established a unified model for customer lifecycle and certified some 178 customer journeys across all touchpoints, based on TM Forum’s Lifecycle Metrics standard . The project also relied on TM Forum’s Information Framework (SID) and Open APIs , for example to enhance the mySTC application with digital enablers.

In addition, TM Forum’s Digital Maturity Model was used to define revenue assurance and ecosystem assurance best practices and operating procedures. The AI Maturity Model was used to guide the expansion of the customer personas developed by STC, which enabled integration with the mySTC app and new Digital Community.

Customer experience targets exceeded

Based on a myriad of customer metrics across three areas, broadly defined as “engaging”, “using”, and “evaluating”, Huawei developed a Customer Experience Index (CEI) along with a dashboard to monitor changes and analytics to understand customer perception and business outcomes and then recommend actions.

STC's Corporate CEx (CCEx) Objectives & Model

At the end of 2020, despite all the pandemic-related challenges, STC excelled at improving digital customer experience and exceeded CEI targets in all three business units. In consumer, the CEI reached 84.14% surpassing the target of 80.1%; in enterprise, the CEI was 85.43% and the target was 81.4%; and in wholesale, the CEI was 85.94% while the target was 80.7%.

Source: STC

The continual customer experience monitoring and insights provided by the platform were key factors in helping STC to grow average revenue per user (ARPU), increase the use of digital channels and improve its Net Promoter Score (NPS) and brand value.

At the end of 2020, STC mobile ARPU had increased by 10.6% compared to the end of 2019, according to the GSMA. The operator’s NPS rose from -20 in 2019 to -5 in 2020 and its customer churn rate improved to be one of the best in the region, according to Analysys Mason .

STC is Saudi Arabia’s strongest brand, with a Brand Strength Index (BSI) score of 83.9 out of 100 and it celebrated an impressive 14% increase in brand value to US$9.2 billion, according to Brand Finance .

Digitally adapting for the “new normal”

STC promoted the use of digital channels through digital awareness campaigns and expanded the number of things customers could do via digital channels, including new service sales, upgrades, add-ons, customer support, and trouble ticket resolution. It also adjusted incentive schemes and commissions to support digital sales. According to STC’s 2020 annual report , 100% of sales in main cities and more than 70% countrywide came from the mySTC app.

STC also introduced novel ways to connect with customers through a new Digital Community (DC), which brings together customers and employees to share tips and experiences as well as crowdsource troubleshooting. The new initiative not only improved customer experience and satisfaction, but also instilled a more customer-centric ethos among employees and the whole culture of STC Group.

The DC allows STC’s more than 20,000 employees to become ambassadors for the company and hear directly from customers, friends and family about their actual experiences with services. “We’re bringing together the voices of customers, employees and our processes to define the customer journey experience,” said Mohammed AlSalim, GM of CCEx Strategy & Assurance at STC.

Huawei and STC extended the existing customer experience platform to accommodate all DC-related aspects, integrating it with OSS and BSS as well as with social media channels for instant feedback.

“Now that we can really measure the digital experience and have end-to-end 360-visibility of customer experience, we can use the modeling, assurance, tools and predictive analytics to prioritize and continue improving the KPIs and to develop new products and services that fully deliver exactly what our customers are looking for,” said Yahya AlGhaihab, GM of CCEx platform management at STC.

Huawei’s SmartCare platform is designed to adapt to rapid changes in customer experience and the customer journeys were enhanced to reflect the “new normal”. This capability enables STC to deliver relevant services, which was critical when the pandemic changed so much for consumers and businesses.

For example, using insights from the customer experience platform and the CEI, STC developed and improved its Jawwy TV application and service and grew its customer base.

STC was able to act quickly to support its more than 20 million mobile subscribers and over 6 million fixed subscribers who were working from home by offering consumers extra data and discounted prices for their employers via digital channels, for example. The use of CEI enabled STC to develop tailored offerings based on experience, customer segments, usage and individual characteristics.

These efforts led to more customer engagement and greater participation in STC’s loyalty program for small- and medium-size enterprises (SMEs). From January 2020 to June 2021, the number of SMEs enrolled in the program increased by 51% to 32,604.

Next Steps for CCEx

The successful program is delivering improvements in a short time and enabling STC to adapt to changes in customer demand caused by the pandemic. The next steps are to continue expanding the implementation across the STC Group, going beyond the telecom operations in the Middle East and North Africa region to include the company’s cloud, digital media and financial technology ( stc pay ) subsidiaries.

Churn Analysis: How to Retain Customers [Telecom Case Study Inside]

Churn analysis uses insights to help B2B and B2C organizations identify the reasons for customer attrition. Let’s explore churn analysis with simple examples.

Ben is dissatisfied with frequent call connectivity issues on the LoSignal network and decides to terminate his contract with them and switch over to their main competitor. 

AcmeCorp decides that the customer experience offered by Manageo’s Project Management tool is not worth what they’re paying for, and decides to cancel their subscription. 

These are the examples of customer churn and how companies lose clients/customers.

Table of Contents

Customer Churn Rate is the percentage of customers who stopped using a product or service during a particular time frame. 

The first case talks about a B2C situation , where Ben is one of the hundreds of thousands of LoSignal’s customers. In LoSignal’s case, the revenue per customer is comparatively lower, as is the cost of acquiring new customers.

In the second B2B case with Manageo , the customer experience plays a more crucial role in preventing AcmeCorp from leaving, because the number of customers is significantly lower, and the cost of acquiring new customers is higher. The revenue generated by every customer like AcmeCorp is also high.

Did you notice?

In the above cases, Ben and AcmeCorp have “churned”.

In today’s growing marketplace, customer attrition is commonplace. Consumers have a wide variety of options to choose from, each one offering something different — a better customer experience, lower pricing, or better products and services. So, it’s vital for organizations to perform customer churn analysis to retain their customers, be it a B2C or a B2B scenario.

What is Customer Churn Analysis?

Churn analysis helps in determining the trend of customer attrition. The impact of churn analytics efforts are visible in improved customer retention rate.

The right churn analysis insights will help you understand 3 key points through the analytics toolkit:

• Why are customers churning? What are the key customer dissatisfaction drivers? You can tackle this with descriptive and diagnostic analytics .
• How do you identify which customers are going to churn in the coming months? You can answer this with predictive analytics .
• What should you do to minimize churn? Prescriptive analytics holds the key to this.

Before getting into the analytics part, we need to first understand churn rate.

You can calculate the customer churn rate by factoring in the number of customers who left during a particular time period and the total number of customers at the start of that time period. Here’s the formula for more clarity: 

Next, the goal of customer churn analytics is to determine why did a customer stop using a product or cancel a subscription . This usually involves tracking the customer journey and finding out what actions the customer took just before they quit. 

For instance, did they get stuck performing a certain task? Did they encounter any bugs in the system while submitting a page? Feedback calls, follow-up surveys and questionnaires can also help in identifying reasons for customer churn. 

Customer churn analysis also tries to predict which customers are likely to quit. For instance, a lower number of login sessions indicates low usage, and a high possibility of customers churning. After all, why would they pay for something they don’t use often?

By answering these questions, you can improve your customer experience and retain more clients.

Why is Churn Analysis Important?

The most important factor is that Churn Analytics Results in increasing your profits. Fred Reichheld, the founder of the NPS score system , found that if you retain just 5% of your customers, it results in at least 25% higher profits in the long run. On the other hand, churned customers don’t contribute anything to your revenue.

Check out Gramener’s NPS Analytics solutions , which aim to help you improve the Net Promoter Score of your brand by analyzing customer behavior and sentiments from multiple customer feedback and review sources. Check out our guide on how to calculate NPS with Machine Learning.

Second, retaining customers also means that you will unlock the long-term benefits of customer loyalty. A Bain & Company study found that customers who bought from a particular e-commerce platform for about 3 years, spent about 67% more than customers who had been buying from the platform for about 6 months.

Third, churn analysis reduces costs. Depending on your industry, acquiring new customers is five to 25 times more expensive than retaining an existing one. 

It costs more for businesses to acquire new customers than retain existing ones, because existing customers have already bought into your products or services, the philosophy of your company, and are more familiar with your offerings. 

In fact, customers who have been your customers for quite some time will do the best they can to not churn, because new offerings will mean an unfamiliar way of doing things. 

How Does Churn Analytics Work?

Simply calculating the churn rate is not sufficient to help you decrease it. You need the following data points as well.

Churn analytics can help you answer the above questions and more. Let’s tackle the “why” question first.  

Why Does Customer Churn Happen?

To answer this, you need to first capture the user behavior before they dropped off and see at which stage they decided to churn. Once you’ve captured the data, you will get a few initial insights.

Here are a few possible scenarios for customer churn in B2B:

• The overall customer experience is lacking
• Customers don’t get the value they expected from your product
• You don’t have a robust onboarding process that helps users start using the offering
• Your product is too expensive compared to competitors.
• UI/UX is not intuitive and fluid
• Bugs and glitches
• Slow load time or processing time

Against each reason, you can start assigning $ values.

For instance, let’s say you’ve found out that 20 of your churned customers, each of whom subscribed to your offering for $100 a month, quit due to poor customer experience. This means that you have lost revenue of 20X$100 = $2000 a month due to delivering a subpar customer experience. 

Once you have determined the reasons for your biggest revenue losses, you can prioritize and work on them.    

Which Customers are Canceling?

As you get more data about the customers who cancel, you can classify them into different buckets based on their demographics, behavior, and the offerings they are using. 

Customer churn is one of the biggest problems of the streaming services industry in the U.S., with 41% of customers churning as of Q1 of 2020 (largely exacerbated by users staying home and wanting to try new services during the Covid-19 lockdowns).  

So you can bucket churned customers based on common characteristics such as: 

• Whether they signed up for a trial
• The plan they chose
• The number of devices they chose
• Users who experienced a particular update or new feature

This grouping and analysis of customers is called “Cohort Analysis” and it lets you zero in on the common factors that led to customer attrition. 

For instance, a large number of customers who signed up for a particular plan may cancel. Delving into the details, you may find that a competitor has a similar offering at a lower cost. Or a group of customers didn’t convert after the trial because they didn’t find enough value to pay for a subscription. 

You can take targeted action to retain each of your cohorts. While a group of customers responds well to discounts, other customers will stick with you even without them.Some customers love loyalty programs, while for some others, it doesn’t make a significant difference. This clustering of customers helps you understand how you can improve your customer experience.

How Can You Predict Customer Churn?

Once you have a sufficiently large dataset of your churned customers, you will begin to understand which customers are churning, and why. Based on this, you will be in a position to predict which customers will churn next. 

How can you achieve this? You can train a Churn Analysis Machine Learning model based on the data that will predict which customers are likely to churn. 

There are several algorithms like decision trees, random forests, and logistic regression that can be deployed based on your industry, offerings, and other factors. 

Each will give a different accuracy rate. Based on these algorithms, a robust AI model is tailored for our organization.

Let’s look at a real-time case study based on a set of algorithms that we applied for one of our clients, a leading telecommunications provider in the U.S. 

Case Study: Churn Analysis for a Leading Telecom Client

Customer churn in the telecom industry is very common due to huge competition.

Did You Know!

The U.S. telecom industry witnesses an annual churn rate of 30-35% . This means that for every 100 people who start the year as customers, 30-35 opt out by the end of the year.

One of the measures that companies use to predict who will churn, target the right customers and deploy interventions to prevent this. 

The Challenge

Gramener worked with a major telecom operator to identify customers who are likely to churn so that the firm could target its marketing efforts in the right direction. 

The Approach

Gramener applied a series of classification models based on customer behavior, demographics, and network behavior . The result was a series of churn prediction models of increasing accuracy.

The Outcome and Impact

Through derived variables and hybrid models, Gramener identified savings of over 60% on acquisition cost through targeted marketing. The process of identifying targets was also fully automated by the model.

Reduce Customer Churn with Gramener

Customer churn analytics is a great way to find out which customers are canceling, why they are canceling, and who will cancel next. Taking the analytics route will also let you reap the maximum ROI from customer experience (CX) efforts .

It helps you segment them and target marketing campaigns according to their demographics and behavior. It’s vital to provide excellent customer service if you want to keep your current customers. If customer service is inadequate, it does not take long for customers to migrate to another service provider. When your customers rate your services, you’ll be able to see how satisfied or dissatisfied they are. The customer sentiment analysis technique is used to reduce customer attrition.

The entire process helps you retain more customers, build a brand, and get you more repeat customers and referrals which will help you uncover your customers’ pain points and give you an opportunity to address them. This in turn will lead to an improved customer experience.

Are you interested in knowing more about how you can reduce customer churn? Talk to us today.

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Varun Bala Krishnan

1 thought on “churn analysis: how to retain customers [telecom case study inside]”.

Could you please provide more details about the features. what type of data pre processing, feature engineering, modelling were done at a high level.

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The Secret behind Tunisie Telecom's Improved Customer Experience

User Tunisie Telecom provides services to more than seven million subscribers over fixed and mobile networks.

Challenge The telecom company needed to reduce the timelines required for connecting and assessing serviceability, and to increase customer satisfaction and streamline operations.

Solution A comprehensive ecosystem leveraging ArcGIS Enterprise was created to provide a company-wide system of record, powerful engagement, and resources for insight.

• Decreased time to connect
• Decreased time for serviceability assessment
• Increased customer satisfaction

Partner All work was performed by the Tunisie Telecom team.

Since its inception, Tunisie Telecom has worked to consolidate the telecommunications infrastructure in Tunisia and has improved telecommunication coverage throughout the country.

Tunisie Telecom actively contributes to the industry, promoting the use of information and communications technology (ICT) and the development of innovative telecom products and services. These include fixed broadband (Asymmetric Digital Subscriber Line [ADSL], Very High Speed Digital Subscriber Line [VDSL], fiber-optic cable, mobile voice calls, and data for business to consumer (B2C) and business to business (B2B) segments.

A pioneer of the telecoms sector in Tunisia, Tunisie Telecom has established a set of defining values, ​​which places the customer at the center of its priorities. They are as follows: simplicity, responsibility, team spirit, and commitment.

With the adoption of these values comes continual improvement of the standards throughout the company and an increase in the quality of the services it provides. Tunisie Telecom has more than seven million subscribers across its fixed and mobile networks.

To accommodate its customers, Tunisie Telecom has 24 regional offices with over 6,000 employees serving customers across 160 direct retail stores with nearly 100 franchises, and more than 13,000 independent points of sale.

Tunisie Telecom had several challenges—like many telecoms throughout the world do and work to solve each day. The company wanted to grow its market share, simplify operational workflows, and improve the customer experience.

As staff assessed these areas before working to deploy ArcGIS tools and resources, several key performance indicators (KPIs) and pain points were defined mainly in the fixed broadband (FBB) customer journey. These included manual processing for multiple activities in the customer life cycle and the lack of SLAs and OLAs between frontline and technical teams, leading to lengthy delivery and repair times.

To address these challenges, Tunisie Telecom deployed ArcGIS Enterprise as a comprehensive ecosystem for its operations. This provided a powerful foundation for teams to leverage when creating purpose-built web applications and maps and utilizing out-of-the-box desktop and field mobile applications, such as ArcGIS Workforce, ArcGIS Collector, or ArcGIS Field Maps.

As they worked to improve order fulfillment and serviceability request timelines, the team members at Tunisie Telecom took the self-sufficient digital experience for their customers to the next level by deploying an insightful and engaging custom application called Taghtia, which was built on Esri software.

The Taghtia application allows customers to request new service and immediately receive a yes or no serviceability assessment. One key element of the application is the creation and use of MyAddress , a unique customer location identifier that is used throughout the serviceability and ordering processes.

In the back end, another application, called GeoNetwork C (for Connection ), is used to support planners and engineers who work on customer connection projects.

Additionally, within Taghtia, customers have the ability to file a complaint. This service request or other inquiry is immediately delivered to the operations team and is visible in a dashboard—created using ArcGIS Dashboards—for resolution.

To simplify and speed up the installation and repair workflows, ArcGIS Workforce was deployed. This allows teams to quickly and easily create, issue, track, update, and reconcile items such as new order installations, customer complaints, and repairs.

Furthermore, though leveraging ArcGIS Workforce, the Tunisie Telecom teams can effortlessly monitor the status and progress of all work in their region in a dashboard.

Through the integration and deployment of ArcGIS Enterprise as a foundation for operations, Tunisie Telecom positively impacted its goals as follows:

• Overall customer satisfaction when placing online orders for fixed broadband rose from 89 percent to 95 percent.
• Time to assess serviceability of customer locations decreased from an average of 50 hours to just minutes.
• Time to connect decreased from over three days to under 24 hours.

Tunisie Telecom [TT] started its digital transformation journey for the delivery process of fixed and internet services using Web GIS and GIS field applications such as Taghtia, GeoNetwork, GeoNetwork C, SIG-DAO, MyAddress, and Workforce Management. It provided a unique, fully automated digital experience for our FBB customers and improved our operational KPIs.

Explore how Esri can support your organization

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Clients & Success Stories

• Multinational Telecoms Discover BSS/OSS Transformations of the World Biggest Telcos Browse now

Explore Comarch telecom case study examples:

Would you like to know how different operators are utilizing Comarch IT solutions, and to find out the tangible effects of our systems? Discover compelling telecommunication case studies on AI-driven technologies, BSS, OSS and more. Learn how Comarch helps telecommunication service providers optimize their businesses, grow effectiveness and shorten their time to market. See how we prepare telcos for the upcoming new opportunities, such as IoT and 5G services. Explore the telecom case study examples and find out what our software can do for your business.

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• Telecoms Operating Within International Groups
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Telecoms Operating Within International Groups:

End-to-end view of services with integrated resource and service layers thanks to Comarch Resource & Service Inventory

Convergent billing for improved billing processes and shorter time to market for new services

Full automation of fault management tasks, including correlation and handling trouble tickets

Transformation of network planning and design processes with Comarch OSS

Improved efficiency of network planning and optimization processes in Telefónica subsidiaries in Latin America

Better control of network operations with transport network management & configuration, fault and performance mediation

Support in entering the IoT / M2M market and becoming the leading M2M boutique provider in the CEE region thanks to Comarch IoT Connect

IT transformation agreement to deliver better digital experience to the growing base of mobile and fixed services end-customers. 

Comprehensive BSS stack for mobile, fixed and network services

Optimization of field service management for a network covering more than 300 000 square kilometres and more than 20 million subscribers

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Supporting implementation and development of FTTH (Fiber to the Home) all over the country by delivering an OSS system

Fault management, service monitoring and performance management multi-tenant solution and tools for a single Vodafone Global NOC, managing the operator’s networks in several countries

Integrated Assurance & Analytics for mobile, fixed and cable networks & services in Vodafone Germany, including Vodafone Kabel Deutschland

Switching to process-driven infrastructure and network management, with centralized inventory management and planning

Using Comarch IoT Connect to create a new IoT connectivity platform serving the Saudi market

B2B BSS system for satellite Internet service provider

Improved network performance visibility with Comarch Performance Management platform

Rollout of OSS/BSS IT infrastructure covering major operational and business processes

Multi-market Transformation Improves Time to Market and the Cost of Service Delivery in the Business Customer Segment

Delivering new common integrated customer-, product-, order- and billing solution using Comarch BSS/OSS suite for all business operations.

Watch videos about our telecom case studies!

Fixed and mobile, triple and quadplayers:.

Support for development of next-generation smart metering services thanks to Comarch OSS & assurance products combined with Managed Services

Smart BSS supporting the services of high-speed broadband access across all territories in the north of France

New sophisticated billing system extending the supply chain with separate pricing of services for individual customers

Support for data mediation and interconnect settlements

Integrated BSS system for large enterprises and corporates, leading to streamlined KPN customer experience, reduced costs and minimal investment risks

KPN and Comarch Expand Partnership After 16 Years of Successful Cooperation

Support in launch of the world’s first 5G network with the help of Comarch products from the Operations Support Systems and Intelligent Assurance & Analytics range

Transformation into a full MVNO and BSS overhaul for three operator brands

Support for Orange and T-Mobile infrastructure-sharing initiative in Poland – planning and optimization of the “golden grid” network

Implementation of fully-fledged BSS system ready to serve first customers within 4.5 months

Support for the wholesale B2B activities with InterPartner Billing system for one of the biggest mobile telecom carriers in Poland

Delivery of system handling interconnect agreements, covering current and future needs

Implementation of Comarch Fault Management for reduced risk of network failures and improved service quality for customers

Mass-market business support systems (CRM, orders, billing and charging)

Enhancement of multi-service, multi-technology and multi-vendor IT environment and operations with Comarch BSS

Supporting Sunrise’s IoT services expansion by delivering an out-of-the-box IoT Connect Platform

Support in reaching the goal of becoming a carrier-neutral infrastructure leader thanks to Comarch BSS

Integrated BSS system handling subscribers of IP telephony (calling cards)

Smart BSS system supporting the development of converged telecom services

Support for an innovative broadband Internet access project with the help of Comarch BSS

Cable/Satellite TV:

Previously CableOnda Panama

Helping a local cable operator to become customer-centric and maximize profits with a new end-to-end BSS solution

Increasing operational efficiency via a centralized BSS system for multiple billing and customer care systems

Implementing a Field Service Management system to automate tasks in the field

Support for Germany’s largest broadcast and media industry service provider in the rapid and efficient introduction of new TV services (under the brand name freenet TV), based on DVB-T2

Comprehensive BSS system supporting the Polish multi-play operator in pursuit of a product innovation strategy, through streamlining and facilitating the creation of new offers

Project carried out for Multimedia

Deployment of a comprehensive billing system replacing the various legacy systems of Multimedia’s daughter companies

Customer experience improvement in a multi-partner model of field service delivery

Providing a customized BSS/OSS system to the company, with exclusive assignment of the 450MHz spectrum in Germany.

FSM integration, operation automation, and improved customer experience for B2B and B2C clients in the Netherlands.

Streamlining the operations supporting readiness, fulfillment, assurance and service monitoring domains, for the digital transformation in Japanese telecoms.

A reliable and deeply automated FSM system tailored to the needs of Ukraine’s largest telco operator

Streamlined field service operations and automated management of more than 300,000 communications circuits and thousands of network devices in Indonesia

Assisting MWingz to become Belgium’s first shared network by providing an operating and managing system in the SaaS model.

Supporting the creation of an IoT ecosystem for Japanese enterprises for a software provider

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• Case Studies

How Scandinavia’s largest telco is enhancing customer experience with conversational AI

Last updated 22 February 2024

With over 20 unique integrations, Telenor’s virtual agent Telmi is one of the most advanced of its kind in the world.

In 2023, enhancing telecom customer experience is still a primary concern for all telecom companies. Already, in 2019, the American Customer Satisfaction Index research revealed that telcos placed last in customer satisfaction when ranked against other private sector companies such as banks and insurance firms. Given customers' highly heightened expectation of technical savvy and fast responses, telecom companies must take concrete steps to ensure they can deliver a consistently satisfying business-to-consumer experience. Investing in customer service infrastructure, formulating policies that help maintain effective communication channels with customers, and developing a better customer service strategy could all contribute to telecom customer experience improvement.

This very issue was at the center of Norwegian telecommunications giant Telenor’s drive to digitize its customer service offerings with the introduction of conversational artificial intelligence. “Communication is at the heart of everything we do at Telenor,” says Anna Måsender, the company’s Head of Customer Service. Måsender is principally responsible for Telenor’s omnichannel support strategy and is spearheading the adoption of automated online chat as a leading method of company-customer communication. “Customers come to us with issues that are important and individual to them, and we need to be both easily accessible when they require it, and capable of making them feel reassured. Conversational AI presents itself as the perfect platform to achieve these goals,” adds Måsender. ‍

One of the most advanced virtual agents of its kind

In late 2018, Telenor approached boost.ai to develop an innovative solution that would allow its customers to take advantage of the company’s wide portfolio of products and services with as little friction as possible. This took the form of an ai customer service virtual agent named Telmi that, today, can be easily accessed via the telco’s website. Thanks to deep learning algorithms and natural language technologies, Telmi is able to understand and interact with customers at an advanced conversational level, delivering near-instant assistance at far greater speeds than traditional customer service channels.

Telmi also currently offers over 20 unique integrations, making it one of the most advanced virtual agents of its kind in the world. “Integrations are a key part of Telmi’s functionality, giving our customers agency over the process by incorporating their subscription and services into every interaction,” says Jens Mosbergvik, Head of Operational Support, Customer Care. “We learned early on that customers are not simply looking for answers but require tangible solutions. It’s a small difference, but a crucial one.”

In order to deliver these solutions, Telmi offers a range of functionality to customers who are logged into their Telenor account, including requesting a PUK code, upgrading their mobile data plan, or viewing their invoice directly in the chat window. “Telmi can access a customer’s account and retrieve the information that they need, instantly,” says Morten Lossius, an AI Trainer who is part of the team at Telenor responsible for bringing Telmi online. “If a customer needs more data, for example, Telmi orders it for them, so that they can continue to use their phone without any downtime,”

The AI trainer team is in charge of measuring the experience and satisfaction of customers as they interact with Telmi. They do this via boost.ai’s easy-to-use software which allows them to monitor the quality of each integration, improving the virtual agent and continuously striving towards a better telecom customer experience .

Exceeding expectations in less than 12 months

From a business perspective, Telmi exceeded the expectations that were outlined by Telenor when the project launched in January 2019. The clear business and ROI goals that the company had defined were easily achieved within the first year, and Telmi further proved itself useful, acting as an additional sales channel rather than just a simple answer-bot.

A fact that is bolstered by a study published by McKinsey that reported increases in customer satisfaction rates of more than 20% (and revenue increases of up to 15%) when companies actively prioritized customer experience. Through the development and implementation of its virtual agent, Telenor gained a greater understanding of how conversational AI can add overall value to its business and for customers going forward, instead of only being looked at as a source of cost-cutting.

“As Scandinavia’s largest telecommunications company, it is important for us to have a clear vision of where we want our digital customer service strategy to be within five years,” adds Måsender, who explains that this applies both in terms of volume and scope, but also in terms of customer experience. “Telmi gives us the flexibility to achieve this without compromising on the high customer service standards that we set for ourselves.”

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Eight ai use cases in the telecom industry, 7 min read ·.

The telecommunications industry is evolving rapidly, and artificial intelligence (AI) is playing a pivotal role in shaping its future. Clear, uninterrupted service is the key to growth. The quality of the customer experience has long been a differentiator, but existing networks were never meant to support current traffic volumes.

Business leaders are under pressure to transition to 5G and beyond while simultaneously evolving their networks from a cost center to a profit center. As a result, all carriers are now looking to leverage innovation and technology to improve the customer experience, optimize network efficiency and performance, enhance efficiency and drive revenue.

Here are eight essential AI use cases in telecom that demonstrate how carriers can leverage AI and other technologies going forward.

1. Network Operations Monitoring and Management

A network that’s not working is a network that’s not earning money. Constantly optimizing existing networks has therefore become an operational competency for all network operators. All carriers have deep historical data on every aspect of their network performance. The right combination of AI, data science, machine learning, cloud and edge computing will enable them to leverage to its fullest extent.

When carriers combine the right technologies in the right ways, the future of AI in telecommunications is incredibly bright. Using custom tools, advanced dashboards, and centralized access to key network metrics and measures for remediation. These AI-enabled tools can even begin to conduct root-cause analyses and provide recommendations on how to make precise adjustments to antenna placement, power, tower height, frequency and more to keep the network performing at its peak.

Anticipating failures before they happen also lets carriers provide swift remediation in the field and avoid or minimize downtime.

2. Predictive (Prognostic) Maintenance

Commercial equipment is typically maintained on an assumptive basis. For instance, an airline assumes the need to replace or service jet engines within a specified Time Between Overhauls (TBOs). They plan to briefly remove each engine from service within that TBO, and the number of engines that are out of service—and not driving revenue—affects everything from ticket prices to departure times.

Until recently, telecom carriers have operated their networks on a similar basis. But combining the right technologies can enable them to shift to predictive maintenance, in which they leverage the vast stores of data that reflect how their infrastructure components are actually being used. Predicting failure rather than assuming it enables operators to maximize the life of each asset. Nothing is removed from service while it still has significant useful life, and nothing stays in service long enough to fail.

This enables the telecom carrier to maximize network uptime, plan for CapEx and OpEx spending, and drive efficiency.

3. AI-based Fraud Mitigation Solutions in Telecom

Telecom fraud has been a challenge since the industry began. Today, AI-enabled tools let carriers stay ahead of malicious actors’ evolving tactics. They also enable constant monitoring to identify when bots are using your network, prevent malicious actors from accessing personal customer information and other sensitive data, and prevent other unauthorized access.

When paired with the right mix of other technologies, typically Internet of Things (IoT), data and cloud, AI-enabled tools are ideal for constantly monitoring your network and infrastructure. These regular audits and risk assessments let you monitor call traffic and usage patterns to detect suspicious activities and irregularities so you can respond to incidents more quickly.

Once AI finds the holes, you can patch them quickly and update encryption protocols, data-storage practices and disaster-recovery plans to safeguard sensitive information, eliminate vulnerabilities and harden your infrastructure. This enables you to minimize financial losses, avoid reputational damage, and maintain legal and regulatory compliance.

A network that’s not working is a network that’s not earning money.

4. Customer Service and Marketing Virtual Digital Assistants

Combining machine learning (ML) and AI with natural language processing (NLP) and conversational search powers chatbots and other virtual assistants that already handle routine customer inquiries. This requires the carrier to determine the ideal balance of human skills and machine capabilities, but once that’s done, this powerful combination can free up human workers to take on more complex and valuable tasks.

AI use cases in telecom can go beyond just a standard chatbot that puts people in a queue. In many cases, telecom companies can use AI to handle a large amount of customer service issues, keeping your employees free for the bigger escalations. Adding retrieval-augmented generation technology empowers bots to leverage a far greater range of internal documents to serve customers in even more sophisticated ways, yet still return answers in conversational formats.

Beyond just chatbots and customer service assistants, a strong customer data platform (CDP) enables marketers to create customer journey maps and update them in real time. Coupled with the right analytics program, a good CDP will let the carrier understand not just what the customer is doing, but why they’re doing it and what they’re likely to do next. With that insight in hand, marketing teams can tailor promotions and offers to drive upsells and cross-sells.

5. Intelligent CRM Systems

Customer service and related issues are a constant struggle. Some carriers have successfully differentiated their entire brands purely on customer service. Now carriers can begin to leverage AI’s ability to parse vast data sets in near-real time to improve their customer relationship management (CRM) efforts.

When coupled with the right data and hyper automation, the right CDP and real-time customer journey maps can automate lead scoring and prioritize the potential value of each customer by predicting their cross-sell and upsell potential.

Carriers can then use this insight to improve the customer experience, prompt employees to better serve customers with next-best actions, enhance interactions everywhere the customer meets the brand, predict and reduce churn and recommend personalized solutions.

It can also enable conquesting and re-conquesting campaigns, help carriers win back lost customers and drive repeat business.

6. Customer Experience Management (CEM)

When brands are doing well, social media can add vast amounts of value and drive revenue consistently. But if a problem crops up that the brand is unaware of and that’s shared virally, the negative impact can be vast.

AI-enabled social-listening tools crawl the Internet searching for sentiment about the brand, both good and bad. These tools can gauge customer satisfaction from social media, reviews, and other feedback, identify issues before they escalate, and suggest remedies that the brand can take to not just fix the situation but share what they did and regain or rebuild sentiment in ways that strengthen the brand.

7. Base Station Profitability

The newly increased demand for high-speed mobile data services and the rapid expansion of mobile networks have placed immense pressure on telecom base stations. Continuing rollouts of enterprise 5G technology has also increased the need to improve capacity and coverage.

The high cost of base station equipment and the need for skilled professionals to deploy and maintain these systems create an ideal use case for AI-enabled tools. From deciding where to place base stations to optimizing their power consumption, carriers can achieve tangible business outcomes with AI and keep both single-band and multi-band base stations running at peak efficiency.

By leveraging AI, we not only predict failures but maximize the life of each asset, ensuring nothing is removed from service while it still has significant useful life.

8. Network Planning and Optimization

Planning network growth is a strategic exercise; it determines the carrier’s future direction. As customer needs grow, so must networks. With that, their infrastructure must grow as well.

But where are the optimal locations? How will the new network allow for loads to be managed and balanced? And how should the network be expanded to allocate resources efficiently? These questions make network planning and optimization a key use case for AI in telecommunications.

Leveraging the transformative power of AI-driven models lets project teams parse vast amounts of data, enable leaders to make smarter decisions, and create digital twins that simulate real-world operations to test and refine their decisions. The result is a comprehensive set of roadmaps to guide the tactical execution of fiber rollouts, capture astonishing value, and highlight opportunities for the most strategic expansion possible. We have a telecom strategy case study that exemplifies how we helped a company harness AI in telecom and evolve their business.

Harness the Power of Telecom AI

As the world demands greater and greater connectivity, network operators have an opportunity to evolve and build networks intelligently by using AI and digital twins to analyze and act upon vast amounts of data. Doing so will enable network decisions that resonate positively across the network for years to come.

Sand Technologies has a long history of supporting sector leaders in every aspect of their business. Contact us today to learn more about how we can partner with you to take your networks and operations to the next level.

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