The new telcos: A field guide

Introduction

The traditional industry view is that “telcos” are a well-defined and fairly cohesive group. Industry associations like GSMA, ETNO, CTIA and others have typically been fairly homogeneous collections of fixed or mobile operators, only really varying in size. The third-ranked mobile operator in Bolivia has not really been that different from AT&T or Vodafone in terms of technology, business model or vendor relationships.

Our own company, STL Partners used to have the brand “Telco 2.0”. However, our main baseline assumption then was that the industry was mostly made up the same network operators, but using a new 2.0 set of business models.

This situation is now changing. Telecom service providers – telcos – are starting to emerge in a huge variety of new shapes, sizes and backgrounds. There is fragmentation in technology strategy, target audiences, go-to-market and regional/national/international scope.

This report is not a full explanation of all the different strategies, services and technological architecture. Instead of analysing all of the “metabolic” functions and “evolutionary mechanisms”, this is more of a field-guide to all the new species of telco that the industry is starting to see. More detail on the enablers – such as fibre, 5G and cloud-based infrastructure – and the demand-side (such as vertical industries’ communications needs and applications) can be found in our other output.

The report provides descriptions with broad contours of motivation, service-offerings and implications for incumbents. We are not “taking sides” here. If new telcos push out the older species, that’s just evolution of those “red in tooth and claw”. We’re taking the role of field zoologists, not conservationists.

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Field guides are collections/lists of natural & human phenomena

animal-species-telcos-stl-partners

Source: Amazon, respective publishers’ copyright

The historical landscape

The term “telco” is a little slippery to define, but most observers would likely agree that the “traditional” telecoms industry has mostly been made up of the following groups of CSPs:

  • MNOs: Countries usually have a few major mobile network operators (MNOs) that are typically national, or sometimes regional.
  • Fixed operators: Markets also have infrastructure-based fixed telcos, usually with one (or a small number) that were originally national state-owned monopolies, plus a select number of other licensed providers, often with greenfield FTTX fibre. Some countries have a vibrant array of smaller “AltNets”, or competitive carriers (originally known as CLECs in the US).
  • Converged operators: These combine fixed and mobile operations in the same business or group. Sometimes they are arms-length (or even in different countries), but many try to offer combined or converged service propositions.
  • Wholesale telcos: There is a tier of a few major international operators that provide interconnect services and other capabilities. Often these have been subsidiaries (or joint ventures) of national telcos.

In addition to these, the communications industry in each market has also often had an array of secondary connectivity or telecom service providers as a kind “supporting cast”, which generally have not been viewed as “telecom operators”. This is either because they fall into different regulatory buckets, only target niche markets, or tend to use different technologies. These have included:

  • MVNOs
  • Towercos
  • Internet Exchanges
  • (W)ISPs
  • Satellite operators

Some of these have had a strong overlap with telcos, or have been spun-out or acquired at various times, but they have broadly remained as independent organisations. Importantly, many of these now look much more like “proper telcos” than they did in the past.

Why are “new telcos” emerging now?

To some extent, many of the classes of new telco have been “hiding in plain sight” for some time. MVNOs, towercos and numerous other SPs have been “telcos in all but name”, even if the industry has often ignored them. There has sometimes been a divisive “them and us” categorisation, especially applied when comparing older operators with cloud-based communications companies, or what STL has previously referred to as “under the floor” infrastructure owners. This attitude has been fairly common within governments and regulators, as well as among operator executives and staff.

However, there are now two groups of trends which are leading to the blurring of lines between “proper telcos” and other players:

  • Supply-side trends: The growing availability of the key building blocks of telcos – core networks, spectrum, fibre, equipment, locations and so on – is leading to democratisation. Virtualisation and openness, as well as a push for vendor diversification, is helping make it easier for new entrants, or adjacent players, to build telecom-style networks
  • Demand-side trends: A far richer range of telecom use-cases and customer types is pulling through specialist network builders and operators. These can start with specific geographies, or industry verticals, and then expand from there to other domains. Private 4G/5G networks and remote/underserved locations are good examples which need customisation and specialisation, but there are numerous other demand drivers for new types of service (and service provider), as well as alternative business models.

Taken together, the supply and demand factors are leading to the creation of new types of telcos (sometimes from established SPs, and sometimes greenfield) which are often competing with the incumbents.

While there is a stereotypical lobbying complaint about “level playing fields”, the reality is that there are now a whole range of different telecom “sports” emerging, with competitors arranged on courses, tracks, fields and hills, many of which are inherently not “level”. It’s down to the participants – whether old or new – to train appropriately and use suitable gear for each contest.

Virtualisation & cloudification of networks helps newcomers as well as existing operators

virtualisation-cloudification-networks-STL-Partners

Source: STL Partners

Where are new telcos likeliest to emerge?

Most new telcos tend to focus initially on specific niche markets. Only a handful of recent entrants have raised enough capital to build out entire national networks, either with fixed or mobile networks. Jio, Rakuten Mobile and Dish are all exceptions – and ones which came with a significant industrial heritage and regulatory impetus that enabled them to scale broadly.

Instead, most new service providers have focused on specific domains, with some expanding more broadly at a later point. Examples of the geographic / customer niches for new operators include:

  • Enterprise private 4G/5G networks
  • Rural network services (or other isolated areas like mountains, offshore areas or islands)
  • Municipality / city-level services
  • National backbone fibre networks
  • Critical communications users (e.g. utilities)
  • Wholesale-only / shared infrastructure provision (e.g. neutral host)

This report sets out…

..to through each of the new “species” of telcos in turn. There is a certain level of overlap between the categories, as some organisations are developing networking offers in various domains in parallel (for instance, Cellnex offering towers, private networks, neutral host and RAN outsourcing).

The new telcos have been grouped into categories, based on some broad similarities:

  • “Evolved” traditional telcos: operators, or units of operators, that are recognisable from today’s companies and brands, or are new-entrant “peers” of these.
  • Adjacent wireless providers: these are service provider categories that have been established for many years, but which are now overlapping ever more closely with “traditional” telcos.
  • Enterprise and government telcos: these are other large organisations that are shifting from being “users” of telecoms, or building internal network assets, towards offering public telecom-type services.
  • Others: this is a catch-all category that spans various niche innovation models. One particular group here, decentralised/blockchain-based telcos, is analysed in more detail.

In each case, the category is examined briefly on the basis of:

  • Background and motivation of operators
  • Typical services and infrastructure being deployed
  • Examples (approx. 3-4 of each type)
  • Implications for mainstream telcos

Table of contents

  • Executive Summary
    • Overview
    • New telco categories and service areas
    • Recommendations for traditional fixed/mobile operators
    • Recommendations for vendors and suppliers
    • Recommendations for regulators, governments & advisors
  • Introduction
    • The historical landscape
    • Why are “new telcos” emerging now?
    • Where are new telcos likeliest to emerge?
    • Structure of this document
  • “Evolved” traditional telcos
    • Greenfield national networks
    • Telco systems integration units
    • “Crossover” Mobile, Fixed & cable operators
    • Extra-territorial telcos
  • Adjacent wireless providers
    • Neutral host network providers
    • TowerCos
    • FWA Fixed Wireless Access (WISPs)
    • Satellite players
  • Enterprise & government telcos
    • Industrial / vertical MNOs
    • Utility companies offering commercial telecom services
    • Enterprises’ corporate IT network service groups
    • Governments & public sector
  • New categories
    • Decentralised telcos (blockchain / cryptocurrency-based)
    • Other “new telco” categories
  • Conclusions

Related Research

 

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Innovation leader case study: Telefónica Tech AI of Things

The origins of Telefónica Tech AI of Things

Telefónica LUCA was set up in 2016 to “enable corporate clients to understand their data and encourage a transparent and responsible use of that data”.

Before the creation of LUCA, Telefónica’s focus had been on developing assets and making acquisitions (e.g. Synergic Partners) to build strong internal capabilities around data and analytics – with some data monetisation capabilities housed within their Telefónica Digital unit (a global business unit selling products beyond connectivity, which was disbanded in 2016). Typical projects the team undertook related to using network data to make better decisioning for the network and marketing teams, and providing Telefónica Digital with external monetisation opportunities such as Smart Steps (aggregated, anonymised data for creation of vertical products) and Smart Digits (provision of consent-based data to the advertising industry).

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Creating the autonomous LUCA unit made a statement that Telefónica was serious about its strategy to offer data products to enterprise customers. Quoting from the original press release, “LUCA offered three lines of products and services:

The Business Insights area brings the value of anonymous and aggregated data on Telefónica’s networks for a wide range of clients. This includes Smart Steps, which is focused on mobility analysis solutions for more efficient planning. For example, to optimise transport networks and tourist management in cities, or in the case of a health emergency, in helping to better understand population movements and in limiting the spread of pandemics.

The analytical and external consultancy services for national and international clients will be provided by Synergic Partners, a company specialized in Big Data and Data Science which was acquired by Telefónica at the end of 2015.

Furthermore, LUCA will help its clients by providing BDaaS (Big Data as a Service) to empower clients to get the most out of their own data, using the Telefónica cloud infrastructure.”

The following table shows a timeline from the origins of LUCA in the Telefónica Digital business unit through to its merger into the Telefónica Tech AI of Things business in 2019 – illustrating the progression of its products and other major activities.

Timeline of Telefónica’s data monetisation business

Telefonica-data-monetisation-luca-AI-IoT

Source: STL Partners, Charlotte Patrick Consult

Points to note on the timeline above:

  • Telefónica stood out from its peers with the purchase of Synergic Partners in 2015 (bringing in 120 consultancy headcount). This provided not only another leg to the business with consulting capabilities, but also additional headcount to scope and sell their existing product sets.
  • Looking at the timeline, it took Telefónica two years from this purchase and the establishment LUCA to expand its portfolio. In 2018, a range of new, mainly IoT-related capabilities, were launched, built up from existing projects with individual customers.
  • Telefónica has added machine learning to its products across the timeframe, but in 2019 the development of NLP capability for use in Telefónica’s existing products, and an internal data science platform, were then productised for customers (see below discussion about its Aura product set).
  • As the number of products has expanded, the number of partnerships has also expanded, bringing specific platforms and capabilities which can be combined with Telefónica’s own data capabilities to provide added value (examples include CARTO which creates geographic visualisations of Telefónica’s data).
  • Looking at changing vertical priorities:
    • Telefónica has always been strong in the advertising sector, starting with products from O2 UK in 2012. The exact nature of what it has offered has changed over time and some capabilities have been sold, however, it still has a strong mobile marketing business and expects it data to become of more interest to brands/media agencies as the use of cookies diminishes across the next few years.
    • The retail sector offers opportunity, but has been challenging to target over the years. Although Telefónica has interesting data for retail companies, creating replicable products is challenging as the large retailers each have differing requirements and working with small cell data in-store can be expensive. The product set is therefore currently being simplified, as the pandemic has also reduced demand from retailers.

One of Telefónica’s key capabilities which is not clearly displayed in the timeline is the provision of services to the marketing teams of the various verticals it targets. These include analytics products which Telefónica has developed from its internal capabilities and other functionality such as pricing tools.

The formation of Telefónica Tech

In 2019, Telefónica LUCA became part of the newly formed, autonomous Telefónica Tech business unit. The organisation is split into two business areas: cybersecurity & cloud, and the assets from Telefónica LUCA combined with the IoT unit. The goal of Telefónica Tech is to:

  • Enable the financial markets to clearly see revenue progression. Telefónica’s stated aim is for sustained double digit growth, which it achieved with year-on-year growth of 13.6% in 2020, although the IoT and Big Data segment only grew 0.8% y-o-y in 2020, due to the impact of COVID-19 on IoT deployments, especially in retail. Showing signs of recovery, in H121 revenue growth in the IoT and Big Data segment rose to 8.1% y-o-y, and to 26% y-o-y for the whole of Telefónica Tech.
  • Coordinate innovation, particularly around post-pandemic opportunities such as remote working, e-health, e-commerce and digital transformation
  • Take advantage of global synergies and leveraging existing assets
  • Ease M&A and partnerships activity (it already has 300 partners to better reach new markets, including relations with 60 start-ups across products)
  • Build relationships with cloud providers (it has existing relationships with Microsoft, Google and SAP).

To better leverage existing assets, Telefónica LUCA was integrated with Telefónica’s IoT capabilities to create a more unified set of capabilities:

  1. IoT is seen as an enabling opportunity for AI, which can bring added value to Telefónica’s 10,000 IoT customers (with 35 million live IoT SIMs worldwide). Opportunities include provision of intelligence around “things” (for example, products to analyse sensor data) and then the addition of Business Insight services (i.e. analysis of aggregated, anonymised Telefónica data which adds further insight alongside the data coming from IoT devices).
  2. AI is now often a commodity discussion with C-Level prospects and Telefónica wishes to be seen as a strategic partner. Telefónica’s AI of Things proposition offers an execution layer and integration experts with security-by-design capabilities.
  3. Combining capabilities provides sales teams with an end-to-end value proposition, as the addition of AI is often complimentary to cloud transformation projects and the implementation of digital platforms.

There is a growing ecosystem in IoT and data which will generate more opportunities as both IoT solutions and ML/AI solutions mature, although it is not a straightforward decision for Telefónica on how to compete within this ecosystem.

Table of contents

  • Executive Summary
    • How successful has Telefónica been in data monetisation?
    • Learnings from Telefónica’s experience
    • Key success factors
    • Telefónica’s future strategy
  • Introduction
    • The origins of Telefónica Tech AI of Things
    • The formation of Telefónica Tech
  • Vision, mission and strategy
    • Scaling the business
    • Building a product set
    • Learnings from Telefónica Tech AI of Things
  • Organisational strategy
    • Where should the data monetisation team live?
    • Structure of Telefónica Tech AI of Things Team
    • External partnerships
    • Future plans
  • Data portfolio strategy
    • Tools and infrastructure
    • AI Suite
    • Vertical strategy
    • Product development beyond analytics
  • Conclusion and future moves

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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Telco data monetisation: What’s it worth?

Data revenue opportunities are variable

Monetisation of telco data has been an area of activity for the last six years. However, telcos’ interest levels have varied over time due to the complexity of delivering and selling such a diverse range of products, as well as highly variable revenue opportunities depending on the vertical. Telcos’ appetite to pursue data monetisation has also been heavily impacted by the fortunes of other new telco products, in particular IoT, owing to the link between many data/analytics products and IoT solutions.

This report assesses the opportunity for telcos to monetise their data and provide associated data analytics products in two parts:

  1. First, we look at the range of products and services a telco needs to create in order to deliver financial value.
  2. Then, we explore the main use cases and actual financial value of telco data analytics products across 12 verticals, plus horizontal solutions that apply to multiple verticals.

Telco data monetisation: Calculation methodology

The methodology used to model the financial value of telco data analytics is outlined in the figure below.

  • The starting point for this analysis is 210 data or data analytics use cases, spread across 12 verticals and the horizontal solutions applicable to multiple verticals.
  • We then assess how difficult it is for a telco to address each use case, based on pre-requisite supporting platforms and solutions, regulatory constraints, etc. (shown in red). This evaluation enables us to assess how likely telcos are to develop products for each use case.
  • Thirdly, we assess which types of telco are able to develop the use case (in yellow). For example, telcos in a market with particularly restrictive regulation around use of personal data are simply not able to create certain products.
  • Finally, it is necessary to understand whether the data/analytics products created for a use case can be offered as an independent, standalone product, or more likely to be provided as a bolt-on service to another, pre-existing solution. This question is primarily pertinent in the IoT space where basic data/analytics are likely to be included in the price of the IoT service.
    • For products that we expect to be sold independently, we calculate the potential revenue based on estimated pricing for the type of data product, where known, and likely volumes that a telco will sell in a year.
    • For data analytics products closely linked to IoT, we attach no monetary value.

Calculation methodology for the feasibility and value of telco data monetisation use cases

Rationale behind data monetisation potential

Source: STL Partners, Charlotte Patrick Consult

Viewing the data

Underlying the analysis in this report is a database tool including a detailed assessment of each of the 210 data monetisation use cases we have identified, with numerical analysis and charting capabilities. We know many of our readers will be interested to explore the detailed data, and so have made it available for download on the website in the form of an Excel spreadsheet.

Full use case database and analysis available on our website

Source: STL Partners

Table of Contents

  • Executive Summary
  • Introduction
    • Calculation methodology
  • What is this market worth to telcos?
  • Creating products for data monetisation
    • Telco products for the ecosystem
    • Data and analytics for IoT
    • Use of location in data monetisation
  • Maximising value in different verticals
    • Advertising and market research
    • Agriculture
    • Finance
    • Government
    • Insurance
    • Healthcare
    • Manufacturing
    • Real estate and construction
    • Retail
    • Telecom, media and technology
    • Transportation
    • Utilities
    • Horizontal solutions for all verticals
  • Conclusion and recommendations
    • How to pick a winning project
  • Index

Telcos and enterprise verticals: 5G is not the only opportunity

Introduction

This report outlines key challenges within selected industry verticals, and how telcos can help resolve them with three emerging networking technologies – 5G, IoT and edge computing.

This research builds on many previous reports:

Enterprise services evolve alongside communications and information technologies

The early days of 2G/3G

  • Basic M2M connectivity
  • Early versions of private networks, bypassing the internet for sensitive data transfer

Improving mobility and capacity with 4G and fibre

  • Better connectivity drives demand for video-conferencing and more sophisticated UCaaS
  • Mobile and fixed data connectivity is powerful enough to enable greater enterprise mobility and support the shift towards cloud-based services
  • Different verticals increasingly require bespoke solutions for unique needs – which are easier to deliver through the cloud

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Greater flexibility and frictionless experiences with 5G, IoT and edge computing

  • Increasing complexity of connectivity, IoT, cloud and IT ecosystems are driving demand for flexible yet seamless solutions:
    • Mobile connectivity across geographies without onerous roaming charges
    • Seamless mobile connectivity across multiple networks and technologies, especially in remote areas
    • Frictionless remote set-up of IoT devices shipped directly from manufacturer to live environment
    • Ability to migrate to new technologies seamlessly (e.g. public sector move from TETRA to cellular)
    • All of the above controlled and monitored on user-friendly, cloud-based dashboards
  • The shift from product to service-based business models means a growing number of enterprises want to embed connectivity into their offer to customers
    • i.e. demand for greater control over wholesale connectivity solutions
    • remote maintenance, asset-tracking, etc.

5G applications will arrive at different times…

evolution of 5G technology eMBB, URLLC, private 5G, massiv IoT

Source: STL Partners

…in the meantime, other technologies can help address enterprise needs

The interdependencies between 5G, IoT and edge computing

Source: STL Partners

The problem with 5G for enterprises

  • Most enterprises are not looking at 5G in isolation, but as one of many technologies that will help resolve pain points around efficiency and innovation. The Internet for Things (I4T) and edge computing are two other key technologies that many enterprises need, and which telcos could potentially provide
  • In the long term, STL Partners does not expect 5G connectivity on its own to deliver growth for telcos. So to grow enterprise revenues, telcos should also develop I4T and edge computing solutions
  • But developing expertise in 5G, I4T and edge computing will be expensive and complex to manage
  • Therefore, telcos should start by targeting their investments to meet specific enterprise pain points

This report helps telcos assess how to target their investments by highlighting key pain points in a selection of industry verticals, and how relevant 5G, I4T and edge computing are for solving them.

Sectors with strong demand for all three technologies hold the highest potential value, but this will be difficult for telcos to capture owing to strong competition in I4T and edge computing from other technology companies.

This report covers a selection of verticals that STL Partners has developed knowledge of through research and consulting activities: manufacturing, construction, utilities, agriculture, transport, automotive, healthcare, and sports, media and entertainment. 

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The IoT money problem: 3 options

Introduction

IoT has been a hot topic since 2010, but despite countless IoT initiatives being launched questions remain about how to monetise the opportunity.

This report presents:

  • A top-level summary of our thinking on IoT so far
  • Examples of 12 IoT verticals and over 40 use-cases
  • Case-studies of four telcos’ experimentation in IoT
  • Three potential roles that could help telcos monetise IoT

Overview

In the early days of the IoT (about five years ago) cellular connectivity was expected to play a major role – Ericsson predicted 50 billion connected devices by 2020, 20 billion of which would be cellular.

However, many IoT products have evolved without cellular connectivity, and lower cost connectivity solutions – such as SIGFOX – have had a considerable impact on the market.

Ericsson now forecasts that, although the headline number of around 50 billion connected devices by 2020 will remain the same, just over 1 billion will use cellular.

Despite these changes IoT is still a significant opportunity for telcos, but they need to change their IoT strategy to become more than connectivity providers as the value of this role in the ecosystem is likely to be modest.

Mapping the IoT ecosystem

The term IoT describes a diverse ecosystem covering a wide range of different connectivity types and use-cases. Therefore, to understand IoT better it is necessary to break it down into horizontal layers and vertical segments (see Figure 1).

Figure 1: A simplified map of the IoT ecosystem

Source: STL Partners

We are seeking input from our clients to shape our IoT research and have put together a short survey asking for your thoughts on:

  • What role telcos can play in the IoT ecosystem
  • Which verticals telcos can be successful in
  • What challenges telcos facing in IoT
  • How can STL support telcos developing their IoT strategy

To thank you for your time we will send you a summary of the survey results at the end of June 2017.

…to access the other 28 pages of this 31 page Telco 2.0 Report, including…

  • Introduction
  • Mapping the IoT ecosystem
  • Overview
  • Mapping the IoT ecosystem
  • IoT: A complicated and evolving market
  • Telcos are moving beyond connectivity
  • And use cases are increasing in complexity
  • IoT verticals – different end-customers with different needs
  • 12 examples of IoT verticals
  • What connectivity should telcos provide?
  • Four examples of IoT experimentation
  • Case study 1: AT&T: Vertically-integrated ecosystem architect
  • Case study 2: Vodafone: a ‘connectivity plus’ approach
  • Case study 3: SK Telecom: ecnouraging innovation through interoperability
  • Case study 4: Deutsche Telekom AG: the open platform integrator
  • Three potential monetisation strategies
  • Ecosystem orchestrator
  • Vertical champion
  • Trust broker
  • Conclusions

…and the following figures…   

  • Figure 1: A simplified map of the IoT ecosystem
  • Figure 2: Telcos moving beyond connectivity
  • Figure 3: IoT use cases are increasing in complexity
  • Figure 4: Use cases in manufacturing
  • Figure 5: Use cases in transportation
  • Figure 6: Use cases in utilities
  • Figure 7: Use cases in surveillance
  • Figure 8: Use cases in smart cities
  • Figure 9: Use cases in health & care
  • Figure 10: Use cases in agriculture
  • Figure 11: Use cases in extractive industries
  • Figure 12: Use cases in retail
  • Figure 13: Use cases in finance
  • Figure 14: Use cases in logistics
  • Figure 15: Use cases in smart home / building
  • Figure 16: Connectivity complexity profile for pay-as-you-drive insurance and rental services
  • Figure 17: Telco opportunity for deep learning pay-as-you-drive insurance and rental services

Connected Home: Telcos vs Google (Nest, Apple, Samsung, +…)

Introduction 

On January 13th 2014, Google announced its acquisition of Nest Labs for $3.2bn in cash consideration. Nest Labs, or ‘Nest’ for short, is a home automation company founded in 2010 and based in California which manufactures ‘smart’ thermostats and smoke/carbon monoxide detectors. Prior to this announcement, Google already had an approximately 12% equity stake in Nest following its Series B funding round in 2011.

Google is known as a prolific investor and acquirer of companies: during 2012 and 2013 it spent $17bn on acquisitions alone, which was more than Apple, Microsoft, Facebook and Yahoo combined (at $13bn) . Google has even been known to average one acquisition per week for extended periods of time. Nest, however, was not just any acquisition. For one, whilst the details of the acquisition were being ironed out Nest was separately in the process of raising a new round of investment which implicitly valued it at c. $2bn. Google, therefore, appears to have paid a premium of over 50%.

This analysis can be extended by examining the transaction under three different, but complementary, lights.

Google + Nest: why it’s an interesting and important deal

  • Firstly, looking at Nest’s market capitalisation relative to its established competitors suggests that its long-run growth prospects are seen to be very strong

At the time of the acquisition, estimates placed Nest as selling 100k of its flagship product (the ‘Nest Thermostat’) per month . With each thermostat retailing at c. $250 each, this put its revenue at approximately $300m per annum. Now, looking at the ratio of Nest’s market capitalisation to revenue compared to two of its established competitors (Lennox and Honeywell) tells an interesting story:

Figure 1: Nest vs. competitors’ market capitalisation to revenue

 

Source: Company accounts, Morgan Stanley

Such a disparity suggests that Nest’s long-run growth prospects, in terms of both revenue and free cash flow, are believed to be substantially higher than the industry average. 
  • Secondly, looking at Google’s own market capitalisation suggests that the capital markets see considerable value in (and synergies from) its acquisition of Nest

Prior to the deal’s announcement, Google’s share price was oscillating around the $560 mark. Following the acquisition, Google’s share price began averaging closer to $580. On the day of the announcement itself, Google’s share price increased from $561 to $574 which, crucially, reflected a $9bn increase in market capitalisation . In other words, the value placed on Google by the capital markets increased by nearly 300% of the deal’s value. This is shown in Figure 2 below:

Figure 2: Google’s share price pre- and post-Nest acquisition

 

Source: Google Finance

This implies that the capital markets either see Google as being well positioned to add unique value to Nest, Nest as being able to strongly complement Google’s existing activities, or both.

  • Thirdly, viewing the Nest acquisition in the context of Google’s historic and recent M&A activity shows both its own specific financial significance and the changing face of Google’s acquisitions more generally

At $3.2bn, the acquisition of Nest represents Google’s second largest acquisition of all time. The largest was its purchase of Motorola Mobility in 2011 for $12.5bn, but Google has since reached a deal to sell the majority of its assets (excluding its patent portfolio) to Lenovo for $2.9bn. In other words, Nest is soon to become Google’s largest active, inorganic investment. Google’s ten largest acquisitions, as well as some smaller but important ones, are shown in Figure 3 below:

Figure 3: Selected acquisitions by Google, 2003-14

Source: Various

Beyond its size, the Nest acquisition also continues Google’s recent trend of acquiring companies seemingly less directly related to its core business. For example, it has been investing in artificial intelligence (DeepMind Technologies), robotics (Boston Dynamics, Industrial Perception, Redwood Robotics) and satellite imagery (Skybox Imaging).

Three questions raised by Google’s acquisition of Nest

George Geis, a professor at UCLA, claims that Google develops a series of metrics at an early stage which it later uses to judge whether or not the acquisition has been successful. He further claims that, according to these metrics, Google on average rates two-thirds of its acquisitions as successful. This positive track record, combined with the sheer size of the Nest deal, suggests that the obvious question here is also an important one:

  • What is Nest’s business model? Why did Google spend $3.2bn on Nest?

Nest’s products, the Nest Thermostat and the Nest Protect (smoke/carbon monoxide detector), sit within the relatively young space referred to as the ‘connected home’, which is defined and discussed in more detail here. One natural question following the Nest deal is whether Google’s high-profile involvement and backing of a (leading) company in the connected home space will accelerate its adoption. This suggests the following, more general, question:

  • What does the Nest acquisition mean for the broader connected home market?

Finally, there is a question to be asked around the implications of this deal for Telcos and their partners. Many Telcos are now active in this space, but they are not alone: internet players (e.g. Google and Apple), big technology companies (e.g. Samsung), utilities (e.g. British Gas) and security companies (e.g. ADT) are all increasing their involvement too. With different strategies being adopted by different players, the following question follows naturally:

  • What does the Nest acquisition mean for telcos?

 

  • Executive Summary
  • Introduction
  • Google + Nest: why it’s an interesting and important deal
  • Three questions raised by Google’s acquisition of Nest
  • Understanding Nest and Connected Homes
  • Nest: reinventing everyday objects to make them ‘smart’
  • Nest’s future: more products, more markets
  • A general framework for connected home services
  • Nest’s business model, and how Google plans to get a return on its $3.2bn investment 
  • Domain #1: Revenue from selling Nest devices is of only limited importance to Google
  • Domain #2: Energy demand response is a potentially lucrative opportunity in the connected home
  • Domain #3: Data for advertising is important, but primarily within Google’s broader IoT ambitions
  • Domain #4: Google also sees Nest as partial insurance against IoT-driven disruption
  • Domain #5: Google is pushing into the IoT to enhance its advertising business and explore new monetisation models
  • Implications for Telcos and the Connected Home
  • The connected home is happening now, but customer experience must not be overlooked
  • Telcos can employ a variety of monetisation strategies in the connected home
  • Conclusions

 

  • Figure 1: Nest vs. competitors’ market capitalisation relative to revenue
  • Figure 2: Google’s share price, pre- and post-Nest acquisition
  • Figure 3: Selected acquisitions by Google, 2003-14
  • Figure 4: The Nest Thermostat and Protect
  • Figure 5: Consumer Electronics vs. Electricity Spending by Market
  • Figure 6: A connected home services framework
  • Figure 7: Nest and Google Summary Motivation Matrix
  • Figure 8: Nest hardware revenue and free cash flow forecasts, 2014-23
  • Figure 9: PJM West Wholesale Electricity Prices, 2013
  • Figure 10: Cooling profile during a Rush Hour Rewards episode
  • Figure 11: Nest is attempting to position itself at the centre of the connected home
  • Figure 12: US smartphone market share by operating system (OS), 2005-13
  • Figure 13: Google revenue breakdown, 2013
  • Figure 14: Google – Generic IoT Strategy Map
  • Figure 15: Connected device forecasts, 2010-20
  • Figure 16: Connected home timeline, 1999-Present
  • Figure 17: OnFuture EMEA 2014: The recent surge in interest in the connected home is due to?
  • Figure 18: A spectrum of connected home strategies between B2C and B2B2C (examples)
  • Figure 19: Building, buying or partnering in the connected home (examples)
  • Figure 20: Telco 2.0™ ‘two-sided’ telecoms business model