Telco Cloud: Why it hasn’t delivered, and what must change for 5G

Related Webinar – 5G Telco Clouds: Where we are and where we are headed

This research report will be expanded upon on our upcoming webinar 5G Telco Clouds: Where we are and where we are headed. In this webinar we will argue that 5G will only pay if telcos find a way to make telco clouds work. We will look to address the following key questions:

  • Why have telcos struggled to realise the telco cloud promise?
  • What do telcos need to do to unlock the key benefits?
  • Why is now the time for telcos to try again?

Join us on April 8th 16:00 – 17:00 GMT by using this registration link.

Telco cloud: big promises, undelivered

A network running in the cloud

Back in the early 2010s, the idea that a telecoms operator could run its network in the cloud was earth-shattering. Telecoms networks were complicated and highly-bespoke, and therefore expensive to build, and operate. What if we could find a way to run networks on common, shared resources – like the cloud computing companies do with IT applications? This would be beneficial in a whole host of ways, mostly related to flexibility and efficiency. The industry was sold.

In 2012, ETSI started the ball rolling when it unveiled the Network Functions Virtualisation (NFV) whitepaper, which borrowed the IT world’s concept of server-virtualisation and gave it a networking spin. Network functions would cease to be tied to dedicated pieces of equipment, and instead would run inside “virtual machines” (VMs) hosted on generic computing equipment. In essence, network functions would become software apps, known as virtual network functions (VNFs).

Because the software (the VNF) is not tied to hardware, operators would have much more flexibility over how their network is deployed. As long as we figure out a suitable way to control and configure the apps, we should be able to scale deployments up and down to meet requirements at a given time. And as long as we have enough high-volume servers, switches and storage devices connected together, it’s as simple as spinning up a new instance of the VNF – much simpler than before, when we needed to procure and deploy dedicated pieces of equipment with hefty price tags attached.

An additional benefit of moving to a software model is that operators have a far greater degree of control than before over where network functions physically reside. NFV infrastructure can directly replace old-school networking equipment in the operator’s central offices and points of presence, but the software can in theory run anywhere – in the operator’s private centralised data centre, in a datacentre managed by someone else, or even in a public hyperscale cloud. With a bit of re-engineering, it would be possible to distribute resources throughout a network, perhaps placing traffic-intensive user functions in a hub closer to the user, so that less traffic needs to go back and forth to the central control point. The key is that operators are free to choose, and shift workloads around, dependent on what they need to achieve.

The telco cloud promise

Somewhere along the way, we began talking about the telco cloud. This is a term that means many things to many people. At its most basic level, it refers specifically to the data centre resources supporting a carrier-grade telecoms network: hardware and software infrastructure, with NFV as the underlying technology. But over time, the term has started to also be associated with cloud business practices – that is to say, the innovation-focussed business model of successful cloud computing companies

Figure 2: Telco cloud defined: New technology and new ways of working

Telco cloud: Virtualised & programmable infrastructure together with cloud business practices

Source: STL Partners

In this model, telco infrastructure becomes a flexible technology platform which can be leveraged to enable new ways of working across an operator’s business. Operations become easier to automate. Product development and testing becomes more straightforward – and can happen more quickly than before. With less need for high capital spend on equipment, there is more potential for shorter, success-based funding cycles which promote innovation.

Much has been written about the vast potential of such a telco cloud, by analysts and marketers alike. Indeed, STL Partners has been partial to the same. For this reason, we will avoid a thorough investigation here. Instead, we will use a simplified framework which covers the four major buckets of value which telco cloud is supposed to help us unlock:

Figure 3: The telco cloud promise: Major buckets of value to be unlocked

Four buckets of value from telco cloud: Openness; Flexibility, visibility & control; Performance at scale; Agile service introduction

Source: STL Partners

These four buckets cover the most commonly-cited expectations of telcos moving to the cloud. Swallowed within them all, to some extent, is a fifth expectation: cost savings, which have been promised as a side-effect. These expectations have their origin in what the analyst and vendor community has promised – and so, in theory, they should be realistic and achievable.

The less-exciting reality

At STL Partners, we track the progress of telco cloud primarily through our NFV Deployment Tracker, a comprehensive database of live deployments of telco cloud technologies (NFV, SDN and beyond) in telecoms networks across the planet. The emphasis is on live rather than those running in testbeds or as proofs of concept, since we believe this is a fairer reflection of how mature the industry really is in this regard.

What we find is that, after a slow start, telcos have really taken to telco cloud since 2017, where we have seen a surge in deployments:

Figure 4: Total live deployments of telco cloud technology, 2015-2019
Includes NFVi, VNF, SDN deployments running in live production networks, globally

Telco cloud deployments have risen substantially over the past few years

Source: STL Partners NFV Deployment Tracker

All of the major operator groups around the world are now running telco clouds, as well as a significant long tail of smaller players. As we have explained previously, the primary driving force in that surge has been the move to virtualise mobile core networks in response to data traffic growth, and in preparation for roll-out of 5G networks. To date, most of it is based on NFV: taking existing physical core network functions (components of the Evolved Packet Core or the IP Multimedia Subsystem, in most cases) and running them in virtual machines. No operator has completely decommissioned legacy network infrastructure, but in many cases these deployments are already very ambitious, supporting 50% or more of a mobile operator’s total network traffic.

Yet, despite a surge in deployments, operators we work with are increasingly frustrated in the results. The technology works, but we are a long way from unlocking the value promised in Figure 2. Solutions to date are far from open and vendor-neutral. The ability to monitor, optimise and modify systems is far from ubiquitous. Performance is acceptable, but nothing to write home about, and not yet proven at mass scale. Examples of truly innovative services built on telco cloud platforms are few and far between.

We are continually asked: will telco cloud really deliver? And what needs to change for that to happen?

The problem: flawed approaches to deployment

Learning from those on the front line

The STL Partners hypothesis is that telco cloud, in and of itself, is not the problem. From a theoretical standpoint, there is no reason that virtualised and programmable network and IT infrastructure cannot be a platform for delivering the telco cloud promise. Instead, we believe that the reason it has not yet delivered is linked to how the technology has been deployed, both in terms of the technical architecture, and how the telco has organised itself to operate it.

To test this hypothesis, we conducted primary research with fifteen telecoms operators at different stages in their telco cloud journey. We asked them about their deployments to date, how they have been delivered, the challenges encountered, how successful they have been, and how they see things unfolding in the future.

Our sample includes individuals leading telco cloud deployment at a range of mobile, fixed and converged network operators of all shapes and sizes, and in all regions of the world. Titles vary widely, but include Chief Technology Officers, Heads of Technology Exploration and Chief Network Architects. Our criteria were that individuals needed to be knee-deep in their organisation’s NFV deployments, not just from a strategic standpoint, but also close to the operational complexities of making it happen.

What we found is that most telco cloud deployments to date fall into two categories, driven by the operator’s starting point in making the decision to proceed:

Figure 5: Two starting points for deploying telco cloud

Function-first "we need to virtualise XYZ" vs platform-first "we want to build a cloud platform"

Source: STL Partners

The operators we spoke to were split between these two camps. What we found is that the starting points greatly affect how the technology is deployed. In the coming pages, we will explain both in more detail.

Table of contents

  • Executive Summary
  • Telco cloud: big promises, undelivered
    • A network running in the cloud
    • The telco cloud promise
    • The less-exciting reality
  • The problem: flawed approaches to deployment
    • Learning from those on the front line
    • A function-first approach to telco cloud
    • A platform-first approach to telco cloud
  • The solution: change, collaboration and integration
    • Multi-vendor telco cloud is preferred
    • The internal transformation problem
    • The need to foster collaboration and integration
    • Standards versus blueprints
    • Insufficient management and orchestration solutions
    • Vendor partnerships and pre-integration
  • Conclusions: A better telco cloud is possible, and 5G makes it an urgent priority

Telco 2030: New purpose, strategy and business models for the Coordination Age

New age, new needs, new approaches

As the calendar turns to the second decade of the 21st century we outline a new purpose, strategy and business models for the telecoms industry. We first described The Coordination Age’, our vision of the market context, in our report The Coordination Age: A third age of telecoms in 2018.

The Coordination Age arises from the convergence of:

  • Global and near universal demands from businesses, governments and consumers for greater resource efficiency, availability and conservation, and
  • Technological advances that will allow near their real-time management.

Figure 1: Needs for efficient use of resources are driving economic and digital transformation

Resource availability, Resource efficiency, Resource conservation: Issues for governments, enterprises and consumers. Solutions must come from all constituents.

Source: STL Partners

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A new purpose for a new age

This new report outlines how telcos can succeed in the Coordination Age, including what their new purpose should be, the strategies, business models and investment approaches needed to deliver it.

It argues that faster networks which can connect tens of billions of sensors coupled with advances in analytics and process digitisation and automation means that there are opportunities for telecoms players to offer more than connectivity.

It also shows how a successful telecoms operator in the Coordination Age will profitably contribute to improving society by enabling governments, enterprises and consumers to collaborate in such a way that precious resources – labour, knowledge, energy, power, products, housing, and so forth – are managed and allocated more efficiently and effectively than ever before. This should have major positive economic and social benefits.

Moreover, we believe that the new purpose and strategies will help all stakeholders, including investors and employees, realign to deliver a motivating and rewarding new model. This is a critical role – and challenge – for all leaders in telecoms, on which the CEO and C-suite must align.

To do this, telecoms operators will need to move beyond providing core communications services. If they don’t choose this path, they are likely to be left fighting for a share of a shrinking ‘telecoms pie’.

A little history 2.0

Back in 2006, STL Partners came up with a first bold new vision for the telecoms industry to use its communications, connectivity, and other capabilities (such as billing, identity, authentication, security, analytics) to build a two-sided platform that enables enterprises to interact with each other and consumers more effectively.

We dubbed this Telco 2.0 and the last version of the Telco 2.0 manifesto we published can be found here – we feel it was prescient and that many of the points we made still resonate today. Indeed, many telecoms operators have embraced the Telco 2.0 two-sided business model over the last ten years.

This latest report builds on much of what we have learned in the previous fourteen years. We hope it will help carry the industry forwards into the next decade with renewed energy and success.

Other recent reports on the Coordination Age:

Table of contents

  • Executive Summary
  • Introduction
  • Industry context: End of the last cycle
    • The telecoms industry is seeking growth
    • Society is facing some major social and economic challenges
    • Addressing society’s (and the telecoms industry’s) challenges
  • The Coordination Age
    • Right here, right now
    • How would the Coordination Age work in healthcare, for example?
  • New opportunities for telcos?
    • The telecoms industry’s new role in the Coordination Age
    • Telcos need an updated purpose
    • This will help to realign stakeholders
    • A new purpose can be the foundation of new strategy too
    • Investment priorities need to reflect the purpose
    • New operational models will also follow
  • Conclusions: What will Telco 2030 look like?

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How telcos can win with SMBs: Strategies for success

SMB markets: An elusive opportunity for telcos

SMBs (small-to-medium-sized businesses) have been a challenging market for telcos historically. Despite this, it remains an attractive opportunity thanks to its sheer size and (potential) margins. Our interview programme, across 10 telcos globally and 100 SMBs in Europe and North America, revealed a feeling that telcos could see real rewards by focusing on this previously underserved market.

“SMB is now a high priority as a large part of our B2B strategy. We see it as a very big and growing opportunity,” noted a Western European Operator. A North American operator commented, “medium enterprises are now an area of great focus for us, there’s lots of potential there. We didn’t use to but are now investing lots of resources.” There are several key factors why telcos are looking to pursue this opportunity now:

  • As consumer average revenue per user (ARPUs) continue to decline, there remains a promise of stability and  growth with business customers.
  • SMBs are becoming more technologically mature and are increasingly embracing trends such as remote working and bring your own device, which can reduce their costs of operation. They have increased need and desire for digital and cloud services, which enable employees to access documents from any device, anywhere – they are often looking to their broadband providers to provide this.
  • Security and compliance are a high priority for SMBs. Previously they may have relied upon the belief that small businesses will not be targeted by cyberattacks, but increasingly SMBs will struggle to do business without being able to prove they are compliant. As this report will go on to highlight, security is an area of key potential telcos should be looking to pursue.
  • Technology such as artificial intelligence (AI) and SD-WAN can enable telcos to provide new services to SMBs while keeping cost of acquisition low.

SMB markets are attractive due to sheer size and (potential) margins

For SMBs, the potential untapped revenues, though relatively small per business, are sizeable when aggregated across SMBs. For example, companies with fewer than 250 employees made up 99% of all enterprises in the EU. But why do telcos often struggle in this space, and what should they do to succeed in this market?

First, it’s important to define what we mean by SMBs and how we should segment them. There is no one clear definition, and segmentation often differs across markets. For example, one operator we spoke to in Mexico pointed out that what they classify as relatively large enterprises would be considered SMBs by telcos in the United States. The definition varies, often dependent on the difference in average company size for each region.

For the purposes of this report, we define SMBs as enterprises with fewer than 100 employees. We also include the category of firms with 2-7 employees – often called SOHO (single office / home office) or VSE (very small enterprise) – in our definition. However, given their size and needs, telcos sometimes group SOHOs with consumers in their “mass-market” lines of business.

The number of potential SMB customers provides the telco with scale of service and large revenue opportunities. These opportunities come from both the acquisition of new customers, for whom operators provide connectivity and communications services (voice, conferencing, UC), and from upselling additional adjacent services to existing customers. These new services might include:

  • Enterprise mobility: management and security of mobile devices, including scenarios like bringyour-own-device (BYOD) and virtual desktops
  • Software-as-a-service: cloud-hosted enterprise software such as productivity software (e.g. Office 365), CRM software (e.g. Salesforce) or accounting packages (e.g. local accounting software)
  • Infrastructure-as-a-service: compute / storage resources and networking capabilities
  • Cybersecurity and disaster recovery: email backup and security services including firewalls, anti-phishing and DDOS attack prevention
  • IoT connectivity: bespoke connectivity solutions for IoT devices (though not the focus of this report, it is a major new area for telco enterprise services).

For most telcos, moving into new services is a crucial move to combat the commoditisation of connectivity. This move is critical in the SMB market, where cost of acquisition of new customers is relatively high, so telcos must offer value-add services to make it profitable.

Telcos’ key challenges in SMB markets: Fragmentation, heterogeneity, “high-touch” engagement

Disparity characterises the SMB market. The divergence of expectations, needs, and technological maturity of SMBs creates fragmentation. Additionally, SMB needs vary by vertical and region, both of which create additional elements of disparity. This market fragmentation has created two crucial challenges for telcos.

  1. It’s hard to understand the customers’ needs because they vary so greatly from one SMB to another.
  2. It’s expensive to serve them because of the time it takes to understand these needs and develop bespoke solutions to address them.

Both of the above challenges are complicated by SMBs’ relatively limited buying power and often limited understanding of their own IT requirements. Despite their smaller budgets, SMBs traditionally require a relatively large investment to win a sale. In comparison to the highly automated, self-service environment of many telcos’ consumer divisions, SMBs want and expect personalised, often dedicated (even face to face) sales and support. Along with knowledge of their product suite, sellers may need to help solve wider IT problems or offer technical guidance. Successful SMB sales teams require broad knowledge and time, making it a comparatively big investment for telcos.

It is not just the sales process that needs to be personalised and consultative; SMBs may also require bespoke product configuration and integration. This kind of service would be expected within a large enterprise but becomes prohibitively expensive within smaller businesses unless it is provided by channels with wider monetisation models (e.g. IT support or equipment sales). In short, SMBs have the engagement expectations of enterprises, with budgets closer to that of consumers. No wonder that few telcos made the effort with SMBs while their consumer businesses were still growing.

To seize this opportunity, telcos must find a way to bridge the gap between the entirely productised world of consumer, and the bespoke sales and services for larger corporates and enterprises.

Table of contents

  • Executive Summary
  • SMB markets: An elusive opportunity for telcos
    • SMB markets are attractive due to sheer size and (potential) margins
    • Telcos’ key challenges in SMB markets: Fragmentation, heterogeneity, “high-touch” engagement
    • There is a disconnect between what telcos think SMBs need and what they actually want
  • Untapped opportunities: Strategies for SMB market success
  • Channel strategies: Engaging SMBs to provide a “high-touch” experience
    • Short term channel strategies
    • Long term channel strategies
  • Product strategies: Where to win quick in a fragmented market
    • Short term product strategies
    • Long-term product strategies
  • Supporting capabilities: Where telcos should invest for success in the SMB market
    • Short-term supporting capabilities needed
    • Long-term supporting capabilities needed
  • Conclusion

Elisa Automate: Growing value with sisu

Elisa’s transformation journey

Almost every telco aspires to innovate and become a ‘digital services player’, selling more than just data, voice, messages, and entertainment services, but few have made significant inroads to this aim.

Yet Elisa, the market leader in Finland, which has a population of only just over 5.5 million people, can stake a claim to having achieved more than most.

The Finnish word ‘sisu’ has no direct English translation. It means a spirit of determination, independence and fortitude, and is considered by some Finns to be the heart of Finnish character.

Elisa and the other Finnish telcos certainly have plenty of sisu. They have resolutely charted their own course and prospered, with Elisa quadrupling its market valuation over the last ten years.

Elisa’s share price has quadrupled since 2009 

Source: Yahoo Finance

The genesis of Elisa Automate

Elisa’s overall strategy was based on a sound but uncommon piece of customer insight: nobody knows what a megabit of data actually is, so it is crazy to price data services based on the volume of data used. So Elisa and the other players in the Finnish market moved to unlimited data packages prices by speed (see report Sense check: Can data growth save telco revenues?).

The consequences of this decision have been that Finnish customers use a lot of data, and secondly, Finnish operators have built out coverage so that they can enjoy using it whenever and wherever.

This means that Elisa has to deliver a lot of data across its network.

Elisa’s data traffic has grown massively

Source: Elisa

Elisa has grown its revenues and EBIT too

Source: Elisa

Necessity can be the mother of invention

To manage profitability in a market where use and therefore data volume is effectively unlimited, Elisa had to tie its costs firmly to its revenues, and to do so elected to keep the ratios of capex and opex to revenue flat. This requires a very clear focus on cost management, and a determination to take every step possible to do so.

Elisa’s capex/revenue ratio is surprisingly low and stable

Elisa capex ratio

 

Source: Elisa

Out of this need came a powerful drive for automation: not to simply cut costs or reduce headcount, but to make the company as efficient as possible.

The result is Elisa Automate, a fully automated Network Operations Centre (NOC), one of three new business concepts that it is selling to others (in this case, telcos), along with Elisa SmartFactory and its video conferencing aggregation service.

Elisa is clearly succeeding, and not just in its financial results. For example, 18% of Finnish business customers say that it is the most innovative IT actor in its market, compared to 6% for CGI and 5% for Fujitsu.

STL Partners has long watched Elisa’s progress with a high degree of fascination. Elisa and its Finnish peers are a little like the Galapagos Islands of telecoms evolution but marked extraordinary by their distinctive approaches rather than extreme geographical isolation.

Contents:

  • Introduction
  • Elisa: creating an innovator
  • Building a stable foundation for innovation
  • Making the most of Finland’s advantages
  • The genesis of Elisa Automate
  • The early drivers of automation
  • The move towards ‘zero touch’
  • Augmenting human intelligence
  • Automation supports rapid mobile service revenue growth
  • Commercialising the opportunity
  • The value proposition
  • Customer spotlight: Orange Spain
  • Conclusions

Figures:

  1. Elisa’s share price has quadrupled since 2009
  2. Elisa’s data traffic has grown massively
  3. Elisa has grown its revenues and EBIT too
  4. Elisa’s Capex/Revenue ratio is surprisingly low and stable
  5. Elisa shares data showing network performance improvements through automation

Telco AI: How to organise and partner for maximum success

Not a passing fad: AI is becoming a core capability for telcos

Artificial intelligence (AI) has become a key enabler of the digital transformation journey for service providers in the telecoms industry, providing them with the insights and capabilities they need to be more agile and take a more software-centric approach to their role.

The document was researched and written independently by STL Partners, supported by Nokia. STL’s conclusions are entirely independent and build on ongoing research into the future of telecoms. STL Partners has been writing about telcos’ AI opportunities since 2016, looking first at how AI might improve the customer experience and then at the critical role AI might play in the future of network operations.

In this report, we provide a comprehensive overview of the state of AI in the telecoms industry. Supported by nearly a dozen in-depth interviews plus an online survey of more than 50 leading telcos around the world, we explore where the industry is looking to progress and how it is planning to do so — and identify the strategic and business opportunities that are being enabled by AI.

This report will be followed by a sequel that quantifies some of the business outcomes telcos can expect from specific AI application areas. In the coming months, we will also publish a report discussing how AI technology is evolving and presenting our vision of the telco AI roadmap.

What is artificial intelligence?

Before going any further, it is important to clarify what we mean by “artificial intelligence”. To us, AI is about using computing capabilities to perform tasks traditionally associated with humans (such as inference, planning, anticipation, prediction and learning) in human-like ways (e.g., autonomous, adaptive). Our definition incorporates machine learning (ML), which we define as a subset of AI that focuses on the ability of machines to receive datasets and adapt responses in pursuit of a goal.

These definitions attempt to encapsulate the distinction between AI and other forms of rules-based automation — although we acknowledge that in practice these lines are easily blurred.

Practically speaking, AI sits on a continuum of other related technologies and concepts, which we have covered at length in our previous reports. Figure 1 illustrates this continuum and depicts the stages we expect telcos will have to go through as they to move from manual to automated and then to AI-augmented processes.

Figure 1: Moving toward AI

The progression of AI maturity in four steps

Source: STL Partners

A long-term ambition for many telcos is to reach the orange zone in Figure 1: a state in which their systems and processes run and learn from themselves with human input limited to the setting of desired business goals. Beyond the targeted use of ML in certain applications, however, the industry and society as a whole are far from realising that ambition. It is still unclear what fully autonomous systems in a telco might look like in practice, let alone whether they will ever be achievable.

Today, most telcos are still figuring out how to play in the blue zone. They’re using targeted data analysis to inform largely human-led decision-making processes, or they’ve implemented some fixed-policy automation where machines follow a script written and inputted by a human. This is valuable work, but it is not the focus of this report. Instead, we focus on the middle section of Figure 1: on those fledging opportunities that move beyond rules-based automation and into the realm of ML-supported automation

Cutting through the hype

AI has generated considerable industry noise and media attention — so much so, in fact, that a recent survey of leading telcos awarded AI the title of “most overhyped emerging technology”. We believe this hype originates in a general lack of understanding of what AI is (and is not), as well as unrealistic expectations about what it can do for a business, how quickly it can be deployed, and how much ongoing work will be needed to manage it. While there is consensus that the technology has great potential, many telcos doubt it will deliver everything that has been promised up to now.

For those disillusioned by the hype, it is worth noting the impact of AI is much likelier to be evolutionary than revolutionary. The line between automation and AI is blurred; so, too, is the progression between the two. While AI has the potential to unlock new business opportunities, realising that potential will require patience and long-term investment.

And yet, the truth is that telcos are uniquely positioned to take full advantage of AI technology — largely because they’re already used to dealing with the huge volumes of data AI relies on. When telcos automate systems, networks and processes — particularly with the injection of AI — they benefit from feedback loops that further improve those automated processes. This drives simplicity in an industry rife with complexity.

The digital transformation we all talk about depends on driving out complexity and becoming more agile, and the only way to do that is by automating intelligently. Looking ahead to the launch of 5G, it will become impossible for telcos to manage billions of connected devices without AI assistance.

Telcos’ current AI focus: Improving speed and efficiency

Key learnings on telco AI initiatives

Through our research, we have identified five primary domains of activity for telcos looking to make use of AI. The first three broadly relate to business process improvement, with the end goal of reducing costs and improving efficiency.

  1. Optimising existing networks and operations. Telcos are using AI not only for network planning and optimisation, but also to improve their human resources, accounting and fraud-management functions. For example, Telefónica has built an ML model capable of monitoring the status of the network, predicting possible failures and an optimising maintenance routes.[1] This has been particularly important in its rollout and maintenance of networks across rural Latin America, where it can take an engineer up to a day to travel to the site of a network fault.
  2. Improving sales and marketing activity. This includes upselling, cross-selling and agent augmentation. Globe Telecom, for example, has created a data-management platform that collates network signal information alongside information from billing and payment systems to provide personalised offers to its mobile customers.[2]
  3. Improving the customer experience. This includes use cases such as fault resolution, churn management, chatbots and virtual assistants. Vodafone has developed the chatbot TOBi, for example, which can handle 70 percent of customer requests and employs ML technology to further improve the support it offers to customers.[3]

The remaining two domains focus on using AI to enable new ways of working that go beyond a telco’s core connectivity offering, with a focus on growing revenues.

  1. Driving (and monetising) customer data. AI can help telcos aggregate massive volumes of anonymised customer data that can then be sold to third parties. Singtel’s DataSpark has taken a step down this data-as-a-service route, providing access to GPS and mobile network data that other organisations can incorporate into their applications and services.[4]
  2. Enabling or supporting new services. This includes cybersecurity and predictive analytics. As an example, AT&T is using ML to quickly identify normal and abnormal activity in it networks.[5] This sort of solution could be sold as a managed service to other enterprises in the future, unlocking a new revenue stream.

Contents of the full report include:

  • Executive Summary
  • Not a passing fad: AI is becoming a core capability for telcos
  • What is artificial intelligence?
  • Cutting through the hype 8
  • Telcos’ current AI focus: Speed and efficiency
  • How are telcos using AI today?
  • Sharing is caring: How telco AI initiatives are organised
  • Centralised AI initiatives
  • Cross-functional R&D units
  • Individual AI initiatives
  • The stumbling blocks for AI implementation — and how to get around them
  • AI initiatives need to be powered by high-quality data
  • Data governance is an essential requirement
  • Exploring the link between data maturity and AI success
  • The tricky transition from the lab to in-field deployment
  • Accept failure and embrace innovation
  • Revamp partnership strategies
  • New challenges, new expectations
  • Finding the impact: How telcos assess the benefits of AI
  • Different types of telcos, different levels of AI maturity
  • Conclusion

Figures:

  1. Moving toward AI
  2. Telco AI initiatives by domain
  3. Centrally coordinated AI initiatives are more likely to scale
  4. Poor data and a lack of internal skills are key challenges
  5. Telcos struggle with data management at every step of the AI journey
  6. Issues with data governance do not preclude AI implementation
  7. Only 1 in 5 AI projects has advanced to live deployment
  8. Collaborative partnering is key to AI success
  9. Nearly half of telcos have not gone live with AI
  10. Fixed-line and wholesale operators lag behind


[1] Source: Telefónica

[2] Source: Cloudera

[3] Source: Vodafone

[4] Source: DataSpark

[5] Source: AT&T

Elisa Smart Factory: How to win over industry leaders in two years

Elisa’s Smart Factory solution

As STL Partners has described in The Coordination Age: A third age of telecoms, moves are afoot in the global digital economy to improve the efficiency of resource utilisation by combining the digital and physical worlds in new and innovative ways. Elisa’s Smart Factory solution is a prime example of how telcos can address this need.

Coordinating manufacturing

In the case of manufacturing industries, understanding and managing the flow and progress of materials and goods through production processes has long been a critical component of business success.

Managing and continually improving complex processes is central to operational success on the supply-side of the manufacturing industry. This includes everything from a floor manager overseeing production, to time-and-motion studies, total quality management, just-in-time production, robotics and automation, and many other managerial and operational approaches.

A number of new concepts and practices are now emerging, driven by the same imperatives but arising to a degree independently and in different disciplines, for example:

  • Industry 4.0 ‘the fourth industrial revolution’ – the trend of automation and data exchange in manufacturing industries
  • Digital twins – a virtualised version of a real thing, a bit like an avatar but for a thing rather than a person. It can simulate the real item, interact with it, and exchange information and commands with other digital twins based on pre-defined rules
  • The Industrial Internet of Things (IIoT) – connecting industrial devices, sensors, equipment, etc., to gather and exchange information, and sometimes perform remote control

Numerous companies have embarked on the journey to incorporate and use such connected technologies. However the degree of progress made varies greatly.

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A growing industry

Connecting machinery is far from a new idea. Many industrial machines and processes are already highly connected and automated, and this goes back as far as sixty years in SCADA (Supervisory Control and Data Acquisition) systems in electricity power station control.

What is new is the ability and desire to link these systems together and allow data exchange and a degree of autonomy within managed bounds. This can optimise performance, improve productivity, and ultimately lead to new operational business models.

There are many different possible paths to achieving these ends. For instance, powerful industrial players and consortia are all trying to establish leadership in different ways. Heavyweight contenders on the industry side include GE, Bosch, Siemens, and PTC, with consortia including the somewhat mystically titled All Seeing Alliance.

STL Partners will explore the wider opportunity and main players competing in this field in an upcoming report titled ‘Why we need an Internet for Things’.

Enter Elisa, the innovative Finlander

Elisa is the leading Finnish mobile and fixed operator and No.2 player in Estonia. It has 6.2 million customers.

Yet despite its relatively small footprint compared to some of the industry giants, STL Partners regards Elisa as one of the most innovative operators in the world, and certainly in Europe. Indeed, 18% of Finnish business customers say that it is the most innovative IT actor in its market, compared to 6% for CGI and 5% for Fujitsu.

One of its notable recent innovations is a totally automated Network Operations Centre (NOC). To create this, Elisa had to go through its own journey of process engineering and automation.

Elisa now resells its Elisa Automate NOC solutions to other operators. Similarly, it has leveraged the IP and learning to create Elisa Smart Factory, a solution to help global enterprise customers achieve the levels of success Elisa has achieved itself.

Our thanks to Henri Korpi, EVP New Business Development, and Kari Terho, General Manager, Smart Factory at Elisa, who talked to us openly about the proposition, the business, and how it came into existence.

Contents:

  • Executive Summary 
  • Introduction
  • Understanding manufacturing customers’ problems
  • Unplanned downtime
  • Unstable production quality
  • Lack of visibility
  • Practical obstacles to smart manufacturing
  • How Elisa approached the solution
  • Creating a service operation centre
  • Smart Factory’s claims
  • How did Elisa get here?
  • “There’s loads of discussion of which platform is best. What you actually need is a solution”
  • Conclusions
  • Success factors and lessons for others
  • Challenges
  • Next steps

Figures:

  1. Downtime, data usage and visibility – the three dogs of manufacturing
  2. Elisa Smart Factory Schematic
  3. Elisa Smart Factory screenshot
  4. Typical business objectives of Smart Factory solutions
  5. What an Elisa 3D Digital Twin looks like
  6. A high level view from Elisa’s “End-to-End Cockpit”
  7. Results from Elisa’s automated NOC

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Telecoms data analytics – Where’s the real value?

Why telecoms data analtyics matters

Telecoms data analytics matter because telcos currently face big challenges. As connectivity services are increasingly commoditised, telcos are seeing a steady decline in core revenues. They are at risk of becoming seen as providers of basic utilities, rather than offering innovative services to their customers.

Improved analytics fuels benefits in multiple layers. Initially in the (human) management of operational performance, and then increasingly through using analytics and managed AI to control automation. This can apply to existing and new services.

Future-proofing: why data telecoms analytics are essential to today’s business, 5G and beyond

There is a lot of noise from the telecoms industry around fifth generation (5G) mobile networks and how 5G may provide a renewed source of revenue growth. There is no doubt that 5G will unlock new vertical opportunities for telcos, however, if telcos do not invest in developing additional services, revenues will primarily still come through connectivity. While some may gain a first-mover advantage, over time, 5G will experience the same diminishing returns per user that we have seen with previous generations (see Figure 1). 5G, through connectivity alone, is therefore likely to make only a short-term impact on telco revenue streams.

Figure 1: The effect of increasing 4G subscriber penetration on ARPUs

Source: Data from company filings, analysis by STL Partners

STL Partners has been writing about the commoditisation problem for many years and has seen that operators increasingly accept it as inevitable. Most, in one form or another, are looking beyond connectivity to improve the bottom line. Telcos are adopting two main strategies:

  • build or acquire new revenue streams outside of connectivity
  • cut costs.

The first of these is increasingly popular. Telcos worldwide have accepted the idea that they must develop new capabilities outside of their core service area and find ways to make money from them. These capabilities, and how well they link back to existing connectivity offers, vary widely. For example:

  • Some, realising telcos’ technical expertise, are developing end to end solutions based on new technologies such as multi-access edge computing and 5G. Although technologies such as 5G may not bring sustained growth through connectivity alone, they do offer the potential for telcos to access new areas of the value chain and derive new growth opportunities.
  • Some are developing new services in specific verticals. For example, TELUS in Canada and Telstra in Australia are both building service platforms in the healthcare sector, primarily through acquisitions of health-tech companies.

Unfortunately, due to heavy capex constraints and debt regulation, many telcos face challenges in investing in innovative technologies and only some have shown real success in building new offerings outside of traditional telecoms. All telcos are, however, implementing the second strategy, focussing on cutting costs and driving efficiencies throughout their organisations. Although exploring new verticals and areas of opportunity outside of connectivity is a must to drive sustained growth, in order to defend their territory against the likes of Amazon (who operate on razor thin margins), it is essential that telcos look internally and cut costs across their businesses.

While we see many variants and combinations of these two core strategies across the industry, there is one key element that ties them together. Operators are increasingly taking the view that the key to success – both in building new revenue streams and keeping costs down – is finding ways to make better use of data.

Through their networks and customer interactions, telcos collect a broad array of data. This data comes from both internal (for example data on network performance) and external (for example customer location data and usage data) sources. Telcos can extract and leverage insights from this data more accurately and more quickly through advanced analytics, informing key business decisions, creating efficiencies for internal processes, and unlocking data-enabled new service areas including the facilitation and adoption of technologies like 5G.

Building an advanced analytics capability

High ambitions: data and the AI continuum

When we talk with operators globally about data analytics, a key point of discussion is artificial intelligence (AI). “AI technology” is often cited as a powerful way to cut costs, increase ARPUs, and reduce churn – across an operator’s business. Indeed, at STL Partners we have written extensively about how this could be achieved. However, much of the discussion around AI in the industry is just that – discussion. Many AI solutions are still in their nascent phases and there is a lot more talk than live implementations that deliver measurable business value.

We raise two points to help cut through this hype and understand the real-world value for operators, both in the long and short-term.

  1. All AI is equal, but some AI is more equal than others”. It may seem out of place to paraphrase George Orwell, but the truth is that operators and vendors alike market an increasingly broad set of solutions to customers and the analyst community under the blanket term “AI” (“all AI is equal”). This is often misleading, if not erroneous. “AI” can mean different things depending on who you speak to, ranging from computers following simple instructions or rules set by humans, to more complex fully autonomous computer systems that learn and improve with limited human interaction (“but some AI is more equal than others”). These examples differ strongly – but both fit within a generic definition of “artificial intelligence”.

Agnostic of what you include in your definition of AI, there are clearly tiers of AI solution which are based on the algorithm’s complexity, its ability to implement decisions independently (in terms of rights/permissions and integration with automated processes), and the level of human interaction or guidance necessary. At STL Partners, we have written previously about how we see advanced data analytics and AI as a continuum, with stepping stones on a journey towards the fully autonomous telco (Figure 2) The detailed explanation and formulation of this continuum is more thoroughly explained in a previous instalment of our AI research series.

  1. Most live and scaled deployments fall under our definition of rules based automation. Operators speaking to us about AI tend to want focus on innovative AI use cases that fall in the right-hand side of Figure 2. Examples include automated and self-improving chatbots that can solve any customer query and translate a complaint into a sale, or self-healing networks that fix themselves with no need for engineers to intervene. It’s true that these use cases will deliver high-value for telcos and help to answer the big questions set out above. However, should telcos be prioritising these if their data systems cannot yet tell them which customers are having a poor experience, or give them a full, real-time view of network performance?

Where are operators compared to their AI aspirations

Source: STL Partners

In terms of real progress, we have seen only a handful of leading Tier 1 operators deploying telecoms data analytics solutions that truly fit under the ML/AI banner within our framework. Most operators are still much earlier on in the journey towards automation. Even those pioneer operators have deployed only in specific geographical regions and in specific parts of their business. They face problems in deploying more complex solutions at scale and deriving measurable value.

At STL Partners, we believe that too much focus on a poorly defined end-goal risks stalling necessary work that must be done up-front. Operators should strive for and research innovative uses of data, but we believe the focus in the short-term, for Tier 1 and 2/3 telcos alike, should be on laying the necessary groundwork to ensure that data is accessible and clean, with a clear governance structure, as well as building the analytics capabilities necessary to make full use of it.

Laying the groundwork: stepping stones toward data analytics

There are three key components to building even the most basic data analytics capabilities:

  1. Clean, unified data
  2. The skills and tools to process and analyse it
  3. The ambition and drive to do so – data-centricity

This may seem straightforward but telcos globally (including even the most advanced operators) have faced challenges in meeting these requirements. For example, 77% of the operators we have spoken to stated that data collection and management was a key issue for them in implementing an analytics strategy. Furthermore, over a third of the operators we spoke with mentioned a lack of both internal and external skills with regards to advanced analytics (see Figure 3).

Figure 3: Top 4 issues faced by telcos looking to make use of data

Source: STL Partners research programme, October 2018

In order to overcome the issues listed in Figure 3, and to build future-proof telecoms data analytics capabilities, telcos must develop the three components mentioned above. Without doing this in the short-term, operators will lack the underlying platform from which to springboard into developing innovative solutions that leverage AI or ML.

Contents:

  • Executive Summary
  • Future-proofing: what to do?
  • Building an advanced telecoms data analytics capability
  • High ambitions: data and the AI continuum
  • Laying the groundwork: stepping stones toward data analytics
  • In practice: Assessing real analytics use cases
  • Improve business as usual
  • Monetise user data
  • Enable next-generation services
  • Conclusions
  • Key recommendations
  • Conclusion

Figures:

  • Figure 1: The effect of increasing 4G subscriber penetration on ARPUs
  • Figure 2: The journey to AI and telco automation
  • Figure 3: Top 4 issues faced by telcos looking to make use of data
  • Figure 4: Telefónica’s data management structure across multiple opcos
  • Figure 5: What is your biggest challenge in leveraging analytics?
  • Figure 6: The opportunity areas for telcos in advanced analytics
  • Figure 7: A comparison of Iliad against the leading Italian operators
  • Figure 8: A graphical representation of KPN’s Data Services Hub
  • Figure 9: Where operators are compared to their AI aspirations

How Zain Bahrain simplified and digitised customer engagement

Introduction

Increasing pressure on the telecoms business model…

Data volumes and revenues continue to grow globally (albeit at a slower rate than before). However, as competition to win market share intensifies, prices are being driven down. As many markets are fully penetrated, the downward price pressure and lower average revenue per user (ARPU) is causing a rapid slowing in global mobile telecoms revenue growth. And, with a high fixed capital and operating cost base, it is unsurprising that telecoms operators are facing a margin squeeze. This situation is clearly illustrated in Figures 1 and 2.

Figure 1: Global wireless telecommunications revenue and EBITDA margin 2012-2016

Source: Telegeography, STL Partners

Figure 2: Regional blended ARPU 2012 & 2017 (USD constant exchange rate)

 

Source: Telegeography, STL Partners

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…is driving the need for cost efficiencies as well as improved customer experiences

To increase or keep margins stable, telcos face the additional pressure of reducing costs through greater automation and process simplicity. Such a reduction in costs would usually be driven by a reduction in workforce and lower network and IT costs. However, operators are faced with new competitors providing alternate communications services (IM, VOIP, social networking) as well as fierce traditional competition and so must improve the quality of their customers’ experiences.

To illustrate, consider Figure 3, which represents the average “Net Promoter Score” (NPS) for several industries. Telecommunications significantly underperforms relative to other industries, with a NPS of 24 – lagging far behind industries such as transportation and retail. These factors all paint a sobering picture for telcos.

Figure 3: NPS by industry, 2018

Source: CustomerGauge

This situation has created a dilemma for telcos – how can they both reduce costs and improve customer experience simultaneously? This is particularly relevant given the notion that improving customer experience is costly and requires investment in multiple channels.

Figure 4: Telcos traditionally face a trade-off between quality of service and running costs but technology potentially solves this dilemma

Source: STL Partners

One telco that has made steps towards achieving this is Zain Bahrain.

Contents:

  • Executive Summary
  • Introduction
  • Increasing pressure on the telecoms business model
  • Zain Bahrain: A simplicity success story
  • How Zain Bahrain’s management achieved success
  • 1. Understand the problem
  • 2. Make basic channel modifications
  • 3. Extend digital channel capabilities
  • 4. Educate customers
  • Key lessons for other operators

Figures:

  • Figure 1: Global wireless telecommunications revenue and EBITDA margin 2012-2016
  • Figure 2: Regional blended ARPU 2012 & 2017 (USD constant exchange rate)
  • Figure 3: NPS by industry, 2018
  • Figure 4: Telcos traditionally face a trade-off between quality of service and running costs but technology potentially solves this dilemma
  • Figure 5: Zain Bahrain NPS Q1 2017- Q4 2017
  • Figure 6: Zain Bahrain channel roles
  • Figure 7: Mobile application – 2017 results
  • Figure 8: Zain Bahrain customer interactions by channel Q1 2017 – Q1 2018
  • Figure 9: Channel mapping
  • Figure 10: Zain mobile app promotion
  • Figure 11: Scratch and win promotion

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IoT and blockchain: There’s substance behind the hype

Introduction

There is currently a lot of market speculation about blockchain and its possible use-cases, including how it can be used in the IoT ecosystem.

This short report identifies three different reasons why blockchain is an attractive technology to use in IoT solutions, and how blockchain can help operators move up the IoT value chain by enabling new business models.

This report leverages research from the following recent STL publications:

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The IoT ecosystem is evolving rapidly, and we are moving towards a hyper-connected and automated future…

Blockchain IoT

Source: STL Partners

This future vision won’t be possible unless IoT devices from different networks can share data securely. There are three things that make blockchain an attractive technology to help overcome this challenge and enable IoT ecosystems:

  1. It creates a tamper-proof audit trails
  2. It enables a distributed operating model
  3. It is open-source

Contents:

  • Introduction
  • IoT is not a quick win for operators
  • Can blockchain help?
  • The IoT ecosystem is evolving rapidly…
  • The future vision won’t be possible unless IoT devices from different networks can share data securely
  • Application 1: Enhancing IoT device security
  • Use-case 1: Protecting IoT devices with blockchain and biometric data
  • Use-case 2: Preventing losses in the global freight and logistics industry
  • Application 2: Enabling self-managing device-to-device networks
  • Use-case 1: Enabling device-to-device payments
  • Use-case 2: Granting location-access through smart locks
  • Use-case 3: Enabling the ‘sharing economy’
  • Blockchain is not a silver bullet
  • Blockchain in operator IoT strategies

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