A3 in customer experience: Possibilities for personalisation

The value of A3 in customer experience

This report considers the financial value to a telco of using A3 technologies (analytics, automation and AI) to improve customer experience. It examines the key area which underpins much of this financial value – customer support channels – considering the trends in this area and how the area might change in future, shaping the requirement for A3.

Calculating the value of improving customer experience is complex: it can be difficult to identify the specific action that improved a customer’s perception of their experience, and then to assess the impact of this improvement on their subsequent behaviour.

While it is difficult to draw causal links between telcos’ A3 activities and customer perceptions and behaviours, there are still some clearly measurable financial benefits from these investments. We estimate this value by leveraging our broader analysis of the financial value of A3 in telecoms, and then zooming in on the specific pockets of value which relate to improved customer experience (e.g. churn reduction).

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The diagram below illustrates that there are two parts of the customer journey where A3 will add most value to customer experience:

  1. The performance of the network, services, devices and applications is increasingly dependent on automation and intelligence, with the introduction of 5G and cloud-native operations. Without A3 capabilities it will be difficult to meet quality of service standards, understand customer-affecting issues and turn up new services at speed.
  2. The contact centre remains one of the largest influencers of customer experience and one of the biggest users of automation, with the digital channels increasing in importance during the pandemic. Understanding the customer and the agent’s needs and providing information about issues the customer is experiencing to both parties are areas where more A3 should be used in future.

Where is the financial benefit of adding A3 within a typical telco customer journey?

A3 customer experience

Source: STL Partners, Charlotte Patrick Consult

As per this diagram, many of the most valuable uses for A3 are in the contact centre and digital channels. Improvements in customer experience will be tied with trends in both. These priority trends and potential A3 solutions are outlined the following two tables:
• The first shows contact centre priorities,
• The second shows priorities for the digital channels.

Priorities in the contact centre

A3 Contact centre

Priorities in the digital channel

A3 Digital channel

Table of Contents

  • Executive Summary
  • The value of A3 in customer experience
  • Use of A3 to improve customer experience
  • The most important uses of A3 for improving the customer experience
    • Complex data
    • Personalisation
    • Planning
    • Human-machine interaction
    • AI point solution
  • Conclusion
  • Appendix: Methodology for calculating financial value
  • Index

Related Research:

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A3 for enterprise: Where should telcos focus?

A3 capabilities operators can offer enterprise customers

In this research we explore the potential enterprise solutions leveraging analytics, AI and automation (A3) that telcos can offer their enterprise customers. Our research builds on a previous STL Partners report Telco data monetisation: What’s it worth? which modelled the financial opportunity for telco data monetisation – i.e. purely the machine learning (ML) and analytics component of A3 – for 200+ use cases across 13 verticals.

In this report, we expand our analysis to include the importance of different types of AI and automation in implementing the 200+ use cases for enterprises and assess the feasibility for telcos to acquire and integrate those capabilities into their enterprise services.

We identified eight different types of A3 capabilities required to implement our 200+ use cases.

These capability types are organised below roughly in order of the number of use cases for which they are relevant (i.e. people analytics is required in the most use cases, and human learning is needed in the fewest).

The ninth category, Data provision, does not actually require any AI or automation skills beyond ML for data management, so we include it in the list primarily because it remains an opportunity for telcos that do not develop additional A3 capabilities for enterprise.

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Most relevant A3 capabilities across 200+ use cases

9-types-of-A3-analytics-AI-automation

Most relevant A3 capabilities for leveraging enterprise solutions

People analytics: This is the strongest opportunity for telcos as it uses their comprehensive customer data. Analytics and machine learning are required for segmentation and personalisation of messaging or action. Any telco with a statistically-relevant market share can create products – although specialist sales capabilities are still essential.

IoT analytics: Although telcos offering IoT products do not immediately have access to the payload data from devices, the largest telcos are offering a range of products which use analytics/ML to detect patterns or spot anomalies from connected sensors and other devices.

Other analytics: Similar to IoT, the majority of other analytics A3 use cases are around pattern or anomaly detection, where integration of telco data can increase the accuracy and success of A3 solutions. Many of the use cases here are very specific to the vertical. For example, risk management in financial services or tracking of electronic prescriptions in healthcare – which means that a telco will need to have existing products and sales capability in these verticals to make it worthwhile adding in new analytics or ML capabilities.

Real time: These use cases mainly need A3 to understand and act on triggers coming from customer behaviour and have mixed appeal to telcos. Telcos already play a significant role in a small number of uses cases, such as mobile marketing. Some telcos are also active in less mature use cases such as patient messaging in healthcare settings (e.g. real-time reminders to take medication or remote monitoring of vulnerable adults). Of the rest of the use cases that require real time automation, a subset could be enhanced with messaging. This would primarily be attractive to mobile operators, especially if they offer broader relevant enterprise solutions – for example, if a telco was involved in a connected public transport solution, then it could also offer passenger messaging.

Remote monitoring/control: Solutions track both things and people and use A3 to spot issues, do diagnostic analysis and prescribe solutions to the problems identified. The larger telcos already have solutions in some verticals, and 5G may bring more opportunities, such as monitoring of remote sites or traffic congestion monitoring.

Video analytics: Where telcos have CCTV implementations or video, there is opportunity to add in analytics solutions (potentially at the edge).

Human interactions: The majority of telco opportunities here relate to the provision of chatbots into enterprise contact centres.

Human learning: A group of low feasibility use cases around training (for example, an engineer on a manufacturing floor who uses a heads-up augmented/virtual reality (AR/VR) display to understand the resolution to a problem in front of them) or information provision (for example, providing retail customers with information via AR applications).

 

Table of Contents

  • Executive Summary
    • Which A3 capabilities should telcos prioritise?
    • What makes an investment worthwhile?
    • Next steps
  • Introduction
  • Vertical opportunities
    • Key takeaways
  • A3 technology: Where should telcos focus?
    • Key takeaways
    • Assessing the telco opportunity for nine A3 capabilities
  • Verizon case study
  • Details of vertical opportunities
  • Conclusion
  • Appendix 1 – full list of 200 use cases

 

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The $300bn COVID digital health dividend

This report introduces a new sizing model for digital healthcare that reflects the recent impact of the COVID pandemic on the sector, with the goal of identifying the new opportunities and risks presented to operators and others attempting or considering investment in the market. A key finding is that market development has been accelerated four years ahead of its prior trajectory, meaning that players should significantly reassess the urgency and scale of their strategic application.

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Why healthcare?

STL Partners has long argued that if telecoms operators want to build new businesses beyond connectivity, they will need 1) clarity on which customer needs to address and 2) long term commitment to investment and innovation to address them. Adding value farther up the value chain requires significant new skills and capabilities, so we believe telecoms operators must be deliberate in their choice of which customers they want to serve, i.e. which verticals, and what they want to do for them. For more detail, see STL Partners’ report How mobile operators can build winning 5G business models.

We believe that healthcare is a vertical that is well suited to telecoms operators’ strategic scope:

  • Healthcare is a consistently growing need in every country in the world
  • It is a big sector that can truly move the needle on telcos’ revenues, accounting for nearly 10% of GDP globally in 2018, up from 8.6% of GDP in 2000 according to WHO data
  • It operates within national economies of scale (even if the technology is global, implementation of that technology requires local knowledge and relationships)
  • The sector has historically been slower than others in its adoption of new technologies, partly due to quality and regulatory demands, factors that telcos are used to dealing with
  • Improving healthcare outcomes is meaningful work that all employees and stakeholders can relate to.

Many telcos also believe that healthcare is a vertical with significant opportunity, as demonstrated by operators’ such as TELUS and Telstra’s big investments into building health IT businesses, and smaller but ongoing efforts from many others. See STL Partners’ report How to crack the healthcare opportunity for profiles of nine telecoms operators’ strategies in the healthcare vertical.

Our research into the telecoms industry’s investment priorities in 2021 shows that the accelerated uptake of digital health solutions throughout the COVID pandemic has only shifted health further up the priority list for operators.

Figure 1: Digital health is among telcos’ top investment priorities in 2021

digital health telecoms priority

Source: STL Partners, Telecoms priorities: Ready for the crunch?

However, few operators have put their full effort into driving the transformation of healthcare delivery and outcomes through digital solutions. From our conversations with operators around the world, we believe this is in large because they are not yet fully convinced that addressing the challenges associated with transforming healthcare – fragmented and complex systems, slow moving public processes, impact on human lives – will pay off. Are they capable of solving these challenges, and is the business opportunity big enough to justify the risk?

Taking a cautious “wait and see” approach to developing a digital health business, launching a couple of trials or PoCs and seeing if they deliver value, or investing in a digital health start-up or two, may have been a viable approach for operators before the COVID pandemic hit, but with the acceleration in digital health adoption this is no longer the case. Now that COVID has forced healthcare providers and patients to embrace new technologies, the proof points and business cases the industry has been demanding have become a lot clearer. As a result, the digital health market is now four years ahead of where it was at the beginning of 2020, so operators seeking to build a business in healthcare should commit now while momentum and appetite for change is strong.

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How is COVID changing healthcare delivery?

The first and most significantly affected area of the digital health landscape throughout 2020 was virtual consultations and telehealth, where almost overnight doctors shifted as many appointments on to phone or video calls as possible. For example, in the UK the proportion of doctor’s visits happening over the phone or video rose from around 13% in late 2019 to 48% at the peak of the pandemic in April-June 2020, while US based virtual consultation provider Teladoc’s total visits tripled between Q219 and Q220, to 2.8mn.

By necessity, regulatory barriers to adoption of virtual consultations were lowered. Other barriers, such as insurers or governments not reimbursing or underpaying doctors for virtual appointments, and organisational and culture barriers among both patients and providers also broke down. The knock on effect has been acceleration across the broader digital health market, in areas such as remote patient monitoring and population level analytics. (See more on the immediate impact of COVID on digital health in STL article How COVID-19 is changing digital health – and what it means for telcos)

The key question is how much of an impact has COVID had, and will it last over the long term? This is what we aim to answer in this report and the accompanying global database tool. Key questions we address in this analysis are:

  • How much has COVID accelerated adoption of digital health applications?
  • What are the cost savings from accelerated uptake of digital health following COVID?
  • Which digital health application areas have been most affected by COVID?
  • Beyond the COVID impact, what is the total potential value of digital health applications for healthcare providers?
  • Which digital health application areas will deliver the biggest cost savings, globally and within specific markets?

To answer these questions we have built a bottom-up forecast model with a focus on the application areas we believe are most relevant to telecoms operators, as illustrated in Figure 2.

Figure 2: Five digital health application areas for telcos

5 digital health application areas

Source: STL Partners

We believe these are most relevant because their high dependence on connectivity, and needs for significant coordination and engagement with a broad range of local stakeholders to succeed, are well aligned with telecoms operators assets. See this STL Partners article for more detail on why these application areas are good entry points for telecoms operators.

NB We chose to omit the Personal health and wellness application area from our bottom-up model. It is a more generic and global application area than the others, dominated by players such as Google/Fitbit and Apple and with little integration thus far into formal healthcare services. While it is nonetheless an area of interest for telecoms operators, especially those that are seeking to build deeper relationships directly with consumers, it is a difficult entry point for telecoms operators seeking to build a healthcare business. This global and consumer focused nature of this application area also means that it is difficult to find reliable local data and quantify its value for healthcare systems.

What are these forecasts for?

Telecoms operators and others should use this forecast analysis to understand the potential value of digital health, including:

  • The size of the digital health opportunity in different markets
  • The market size for new applications across the four areas we modelled (remote patient monitoring, virtual care and telehealth, diagnostics and triage, data and analytics)
  • The relative size of the opportunities across the four application areas in different countries
  • The pace of digital health adoption and market growth in different countries and application areas

In other words, it shows how big the overall digital health market is, how fast it is growing, and which application areas are most valuable and/or growing fastest.

In a follow-up report, we will expand on this analysis to assess how much of this value telecoms operators specifically can capture.

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Telco A3: Skilling up for the long term

Telcos must master automation, analytics and AI (A3) skills to remain competitive

A3 will permeate all aspects of telcos’ and their customers’ operations, improving efficiency, customer experience, and the speed of innovation. Therefore, whether a telecoms operator is focused on its core connectivity business, or seeking to build new value beyond connectivity, developing widespread understanding of value of A3 and disseminating fundamental automation and AI skills across the organisation should be a core strategic goal. Our surveys on industry priorities suggest that operators recognise this need, and automation and AI are correspondingly rising up the agenda.

Expected technology priority change by organisation type, May 2020

technology investment priorities telecoms May 2020

*Updated January 2021 survey results will be published soon. Source: STL Partners survey, 222 respondents.

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Key findings on operators’ A3 strategies

Based on deep dive interviews with 8 telcos, as well as insights from 8 more telcos gathered from previous research programmes.

  • Less advanced telcos are creating a set of basic structures and procedures, as well as beginning to develop a single view of the customer
  • Having a single version of the truth appears to be an ongoing issue for all – alongside continued work on data quality
  • As full end-to-end automation is not a realistic goal for the next few years, interviewees were seeking to prioritise the right journeys to be automated in the short term
  • Reskilling and education of staff was an area of importance for many but not all
  • Just one company had less ambitious data-related aims due to the specialist nature of their services and smaller size of the company – saying that they worked with data on an as-needed basis and had no plans to develop dedicated data science headcount

Preparing for the future: There are four areas where A3 will impact telcos’ businesses

four A3 areas impacting telcos

Source: Charlotte Patrick Consult, STL Partners

In this report we outline the skills and capabilities telcos will need in order to navigate these changes. We break out these skills into four layers:

  1. The basic skillset: What operators need to remain competitive over the short term
  2. The next 5 years: The skills virtually all telcos will need to build or acquire to remain competitive in the medium term (exceptions include small or specialist telcos, or those in less competitive markets)
  3. The next 10 years: The skills and organisational changes telcos will need to achieve within a 10 year timeframe to remain competitive in the long term
  4. Beyond connectivity (5–10 year horizon): This includes A3 skills that telcos will need to be successful strategic partners for customers and suppliers, and to thrive in ecosystem business models. These will be essential for telcos seeking to play a coordination role in IoT, edge, or industry ecosystems.

Table of contents

  • Executive Summary
  • Telcos’ current strategic direction
    • Deep dive into 8 operator strategies
    • Overview of 8 more operator strategies
  • How A3 technologies are evolving
    • Deep dive into 40 A3 applications that will impact telcos’ businesses
    • Internal capabilities
    • Customer requirements
    • Technology changes
    • Organisational change
  • A timeline of telco A3 skills evolution
    • The basic skillset
    • The next 5 years
    • The next 10 years
    • Beyond connectivity

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Growing B2B2X: Taking telcos beyond connectivity and 5G

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The telecoms industry is looking to revive growth

Telecoms operators have enjoyed 30 years of strong growth in all major markets. However, the core telecoms industry is showing signs of slowing. Connectivity revenue growth is declining and according to our research, annual growth in mobile operator revenues pre-COVID were converging to 1% across Asia Pacific, North America, and Western Europe. To help reverse this trend, telecoms operators’ have been investing in upgrading networks (fibre, 4G, 5G), enabling them to offer ever-increasing data speeds/plans to gain more customers and at least sustain ARPUs. However, this has resulted in the increasing commoditisation of connectivity as competitors also upgrade their networks. The costs to upgrade networks coupled with reducing margins from commoditisation have made it difficult for operators to invest in new revenue streams beyond core connectivity.

While connectivity remains an essential component in consumer and enterprises’ technology mix, on its own, it no longer solves our most pressing challenges. When the telecoms industry was first founded, over 150 years ago, operators were set up to solve the main challenge of the day, which was overcoming time and distance between people. Starting in the 1990s, alongside the creation of the internet and development of more powerful data networks, today’s global internet players set out to solve the next big challenge – affordable access to information and entertainment. Today, our biggest challenge is the need to make more efficient use of our resources, whether that’s time, assets, knowledge, raw material, etc. Achieving this requires not only connectivity and information, but also a high level of coordination across multiple organisations and systems to get it to the right place, at the right time. We therefore call this the Coordination Age.

Figure 1: New challenges for telecoms in the Coordination AgeThe coordination age overview

Source: STL Partners

In the Coordination Age, ‘things’ – machines, products, buildings, grids, processes – are increasingly connecting with each other as IoT and cloud-based applications become ubiquitous. This is creating an exponential increase in the volume of data available to drive development of advanced analytics and artificial intelligence, which combined with automation can improve productivity and resource efficiency. There are major socioeconomic challenges that society is facing that require better matching of supply and demand, which not only needs real-time communications and information exchange, but also insights and action.

In the Coordination Age, there is unlikely to be a single dominant coordinator for most ecosystems. While telecoms operators may not have all the capabilities and assets to play an important coordination role, especially compared to the Internet giants, they do have the advantage of being regulated and trusted in their local markets. This presents new opportunities for telecom operators in industries with stronger national boundaries. As such, there is a role for telcos to play in other parts of the value chain which will ultimately enable them to unlock new revenue growth (e.g. TELUS Health and Elisa Smart Factory).

New purpose, new role

The Coordination Age has added increased complexity and new B2B2X business model challenges for operators. They are no longer the monopolies of the past, but one of many important players in an increasingly ecosystem-based economy. This requires telcos to take a different approach: one with new purpose, culture, and ways of working. To move beyond purely connecting people and devices to enabling coordination, telcos will need a fundamental shift in vision. Management teams will need to embrace a new corporate purpose aligned with the outcomes their customers are looking for (i.e. greater resource efficiency), and drive this throughout their organisations.

Historically, operators have served all customers – consumers, small and medium-sized enterprises (SMEs), larger enterprises from all verticals and other operators – with a set of horizontal services (voice, messaging, connectivity).  If operators want to move beyond these services, then they will need to develop deep sector expertise. Indeed, telcos are increasingly seeking to play higher up the value chain and leveraging their core assets and capabilities provides an opportunity to do so.

However, in order to drive new revenues beyond connectivity and add value in other parts of the solution stack, telcos need to be able to select their battles carefully because they do not have the scale, expertise or resources to do it all.

Figure 2: Potential telco roles beyond traditional connectivity

Source: STL Partners

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Clearer on the vision, unclear on the execution

Many telcos have a relatively clear idea of where they want to drive new streams of revenue beyond traditional connectivity services. However, they face various technical, strategic and organisational challenges that have inhibited this vision from reaching fruition and have unanswered questions about how they can overcome these. This lack of clarity is further evident by the fact that some telcos have yet to set explicit revenue targets or KPIs for non-connectivity revenue, and those that have set clear quantifiable objectives struggle to define their execution plan or go-to-market strategy. Even operators that have been most successful in building new revenue streams, such as TELUS and Elisa, do not share targets or revenues for their new businesses publicly. This is likely to protect them from short-term demands of most telecoms shareholders, and because, even when profitable, they may not yet be seen as valuable enough to move the needle.

This report focuses not just on telco ambitions in driving B2B2X revenues beyond core connectivity and the different roles they want to play in the value chain, but more importantly on what strategies telcos are adopting to fulfil their ambitions. Within this research, we explore what is required to succeed from both a technological and organisational standpoint. Our findings are based on an interview programme with over 23 operators globally, conducted from June to August 2020. Our participant group spans across different operator types, geographies, and types of roles within the organisation, ensuring we gain insight into a range of unique perspectives.

In this report, we define B2B2X as a business model which supports the dynamic creation and delivery of new services by multiple parties (the Bs) for any type of end-customer (the X), whether they be enterprises or consumers. The complexity of the value chains within B2B2X models requires more openness and flexibility from party providers, given that any provider could be the first or second ‘B’ in the B2B2X acronym. This research is primarily focused on B2B2X strategies for serving enterprise customers.

In essence, our research is focused on answering the following key question: how can operators grow their B2B2X revenues when traditional core connectivity is in decline?

Table of Contents

  • Executive Summary
  • Introduction
    • The telecoms industry is looking to revive growth
    • New purpose, new role
    • Clearer on the vision, unclear on the execution
  • Beyond connectivity, but where to?
    • “Selling the service sandwich”
    • Horizontal play: Being the best application enabler
    • The vertical-specific digital services provider
    • There is no “best” approach: Some will work better for different operators in different situations
    • 5G is a trigger but not the only one
  • Accelerating the shift towards partnerships and ecosystems
    • Some operator ‘ecosystems’ look more like partnerships
    • Not all telcos define ‘ecosystems’ the same way
    • Most telcos focusing on ecosystems want to orchestrate and influence the proposition
    • Many see ecosystems as a key potential route but ecosystems come with new requirements
  • The market is ripe for telco ecosystems
    • The interest in network intelligence is not new but this time is different
    • Telcos can provide unique value by making their networks more accessible
    • But so far, telcos have not fully embraced this vision yet
  • Conclusions and recommendations

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The future of assurance: How to deliver quality of service at the edge

Why does edge assurance matter?

The assurance of telecoms networks is one of the most important application areas for analytics, automation and AI (A3) across telcos operations. In a previous report estimating the potential value of A3 across telcos’ core business, including networks, customer channels, sales and marketing, we estimated that service assurance accounts for nearly 10% of the total potential value of A3 (see the report A3 for telcos: Mapping the financial value). The only area of greater combined value was in resource management across telecoms existing networks and planned deployments.

Within service assurance, the biggest value buckets are self-healing networks, impact on customer experience and churn, and dynamic SLA management. This estimate was developed through a bottom up analysis of specific applications for automation, analytics and AI within each segment, and their potential to deliver cost savings or revenue uplift for an average sized telecoms operator (see the original report for the full methodology).

Breakdown of the value of A3 in service assurance, US$ millions

Breakdown of the value of A3 in service assurance (US$ millions)

Source: STL Partners, Charlotte Patrick Consult

While this previous research demonstrates there is significant value for telcos in improving assurance on their legacy networks, over the next five years edge assurance will become an increasingly important topic for operators.

What we mean by edge assurance is the new capabilities operators will require to enable visibility across much more distributed, cloud-based networks, and monitoring of a wider and more dynamic range of services and devices, in order to deliver high quality experience and self-healing networks. This need is driven by operators’ accelerating adoption of virtualisation and software-defined networking, for example with increasing experimentation and excitement around open RAN, as well as some operators’ ambitions to play a significant role in the edge computing market (see our report Telco edge computing: How to partner with hyperscalers for analysis of telcos’ ambitions in edge computing).

To give an idea of the scale of the challenge ahead of operators in assuring increasingly distributed network functions and infrastructure, STL Partners’ expects a Tier-1 operator will deploy more than 8,000 edge servers to support virtual RAN by 2025 (see Building telco edge infrastructure: MEC, private LTE and vRAN for the full forecasts).

Forecast of Tier 1 operator edge servers by domain

Forecast of Tier-1 operator edge servers by domain

Source: STL Partners

Given this dramatic shift in network operations, without new edge assurance capabilities:

  • A telco will not be able to understand where issues are occurring across the (virtualised) network and the underlying infrastructure, and diagnose the root cause
  • The promises of cost saving and better customer experience from self-healing networks will not be fully realised in next-generation networks
  • Potential revenue generators such as network slicing and URLLC will be of limited value to customers if the telco can’t offer sufficient SLAs on reliability, latency and visibility
  • It will not be possible to make promises to ecosystem partners around service quality.

Despite the significant number of unknowns in the future of telco activities around 5G, IoT and edge computing, this research ventures a framework to allow telcos to plan for their future service assurance needs. The first section describes the drivers affecting telcos decision-making around the types of assurance that they need at the edge. The second sets out products and capabilities that will be required and types of assurance products that telcos could create and monetise.

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Table of contents

  • Executive Summary
    • The three main telco strategies in edge assurance
    • What exactly do telcos need to assure?
  • Why edge assurance matters
  • Factors affecting edge assurance development
    • What are telcos measuring?
    • Internal assurance applications
    • Location of measurement and analysis
    • Ownership status of equipment and assets being assured
    • Requirements of external assurance users
    • Requirements from specific applications
    • Telco business model
  • The status of edge assurance and recommendations for telcos
    • Edge assurance vendors
    • Telco assurance products
  • Appendix

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Reliance Unlimit: How to build a successful IoT ecosystem

Reliance Unlimit’s success so far

Unlimit, Reliance Jio’s standalone IoT business in India, established in 2016, understood from the start that the problem with the IoT wasn’t the availability of technology, but how to quickly pull it all together into a clear, affordable solutions for the end customer. The result is that less than four years later, it has deployed more than 35,000 end-to-end IoT projects for a prestigious portfolio of customers, including Nissan Motor, MG Motor, Bata, DHL, GSK and Unilever. To meet their varying and evolving needs, Unlimit had built a IoT ecosystem of almost 600 partner companies by the end of 2019. Of these, nearly 100 are fully certified partners, with which Unlimit co-innovates solutions tailored to the Indian market.

The state of the IoT: Balancing cost and complexity

In 1968, Theodore Paraskevakos, a Greek American inventor and businessman, explored the idea of making two machines communicate to each other. He first developed a system for transmitting the caller’s number to the receiver’s device. Building on this experiment, in 1977 he founded Metretek Inc, a company that conducted commercial automatic meter reading, which is essentially today’s commercial smart meter. From then, the world of machine to machine communications (M2M) developed rapidly. The objective was mainly to remotely monitor devices in order to understand conditions and performance. The M2M world was strongly telecommunications-oriented and focused on solving specific business problems. Given this narrow focus, there was little diversity in devices, data sets were specific to one or two measurements, and the communications protocols were well known. Given this context, it is fair to describe first-generation M2M solutions as a siloed, with little – if any – interaction with other data and solutions.

The benefits and challenges of the IoT

The purpose of the Internet of Things (IoT) is to open those silos and incorporate solution designers and developers into the operating environment. In this evolved environment, there might be several applications and solutions, each delivering a unique operational benefit. Each of those solutions require different devices, which produce different data. And those devices require life cycle management, the data needs to be analysed to inform better decisions, and automation integrated to improve efficiency in the operational environment. The communication methods between those devices can also vary significantly, depending on the environment, where the data is, and the type of applications and intelligence required. Finally, all this needs to run securely.

Therefore, the IoT has opened the silos, but it has brought complexity. The question is then whether this complexity is worth it for the operational benefits.

There are several studies highlighting the advantages of IoT solutions. The recent Microsoft IoT Signals publication, which surveys over 3000 decision makers in companies operating across different sectors, clearly demonstrates the value that IoT is bringing to organisations. The top three benefits are:

  • 91% of respondents claim that the IoT has increased efficiency
  • 91% of respondents claim that the IoT has increased yield
  • 85% of respondents claim that the IoT has increased quality.

The sectors leading IoT adoption

The same study highlights how these benefits are materialising in different business sectors. According to this study – and many others – manufacturing is seen as a top adopter of IoT solutions, as also highlighted in STL Partners research on the Industrial IoT.

Automotive, supply chain and logistics are other sectors that have widely adopted the IoT. Their leadership comes from a long M2M heritage, since telematics was a core application of M2M, and is an important part of the supply chain and logistics process.

The automotive sector’s early adoption of IoT was also driven by regulatory initiatives in different parts of the world, for instance to support remotely monitored emergency services in case of accidents (e.g. EU eCall). To enable this, M2M SIMs were embedded in cars, and only activated in the case of an accident, sending a message to an emergency centre. From there, the automotive industry and mobile network operators gradually developed a broader range of applications, culminating in the concept of connected cars. The connected car is much more sophisticated than a single emergency SIM – it is an IoT environment in which an array of sensors is gathering different data, sharing that data externally in various forms of V2X settings, supporting in-vehicle infotainment, and also enabling semiautonomous mobility. Sometime in the future, this will mature into fully autonomous mobility.

The complexity of an IoT solution

The connected car clearly represents the evolution from siloed M2M solutions to the IoT with multiple interdependent data sources and solutions. Achieving this has required the integration of various technologies into an IoT architecture, as well as the move towards automation and prediction of events, which requires embedding advanced analytics and AI technology frameworks into the IoT stack.

High level view of an IoT architecture

Overview of IoT architecture

Source: Saverio Romeo, STL Partners

There are five levels on an IoT architecture:

  1. The hardware level includes devices, sensors, gateways and hardware development components such as microcontrollers.
  2. The communication level includes the different types of IoT connectivity (cellular, LP-WAN, fixed, satellite, short-range wireless and others) and the communication protocols used in those forms of connectivity.
  3. The middleware software backend level is a set of software layers that are traditionally called an IoT platform. A high-level breakdown of the IoT platform includes a connectivity management layer, a device management layer, and data management and orchestration, data analytics and visualisations layers.
  4. The application level includes application development enablement tools and the applications themselves. Those tools enable the development of applications using machine-generated data and various other sources of data –all integrated by the IoT platform. It also includes applications that use results of these analytics to enable remote and automated actions on IoT devices.
  5. Vertically across these levels, there is a security layer. Although this is simplified into a single vertical layer, in practice there are separate security features integrated into IoT solutions at each layer of the architecture. Those features work together to offer layer-to-layer and end-to-end security. This is a complex process that required a detailed use of security-by-design methodology.

The IoT architecture is therefore composed of different technological parts that need to be integrated in order to work correctly in the different circumstances of potential deployment. The IoT architecture also needs to enable scalability supporting the expansion of a solution in terms of number of devices and volume and types of data. Each architectural layer is essential for the IoT solution to work, and they must interact with each other harmoniously, but each requires different technological expertise and skills.

An organisation that wants to offer end-to-end IoT solutions must therefore make a strategic choice between “in-house” IoT architecture development, or form strategic partnerships with existing IoT technology platform providers, and integrate their solutions into a coherent architecture to support an IoT ecosystem.

In the following sections of this report, we discuss Unlimit’s decision to take an ecosystem approach to building its IoT business, and the steps it took to get where it is today.

Table of contents

  • Executive Summary
    • Four lessons from Unlimit on building IoT ecosystems
    • How Unlimit built a successful IoT ecosystem
    • What next?
  • The state of the IoT: Balancing cost and complexity
    • The benefits and challenges of the IoT
    • The sectors leading IoT adoption
    • The complexity of an IoT solution
    • The nature of business ecosystems
  • How Unlimit built a successful IoT business
    • So far, Unlimit looks like a success
    • How will Unlimit sustain leadership and growth?
  • Lessons from Unlimit’s experience

End-to-end network automation: Why and how to do it?

Automation, analytics and AI: A3 unlocks value for operators

STL Partners has been writing about automation, artificial intelligence (AI) and data analytics for several years. While the three have overlapping capabilities and often a single use case will rely upon a combination, they are also distinct in their technical outcomes.

Distinctions between the three As

Source: STL Partners

Operators have been heavily investing in A3 use cases for several years and are making significant progress. Efforts can be broadly broken down into five different domains: sales and marketing, customer experience, network planning and operations, service innovation and other operations. Some of these domains, such as sales and marketing and customer experience, are more mature, with significant numbers of use cases moving beyond R&D and PoCs into live, scaled deployments. In comparison, other domains, like service innovation, are typically less mature, despite the potential new revenue opportunities attached to them.

Five A3 use case domains

Source: STL Partners

Use cases often overlap across domains. For example, a Western European operator has implemented an advanced analytics platform that monitors network performance, and outputs a unique KPI that, at a per subscriber level, indicates the customer experience of the network. This can be used to trigger an automated marketing campaign to customers who are experiencing issues with their network performance (e.g. an offer for free mobile hotspot until issues are sorted). In this way, it spans both customer experience and network operations. For the purpose of this paper, however, we will primarily focus on automation use cases in the network domain.  We have modelled the financial value of A3 for telcos: Mapping the financial value.

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The time is ripe for network automation now

Network automation is not new. In fact, it’s been a core part of operator’s network capabilities since Almon Strowger invented the Strowger switch (in 1889), automating the process of the telephone exchange. Anecdotally, Strowger (an undertaker by profession) came up with this invention because the wife of a rival funeral parlour owner, working at the local community switchboard, was redirecting customers calling for Strowger to her own husband’s business.

Early advertising called the Strowger switch the “girl-less, cuss-less, out-of-order-less, wait-less telephone” or, in other words, free from human error and faster than the manual switchboard system. While this example is more than 100 years old, many of the benefits of automation that it achieved are still true today; automation can provide operators with the ability to deliver services on-demand, without the wait, and free from human error (or worse still, malevolent intent).

Despite automation not being a new phenomenon, STL Partners has identified six key reasons why network automation is something operators should prioritise now:

  • Only with automation can operators deliver the degree of agility that customers will demand. Customers today expect the kind of speed, accuracy and flexibility of service that can only be achieved in a cost-effective manner with high degrees of network automation. This can be both consumer customers (e.g. for next generation network services like VR/AR applications, gaming, high-definition video streaming etc.) or enterprise customers (e.g. for creating a network slice that is spun up for a weekend for a specific big event). With networks becoming increasingly customised, operators must automate their systems (across both OSS and BSS) to ensure that they can deliver these services without a drastic increase in their operating costs.
    One  wholesale operator exemplified this shift in expectations when describing their customers, which included several of the big technology companies including Amazon and Google: “They have a pace in their business that is really high and for us to keep up with their requirements and at the same time beat all our competitors we just need to be more automated”. They stated that while other customers may be more flexible and understand that instantiating a new service takes time, the “Big 5” expect services in hours rather than days and weeks.
  • Automation can enable operators to do more, such as play higher up the value chain. External partners have an expectation that telcos are highly skilled at handling data and are highly automated, particularly within the network domain. It is only through investing in internal automation efforts that operators will be able to position themselves as respected partners for services above and beyond pure connectivity. An example of success here would be the Finnish operator Elisa. They invested in automation capabilities for their own network – but subsequently have been able to monetise this externally in the form of Elisa Automate.
    A further example would be STL Partners’ vision of the Coordination Age. There is a role for telcos to play further up the value chain in coordinating across ecosystems – which will ultimately enable them to unlock new verticals and new revenue growth. The telecoms industry already connects some organisations and ecosystems together, so it’s in a strong position to play this coordinating role. But, if they wish to be trusted as ecosystem coordinators, they must first prove their pedigree in these core skills. Or, in other words, if you can’t automate your own systems, customers won’t trust you to be key partners in trying to automate theirs.
  • Automation can free up resource for service innovation. If operators are going to do more, and play a role beyond connectivity, they need to invest more in service innovation. Equally, they must also learn to innovate at a much lower cost, embracing automation alongside principles like agile development and fast fail mentalities. To invest more in service innovation, operators need to reallocate resources from other areas of their business – as most telcos are no longer rapidly growing, resource must be freed up from elsewhere.
    Reducing operating costs is a key way that operators can enable increased investment in innovation – and automation is a key way to achieve this.

A3 can drive savings to redistribute to service innovation

Source: Telecoms operator accounts, STL Partners estimates and analysis

  • 5G won’t fulfil its potential without automation. 5G standards mean that automation is built into the design from the bottom up. Most operators believe that 5G will essentially not be possible without being highly automated, particularly when considering next generation network services such as dynamic network slicing. On top of this, there will be a ranging need for automation outside of the standards – like for efficient cell-site deployment, or more sophisticated optimisation efforts for energy efficiency. Therefore, the capex investment in 5G is a major trigger to invest in automation solutions.
  • Intent-based network automation is a maturing domain. Newer technologies, like artificial intelligence and machine learning, are increasing the capabilities of automation. Traditional automation (such as robotic process automation or RPA) can be used to perform the same tasks as previously were done manually (such as inputting information for VPN provisioning) but in an automated fashion. To achieve this, rules-based scripts are used – where a human inputs exactly what it is they want the machine to do. In comparison, intent-based automation enables engineers to define a particular task (e.g. connectivity between two end-points with particular latency, bandwidth and security requirements) and software converts this request into lower level instructions for the service bearing infrastructure. You can then monitor the success of achieving the original intent.
    Use of AI and ML in conjunction with intent-based automation, can enable operators to move from automation ‘to do what humans can do but faster and more accurately’, to automation to achieve outcomes that could not be achieved in a manual way. ML and AI has a particular role to play in anomaly detection, event clustering and predictive analytics for network operations teams.
    While you can automate without AI and ML, and in fact for many telcos this is still the focus, this new technology is increasing the possibilities of what automation can achieve. 40% of our interviewees had network automation use cases that made some use of AI or ML.
  • Network virtualisation is increasing automation possibilities. As networks are increasingly virtualised, and network functions become software, operators will be afforded a greater ability than ever before to automate management, maintenance and orchestration of network services. Once networks are running on common computing hardware, making changes to the network is, in theory, purely a software change. It is easy to see how, for example, SDN will allow automation of previously human-intensive maintenance tasks. A number of operators have shared that their teams and/or organisations as a whole are thinking of virtualisation, orchestration and automation as coming hand-in-hand.

This report focuses on the opportunities and challenges in network automation. In the future, STL Partners will also look to more deeply evaluate the implications of network automation for governments and regulators, a key stakeholder within this ecosystem.

Table of Contents

  • Executive Summary
    • End-to-end network automation
    • A key opportunity: 6 reasons to focus on network automation now
    • Key recommendations for operators to drive their network automation journey
    • There are challenges operators need to overcome
    • This paper explores a range of network automation use cases
    • STL Partners: Next steps
  • Automation, analytics and AI: A3 unlocks value for operators
    • The time is ripe for network automation now
  • Looking to the future: Operators’ strategy and ambitions
    • Defining end-to-end automation
    • Defining ambitions
  • State of the industry: Network automation today
    • Which networks and what use cases: the breadth of network automation today
    • Removing the human? There is a continuum within automation use cases
    • Strategic challenges: How to effectively prioritise (network) automation efforts
    • Challenges to network automation– people and culture are key to success
  • Conclusions
    • Recommendations for vendors (and others in the ecosystem)
    • Recommendations for operators

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A3 for telcos: Mapping the financial value

What is analytics, AI and automation worth to telecoms operators?

This report is the second in a two-part series mapping the process and assessing the financial value of automation, analytics and artificial intelligence (AI) in telecoms. In the first report, The value of analytics, automation and AI for telcos – Part 1: The telco A3 application map, we outlined which type of technology was best suited to which processes across a telco’s operations.

In this report, we assess the financial value of each of the operational areas, in dollar terms, for an average telco. Based on our assessment of operator financials and operational KPIs, the figure below outlines our assumptions on the characteristics of an “average” telco used as the basis for our financial modelling. The characteristics of this telco are as shown below, with a slight skew towards developed market operator characteristics since this is currently where most industry proof points used in our modelling have been implemented.

The characteristics of an average telco

characteristics of an average telco

Source: STL Partners, Charlotte Patrick Consult

The first report in the series analysed how each A3 technology could be applied similarly across different functional units of a telecoms operator, e.g. machine learning or automation each have similar processes in network management, channel management and sales and marketing.

Evaluating AI and automation use cases in four domains

To measure financial impact, this report returns to a traditional breakdown of value by functional unit within the telco, breaking down into four key areas:

  1. Network operations: Network deployment, management and maintenance, and revenue management
  2. Fraud: Including services, online, and internal fraud risks
  3. Customer care: Including all assisted and unassisted channels
  4. Marketing and sales: Understanding customers, managing products, marketing programs, lead management and sales processes.

Through an assessment of nearly 150 individual process areas across a telecoms operator’s core operations, we estimate that A3 can deliver the average telco more than $1 billion dollars in value per year, through a combination of revenue uplift and opex and capex savings, equivalent to 7% of total annual revenues.

As illustrated below, core network operations management accounts for by far the greatest proportion of the value.

The relative value of automation, AI and analytics across telco operations

The relative value of AI, automation and analytics across telco operations

Source: STL Partners, Charlotte Patrick Consult

This likely still underrepresents the total, long term potential value of A3 to telcos, since this first iteration does not model the value of A3 processes in areas less unique to telecoms, including supply chain, finance, IT and HR. No doubt that even within the core area of operations, there are potential process areas that have yet to be discovered or proven, and which we have overlooked in our initial attempt to model the value of A3 to telcos. Meanwhile, this is focused purely on telco’s internal operations so also excludes any potential revenue uplift from new A3-enabled services, such as data monetisation or development of AI-as-a-service type solutions.

That said, operators cannot implement all of these processes at once. The enormous challenge of restructuring processes to be more automated and data-centric, putting in place the data management and analytics capabilities, training employees and acquiring new skills, among many others, means that while many leading telcos are well on their way to capturing this value in some areas, very few – if any – have implemented A3 across all process areas. As a benchmark, Telefónica is an industry leader in leveraging automation and AI to improve operational efficiency, and in 2019 it reported total operational savings of €429mn across the entire group. While this is primarily focused on customer facing channels, so likely excludes the value of A3 in many network operations processes, for instance energy efficiency which is delivering significant value to Telefónica and others, it suggests there remains lots of value still to capture.

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Methodology

The financial modelling for the value of A3 was done through an individual assessment of each of the 150+ process areas to understand the main activities within that area of operations, and how automation, analytics and/or machine learning and other AI technologies could be used within those activities. From there, we assess the value of integrating these technologies to existing operational functions to make them more efficient and effective. This means that we do not attribute any additional value to telcos from implementing new technologies that include A3 as a core element of their functionality, e.g. a multi-domain service orchestrator, implemented as part of software-defined networking.

Our bottom up assessment of each process is also validated through real-world proof points from operators or vendors. This means that more speculative areas of A3 application in operators are calculated to offer relatively limited value. As more proof points emerge, we will incorporate them into future iterations.

Table of contents

  • Executive Summary
    • Where is the largest financial benefit from A3?
    • What should telcos prioritise in the short term?
    • How long will it take for telcos to realise this value?
    • What next?
  • Introduction
    • Methodology
  • Breaking down the value of A3 by operational area
    • Network, OSS and BSS
    • Fraud management
    • Care and commercial channels
    • Marketing and sales
  • Conclusions and recommendations

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The value of analytics, automation and AI for telcos – Part 1: The telco A3 application map

Getting to grips with A3

Almost every telco is at some stage of trying to apply analytics, artificial intelligence (AI) and automation (A3) across its organisation and extended value network to improve business results, efficiency and organisational agility.

However, most telcos have taken a fairly scatter-gun approach to deploying these three interrelating technologies, with limited alignment or collaboration across different parts of the business. To become more sophisticated in their adoption of A3, telcos need to develop a C-level plan to manage deployments, empower business units supporting A3 to efficiently deploy resources, and create cross-functional implementations of these technologies.

The first report in this two-part report series supports telcos in this aim through a high-level mapping of the application areas which can be developed by a telco. It illustrates the opportunities and forms the foundation of our ongoing research in A3.

In the second part of the series, we estimate the potential financial value of each of the A3 application areas for telcos. The follow up is now available here: A3 for telcos: Mapping the financial value 

This research builds on STL’s previous reports covering telcos’ early efforts in implementing analytics, AI and automation within specific parts of their operations, as well as benchmarking their progress globally:

Introducing the telco A3 application map

The first section of this report goes further into the use of different types of A3 in the Telco A3 applications map. Our analysis focuses in turn on the six types of problems that are being addressed and how automation, analytics and/or AI can provide solutions – and for which types of problems and in which parts of a telco’s business each of these three technologies can have the greatest impact.

Summarising the six types of problems A3 can help with:

  1. Making sense of complex data – using analytics and ML to identify patterns, diagnose problems and predict/prescribe resolutions
  2. Automating processes – where intelligent automation and RPA helps with decision making, orchestration and completing tasks within telco processes
  3. Personalising customer interactions – where analytics and ML can be used to understand customer data, create segmentation, identify triggers and prescribe actions
  4. Supporting business planning – where analytics and ML can be used in forecasting demand and optimising use of existing assets and future investments
  5. Augmenting human capabilities – this is where AI solutions such as natural language processing and text analytics are used to ‘understand’ and act on human intent or sentiment, or surface information to customers and employees more quickly
  6. Frontier AI solutions – cutting edge AI solutions which have specialist uses within a telco, but are not widely adopted yet

Following our analysis of the key application areas, we look at how A3 is used not only for the individual parts of the business illustrated in the map, but how more sophisticated implementations require significant integration and interdependencies between A3 solutions across multiple areas of a telco’s operations.

It should be noted that this two-part series only considers the application of A3 to telcos’ internal operations and we will consider both the external monetisation of such services and their use in telco products in follow-up reports.Request a report extract

How telcos should use the A3 map

  • Innovation teams within the telco should consider plotting their existing and planned A3 activities on a map such as that shown below
  • This map should be presented to the board and also socialised within IT and support teams such as customer care. It can be used to describe current top-level focus areas and those which are more nascent but considered key in the short and medium-term
  • The map can also be shared with vendor partners and other interested external parties to ensure that they are aware of the company’s priorities.

Table of contents

  • Executive Summary
  • Introduction
  • The A3 problem/solution types
    • Type 1: Complex data uses A3 to conquer size and speed
    • Type 2: Automation to replace or augment human resources
    • Type 3: Personalisation uses algorithms to reveal what’s next
    • Type 4: Bringing optimisation and forecasting into planning
    • Type 5: Augmenting human capabilities focuses on chatbots
    • Type 6: Frontier AI solutions are the leading edge of the A3 future
  • Cross-type applications of A3
    • Concept 1: Sharing data between boxes using a data lake
    • Concept 2: The flow of data across different A3 application areas
  • Appendix 1: Further definition of applications by type
    • Type 1: Making sense of complex data
    • Type 2: Automating processes
    • Type 3: Personalising customer interactions
    • Type 4: Supporting business planning
    • Type 5: Augmenting human capabilities
    • Type 6: Frontier AI solutions
  • Appendix 2

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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The Industrial IoT: What’s the telco opportunity?

The Industrial IoT is a confusing world

This report is the final report in a mini-series about the Internet for Things (I4T), which we see as the next stage of evolution from today’s IoT.

The first report, The IoT is dead: Long live the Internet for Things, outlines why today’s IoT infrastructure is insufficient for meeting businesses’ needs. The main problem with today’s IoT is that every company’s data is locked in its own silo, and one company’s solutions are likely deployed on a different platform than their partners’. So companies can optimise their internal operations, but have limited scope to use IoT to optimise operations involving multiple organisations.

The second report, Digital twins: A catalyst of disruption in the Coordination Age, provides an overview of what a digital twin is, and how they can play a role in overcoming the limitations of today’s IoT industry.

This report looks more closely at the state of development of enterprise and industrial IoT and the leading players in today’s IoT industry, which we believe is a crucial driver of the Coordination Age. In the Coordination Age, we believe the crucial socio-economic need in the world – and therefore the biggest business opportunity – is to make better use of our resources, whether that is time, money, or raw materials. Given the number of people employed in and resources going through industrial processes, figuring out what’s needed to make the industrial IoT reach its full potential is a big part of making this possible.

Three types of IoT

There are three ways of dividing up the types of IoT applications. As described by IoT expert Stacey Higginbotham, each group has distinct needs and priorities based on their main purpose:

  1. Consumer IoT: A connected device, with an interactive app, that provides an additional service to the end user compared with an unconnected version of the device. The additional service is enabled by the insights and data gathered from the device. The key priority for consumer devices is low price point and ease of installation, given most users’ lack of technical expertise.
  2. Enterprise IoT: This includes all the devices and sensors that enterprises are connecting to the internet, e.g. enterprise mobility and fleet tracking. Since every device connected to an enterprise network is a potential point of vulnerability, the primary concern of enterprise IoT is security and device management. This is achieved through documentation of devices on enterprise networks, prioritisation of devices and traffic across multiple types of networks, e.g. depending on speed and security requirements, and access rights controls, to track who is sharing data with whom and when.
  3. Industrial IoT: This field is born out of industrial protocols such as SCADA, which do not currently connect to the internet but rather to an internal control and monitoring system for manufacturing equipment. More recently, enterprises have enhanced these systems with a host of devices connected to IP networks through Wi-Fi or other technologies, and linked legacy monitoring systems to gateways that feed operational data into more user-friendly, cloud-based monitoring and analytics solutions. At this point, the lines between Industrial IoT and Enterprise IoT blur. When the cloud-based systems have the ability to control connected equipment, for instance through firmware updates, security to prevent malicious or unintended risks is paramount. The primary goals in IIoT remain to control and monitor, in order to improve operational efficiency and safety, although with rising security needs.

The Internet for Things (I4T) is in large part about bridging the divide between Enterprise and Industrial IoT. The idea is to be able to share highly sensitive industrial information, such as a change in operational status that will affect a supply chain, or a fault in public infrastructure like roads, rail or electricity grid, that will affect surroundings and require repairs. This requires new solutions that can coordinate and track the movement of Industrial IoT data into Enterprise IoT insights and actions.

Understandably, enterprises are way of opening any vulnerabilities into their operations through deeper or broader connections, so finding a secure way to bring about the I4T is the primary concern.

The proliferation of IoT platforms

Almost every major player in the ICT world is pitching for a role in both Enterprise and Industrial IoT. Most largescale manufacturers and telecoms operators are also trying to carve out a role in the IoT industry.

By and large, these players have developed specific IoT solutions linked to their core businesses, and then expanded by developing some kind of “IoT platform” that brings together a broader range of capabilities across the IT stack necessary to provide end-to-end IoT solutions.
The result is a hugely complex industry with many overlapping and competing “platforms”. Because they all do something different, the term “platform” is often unhelpful in understanding what a company provides.

A company’s “IoT platform” might comprise of any combination of these four layers of the IoT stack, all of which are key components of an end-to-end solution:

  1. Hardware: This is the IoT device or sensor that is used to collect and transmit data. Larger devices may also have inbuilt compute power enabling them to run local analysis on the data collected, in order to curate which data need to be sent to a central repository or other devices.
  2. Connectivity: This is the means by which data is transmitted, including location-based connectivity (Bluetooth, Wi-Fi), to low power wide area over unlicensed spectrum (Sigfox, LoRa), and cellular (NB-IoT, LTE-M, LTE).
  3. IoT service enablement: This is the most nebulous category, because it includes anything that sits as middleware in between connectivity and the end application. The main types of enabling functions are:
    • Cloud compute capacity for storing and analysing data
    • Data management: aggregating, structuring and standardising data from multiple different sources. There are sub-categories within this geared towards specific end applications, such as product or service lifecycle management tools.
    • Device management: device onboarding, monitoring, software updates, and security. Software and security management are often broken out as separate enablement solutions.
    • Connectivity management: orchestrating IoT devices over a variety of networks
    • Data / device visualisation: This includes graphical interfaces for presenting complex data sets and insights, and 3D modelling tools for industrial equipment.
  4. Applications: These leverage tools in the IoT enablement layer to deliver specific insights or trigger actions that deliver a specific outcome to end users, such as predictive maintenance or fleet management. Applications are usually tailored to the specific needs of end users and rarely scale well across multiple industries.

Most “IoT platforms” combine at least two layers across this IoT stack

graphic of 4 layers on the IoT stack

Source: STL Partners

There are two key reasons why platforms offering end-to-end services have dominated the early development of the IoT industry:

  • Enterprises’ most immediate needs have been to have greater visibility into their own operations and make them more efficient. This means IoT initiatives have been driven primarily by business owners, rather than technology teams, who often don’t have the skills to piece together multiple different components by themselves.
  • Although the IoT as a whole is a big business, each individual component to bringing a solution together is relatively small. So companies providing IoT solutions – including telcos – have attempted to capture a larger share of the value chain in order to make it a better business.

Making sense of the confusion

It is a daunting task to work out how to bring IoT into play in any organisation. It requires a thorough re-think of how a business operates, for a start, then tinkering with (or transforming) its core systems and processes, depending on how you approach it.

That’s tricky enough even without the burgeoning market of self-proclaimed “leaders of industrial IoT” and technology players’ “IoT platforms”.

This report does not attempt to answer “what is the best way / platform” for different IoT implementations. There are many other resources available that attempt to offer comparisons to help guide users through the task of picking the right tools for the job.

The objective here is to gain a sense of what is real today, and where the opportunities and gaps are, in order to help telecoms operators and their partners understand how they can help enterprises move beyond the IoT, into the I4T.

 

Table of contents

  • Executive Summary
  • Introduction
    • Three types of IoT
    • The proliferation of IoT platforms
    • Making sense of the confusion
  • The state of the IoT industry
    • In the beginning, there was SCADA
    • Then there were specialised industrial automation systems
    • IoT providers are learning about evolving customer needs
  • Overview of IoT solution providers
    • Generalist scaled IT players
    • The Internet players (Amazon, Google and Microsoft)
    • Large-scale manufacturers
    • Transformation / IoT specialists
    • Big telco vendors
    • Telecoms operators
    • Other connectivity-led players
  • Conclusions and recommendations
    • A buyers’ eye view: Too much choice, not enough agility
    • How telcos can help – and succeed over the long term in IoT

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

AI in customer services: It’s not all about chatbots

Introduction

Internet companies like Google, Apple and Amazon and second tier digital disruptors like Airbnb, Spotify and Netflix have been refining advanced machine learning-enabled analytics to offer increasingly personalised and predictive services. This means consumers increasingly expect a seamless, personalised experience, where service providers anticipate their needs, rather than react to problems after the fact.

If telcos want to regain credibility with consumers, they must develop more personalised and frictionless customer experiences. Many telcos believe that, as for digital native companies, artificial intelligence (AI) technologies can play a crucial role in achieving their goal to reduce costs while improving services.

In this report, we assess the potential value and feasibility of different types of AI applications in customer experience and customer care and outline how telecoms operators should prioritise their efforts. It is based on primary research at STL events, and conversations with telcos, vendors and other industry players. It also builds on previous reports on big data and AI:

Defining AI: An evolution in analytics

In this and following reports, we are using AI as an all-encompassing term for advanced predictive analytics, based on machine learning technologies. Machine learning should be seen as a stage in the evolution of analytics, from basic data analysis, to big data analytics, to fully automated systems where algorithms continually learn from new data to deliver more efficient and effective responses and automated actions.

In our recent report, Big data analytics: Time to up the ante, we emphasized that as telcos have built up their big data programmes, they have gradually replaced siloed legacy infrastructure, where each department has their own databases and data sources, with a horizontally-integrated infrastructure across all departments. This shift enables telcos to run advanced analytics across much broader data sets and use machine learning algorithms to uncover new patterns and correlations across previously segregated data, often described as discovering ‘unknown unknowns’.

Within the field of machine learning, there are three types of algorithms:

  1. Supervised learning: an algorithm is trained on a large set of labelled data (e.g. photos of cats, or examples of spam mail). The algorithm learns to find patterns in the training data, which it then applies to new data samples to predict the correct answer (e.g. one email is spam, another is not). This is good for situations where historical data can predict future events, such as when a customer is likely to churn, or when a location is likely to see a peak in demand for connectivity.
  2. Unsupervised learning: an algorithm is applied to data without labels and with no specific goal, other than to find patterns or structure in the data. This is good for finding new ways to segment customers or detecting outliers, for example fraudulent activity, or an operator’s most and least profitable customers.
  3. Reinforcement learning: an algorithm is given a predefined goal and a set of allowed actions, and is then left to find the most effective way to achieve its goal through trial and error. This is most famously used in gaming, for example by Google’s AlphaGo Zero. Testing different marketing campaigns to achieve the best result is an example of a business application of reinforcement learning.

Deep learning (DL) is an extension of machine learning where there are many more layers in a neural network, allowing the system to work with much larger and more complex data sets, such as images, video, text and audio, in order to identify more subtle patterns.

In most application fields, ML and DL algorithms are not yet at the stage where they can teach themselves what to do, without any guidance and oversight from humans. Most algorithms are also trained for a specific purpose, so their applications are not easily transferrable. But even with these constraints, the benefits of AI for businesses are clear:

  • Self-improving: ML algorithms continuously learn from experience, either from inbuilt rewards systems or guidance from humans
  • High performing: AI can handle huge amounts of data and never sleeps
  • Human-like: it can interact with types of data previously indecipherable by software, enabling it to automate previously uniquely human tasks, e.g. natural language understanding, facial recognition.
  • Wide reaching: AI is suited to automation of both soft (e.g. chatbots, system control) and manual tasks (i.e. robotics)

Contents:

  • Executive Summary
  • Chatbots aren’t an easy win
  • Predictive care: an easier entry point, but with limits
  • So is AI in customer care worth it?
  • Introduction
  • Defining AI: An evolution in analytics
  • STL Partners’ AI Framework: Sizing the opportunity
  • Chatbots: Is it worth the work?
  • What is a chatbot?
  • Most chatbots are not ready to handle customers yet, and vice versa
  • Telenor: Taking an active role in the AI technology revolution
  • T-Mobile Austria (Deutsche Telekom): A more personalised customer experience
  • Recommendations
  • Predictive care & agent assist
  • AT&T is prioritising predictive care
  • Lots of room for improvement in handling customer calls
  • But predictive care can’t solve everything
  • Conclusions

Figures:

  • Figure 1: STL Partners’ AI Framework
  • Figure 2: Defining customer engagement categories
  • Figure 3: Customer experience is telcos’ main current priority with AI
  • Figure 4: The lifecycle of a chatbot/virtual assistant
  • Figure 5: Chatbots are a long way from meeting satisfaction levels of live agents
  • Figure 6: Telecoms operators say they lack the skills to deploy AI
  • Figure 7: Example of a rule-based versus AI-enabled chatbot in financial services
  • Figure 8: DT’s NLU and conversation flow selection and management process
  • Figure 9: Level of customer satisfaction with UK operators’ complaints resolution
  • Figure 10: Top reasons why customers complain to their service providers
  • Figure 11: A clear preference for predictive care
  • Figure 12: Estimate of telco opex breakdown

Understanding Fintech: Why Interest and Investment Has Exploded

Introduction

Why should telcos care about fintech? Telecoms operators have long been interested in financial services, especially consumer-facing financial services. STL Partners has discussed the relationship between telecoms and financial services in a range of prior reports, from Digital Commerce: Show Me the (Mobile) Money, to Apple Pay and Weve Fail: A Wake Up Call, and from Telco-driven Disruption: What NTT DoCoMo, KT, and Globe Got Right, to Digital Commerce 2.0: New $50bn Disruptive Opportunities for Telcos, Banks and Technology Companies.

It is fair to say that telcos have found only mixed success in financial services. While certain operators have had great success in recent years providing mobile money services, there have also been many examples of telco incursions into financial services that have not paid off. On the other hand, there have been many instances of successful disruption in financial services – even technology-led digital disruption. PayPal is the foremost example of a digital business that originally found a niche doing something that banks had made quite laborious – online payments for goods between private individuals – and making it easier. But these disruptions have, to date, been limited and individual. Why, then, should telcos pay attention now?

In the last two years, the wider landscape of financial services has begun to change, as the established players have faced disruption on multiple fronts from a large number of new businesses. This has become known as fintech, and interest and investment are taking off:

Figure 1: Google Trends search on ‘fintech’, 2011 – 2016

Source: Google Trends

Fintech therefore represents a potentially huge shift in the status quo in financial services: this short report provides an overview of this shift. STL Partners will follow up with a report that considers options for telecoms operators, and makes some strategic recommendations.

 

  • Executive Summary
  • Introduction
  • Disrupting the Financial Services Industry
  • Defining fintech
  • Why fintech’s time has come
  • The state of the ecosystem: investment is accelerating
  • Key Capabilities and Service Areas
  • Fintech specific capabilities: doing the same, but differently
  • Fintech service areas: Diverse and developing
  • The Future of Fintech
  • Growth ahead
  • …but there are uncertainties around the future evolution
  • The uncertainties could still play out well for start-ups
  • Conclusion and Outlook

 

  • Figure 1: Google Trends search on ‘fintech’, 2011 – 2016
  • Figure 2: Fintech companies are disrupting financial services
  • Figure 3: Global Investment in Fintech
  • Figure 4: VC-backed Investment in Fintech, by Region
  • Figure 5: A framework for understanding fintech
  • Figure 6: Fintech start-ups within each service area

Digital Health: How Can Telcos Compete with Google, Apple and Microsoft?

Introduction

With the ever-increasing amount of data collected by smartphones, fitness monitors and smart watches, telcos and other digital players are exploring opportunities to create value from consumers’ ability to capture data on many aspects of their own health and physical activity. Connected devices leverage inbuilt sensors and associated apps to collect data about users’ activities, location and habits.

New health-focused platforms are emerging that use the data collected by sensors to advise individual users on how to improve their health (e.g. a reminder to stand up every 60 minutes), while enhancing their ability to share data meaningfully with healthcare providers, whether in-person or remotely. This market has thus far been led by the major Internet and device players, but telecoms operators may be able to act as distributors, enablers/integrators, and, in some cases, even providers of consumer health and wellness apps (e.g., Telefonica’s Saluspot).

High level drivers for the market

At a macro level, there are a number of factors driving digital healthcare.  These include:

  • Population ageing – The number of people globally who are aged over 65 is expected to triple over the next 30 years , and this will create unprecedented demand for healthcare.
  • Rising costs of healthcare provision globally – Serving an aging population, the increase globally in lifestyle and chronic diseases, and rising underlying costs, is pushing up healthcare spending – while at the same time, due to economic pressures there are more limited funds available to pay for this.
  • Limited supply of trained clinicians – Policy issues and changes in job and lifestyle preferences are limiting both educational capacity and ability to recruit and retain appropriately trained healthcare staff in most markets.
  • Shift in funding policy – In many countries, funding for healthcare is shifting away from being based on reimbursement-for-events (e.g., a practice or hospital is paid for every patient visit, for each patient they register, for each vaccination administered), to a greater emphasis on ‘value-based care’ – reimbursement based on successful patient health outcomes.
  • Increased focus on prevention in healthcare provision – in some cases funding is starting to be provided for preventative population health measures, such as weight-loss or quit-smoking programmes.
  • Development of personalised medicine – Personalised medicine is beginning to gain significant attention. It involves the delivery of more effective personalised treatments (and potentially drugs) based on an individual’s specific genomic characteristics, supported by advances in genotyping and analytics, and by ongoing analysis of individual and population health data.
  • Consumerisation of healthcare – There is a general trend for patients – or rather, consumers – to take more responsibility for their own health and their own healthcare, and to demand always-on access both to healthcare and to their own health information, at a level of engagement they choose.

The macro trends above are unlikely to disappear or diminish in the short-to-medium term; and providers, policymakers and payers  are struggling to cope as healthcare systems increasingly fall short of both targets and patients’ expectations.

Digital healthcare will play a key role in addressing the challenges these trends present. It promises better use and sharing of data, of analytics offering deep insight on health trends for individuals and across the wider population, and of the potential for greater convenience, efficacy and reach of healthcare provisioning.

While many (if not most) of the opportunities around digital health will centre on advances in healthcare providers’ ICT systems, there is significant interest in how consumer wellness and fitness apps and devices will contribute to the digital health ecosystem. Consumer digital health and wellness is particularly relevant to two of the trends above: consumerisation of healthcare, and the shift to prevention as a focus of both healthcare providers and payers.

Fitness trackers and smartwatches, and the associated apps for these devices, as well as wellness and fitness apps for smartphone users, could open up new revenue streams for some service providers, as well as a vast amount of personal data that could feed into both medical records and analytics initiatives. The increasing use of online resources by consumers for both health information and consultation, as well as cloud-based storage of and access to their own health data, also creates opportunities to make more timely and effective healthcare interventions.  For telcos, the question is where and how they can play effectively in this market.

Market Trends and Overview

The digital healthcare market is both very large and very diverse. Digital technologies can be applied in many different segments of the healthcare market (see figure below), both to improve efficiency and enable the development of new services, such as automated monitoring of chronic conditions.

The different segments of the digital healthcare market

Source: STL Partners based on categories identified by Venture Scanner

The various segments in Figure 1 are defined as below:

Wellness

  • Mobile fitness and health apps enable consumers to monitor how much exercise they are doing, how much sleep they are getting, their diet and other aspects of their lifestyle.
  • Wearable devices, such as smart watches and fitness bands, are equipped with sensors that collect the data used by fitness and health apps.
  • Electronic health records are a digital record of data and information about an individual’s health, typically collating clinical data from multiple sources and healthcare providers.

Information

  • Services search are digital portals and directories that help individuals find out healthcare information and identify potential service providers.
  • Online health sites and communities provide consumers with information and discussion forums.
  • Healthcare marketing refers to digital activities by healthcare providers to attract people to use their services.

Interactions

  • Payments and insurance – digital apps and services that enable consumers to pay for healthcare or insurance.
  • Patient engagement refers to digital mechanisms, such as apps, through which healthcare providers can interact with the individuals using their services.
  • Doctor networks are online services that enable clinicians to interact with each other and exchange information and advice.

Research

  • Population health management refers to the use of digital tools by clinicians to capture data about groups of patients or individuals that can then be used to inform treatment.
  • Genomics: An individual’s genetic code can be collated in a digital form so it can be used to understand their likely susceptibility specific conditions and treatments.
  • Medical big data involves capturing and analysing large volumes of data from multiple sources to help identify patterns in the progression of specific illnesses and the effectiveness of particular treatment combinations.

In-hospital care

  • Electronic medical records: A digital version of a hospital or clinic’s records of a specific patient. Unlike electronic health records, electronic medical records aren’t designed to be portable across different healthcare providers.
  • Clinical admin: The use of digital technologies to improve the efficiency of healthcare facilities.
  • Robotics: The use of digital machines to perform specific healthcare tasks, such as transporting medicines or spoon-feeding a patient.

In-home care

  • Digital medical devices: All kinds of medical devices, from thermometers to stethoscopes to glucosometers to sophisticated MRI and medical imaging equipment, are increasingly able to capture and transfer data in a digital form.
  • Remote monitoring involves the use of connected sensors to regularly capture and transmit information on a patient’s health. Such tools can be used to help monitor the condition of people with chronic diseases, such as diabetes.
  • Telehealth refers to patient-clinician consultations via a telephone, chat or video call.

The wellness opportunity

This report focuses primarily primarily on the ‘wellness’ segment (highlighted in the figure below), which is experiencing major disruption as a result of devices, apps and services being launched by Apple, Google and Microsoft, but it also touches on some of these players’ activities in other segments.

This report focuses on wellness, which is undergoing major disruption

Source: STL Partners based on categories identified by Venture Scanner

 

  • Executive summary
  • Introduction
  • High level drivers for the market
  • Market Trends and Overview
  • Market size and trends: smartwatches will overtake fitness brands
  • Health app usage has doubled in two years in the U.S.
  • Are consumers really interested in the ‘quantified self’?
  • Barriers and constraining factors for consumer digital health
  • Disruption in Consumer Digital Wellness
  • Case studies: Google, Apple and Microsoft
  • Google: leveraging Android and analytics capabilities
  • Apple: more than the Watch…
  • Microsoft: an innovative but schizophrenic approach
  • Telco Opportunities in Consumer Health
  • Recommendations for telcos

 

  • Figure 1: The different segments of the digital healthcare market
  • Figure 2: This report focuses on wellness, which is undergoing major disruption
  • Figure 3: Consumer digital health and wellness: leading products and services, 2016
  • Figure 4: Wearable Shipments by Type of Device, 2015-2020
  • Figure 5: Wearable OS Worldwide Market Share, 2015 and 2019
  • Figure 6: Take-up of different types of health apps in the U.S. market (2016)
  • Figure 7: % of health wearable and app users willing to share data US market (2016)
  • Figure 8: Elements of the ‘quantified self’, as envisioned by Orange
  • Figure 9: Less than two-third of US wearable buyers wear their acquisition long-term
  • Figure 10: Google Consumer Health and Fitness Initiatives
  • Figure 11: Snapshot of Google Fit User Interface, 2016
  • Figure 12: Google/Alphabet’s areas of focus in the digital healthcare market
  • Figure 13: Apple’s Key Digital Health and Wellness Initiatives
  • Figure 14: Apple Health app interface and dashboard
  • Figure 15: Apple’s ResearchKit-based EpiWatch App
  • Figure 16: Apple’s current areas of focus in the digital healthcare market
  • Figure 17: Microsoft Consumer Fitness/Wellness Device Initiatives
  • Figure 18: Microsoft Health can integrate data from a range of fitness trackers
  • Figure 19: Microsoft Consumer Fitness/Wellness Applications and Services
  • Figure 20: The MDLive Telehealth Proposition, August 2016
  • Figure 21: Microsoft’s areas of focus in the digital healthcare market
  • Figure 22: Telefónica’s Saluspot: Interactive online doctor consultations on-demand

MWC 2016: IoT & Enterprise

IoT Enthusiasm Hits a Peak…

MWC demonstrated beyond a doubt that the IoT merits its recently-awarded reigning spot at the top of the Gartner hype cycle. The vendors present at MWC were more than keen to demonstrate their IoT solutions, using the full range of established and emerging network standards. Notable IoT announcements from operators at the show included AT&T announcing another round of lucrative connected-car deals; and Deutsche Telekom and SKT announcing a major IoT-focused strategic alliance (the Next Generation Enterprise Network Alliance). SKT even showed a range of Android-based devices for dogs, although it couldn’t run to an actual dog to demonstrate them.

There were significant announcements from ARM about low-power chips, from the Linux Foundation about device operating systems, and from Actility, Jasper, Gemalto, and Cisco about service platforms. We also noted that the special relationship between Nokia and Intel touches on the IoT – the two tech vendors took part in a (Narrow Band) NB-IoT trial with Vodafone.

According to one analyst firm, we’re now up to 300 identifiable IoT “platforms” – a testament both to the creative energy being applied to IoT development, and to the potentially crippling degree of fragmentation affecting it. For the industry to progress on IoT, a shakeout – and clearer winners on the standardisation of technologies and platforms – must be coming up somewhere along the line.

The fight between the platforms, however, is not the only important story. There’s also a big question about the level of the IoT stack at which the most value will accrue. The candidates: IoT devices, the network, the service-enablement platform, the data layer, or individual apps? This cuts across the question of which vendor’s platform will ‘win’. There will certainly be multiple IoT platforms that find traction, with some particularly suited to specific verticals and use cases, but understanding where operators and others can most effectively monetise the IoT opportunity is a fundamental question that most players still seem unclear on.

 

  • Executive Summary
  • IoT Enthusiasm Hits a Peak…
  • Identifying IoT value – IT vendor strategies, cognitive computing
  • NB-IoT: the LPWAN option that suits telcos, but does it suit customers?
  • …But the ’50 Billion Connected Device by 2020′ Dream Is Over

 

  • Figure 1: Selected telco involvement in key LPWAN projects
  • Figure 2: This used to say “50bn connected devices”. Now it doesn’t.