Network use metrics: Good versus easy and why it matters

Introduction

Telecoms, like much of the business world, often revolves around measurements, metrics and KPIs. Whether these relate to coverage of networks, net-adds and churn rates of subscribers, or financial metrics such as ARPU, there is a plethora of numerical measures to track.

They are used to determine shifts in performance over time, or benchmark between different companies and countries. Regulators and investors scrutinise the historical data and may set quantitative targets as part of policy or investment criteria.

This report explores the nature of such metrics, how they are (mis)used and how the telecoms sector – and especially its government and regulatory agencies – can refocus on good (i.e., useful, accurate and meaningful) data rather than over-simplistic or just easy-to-collect statistics.

The discussion primarily focuses on those metrics that relate to overall industry trends or sector performance, rather than individual companies’ sales and infrastructure – although many datasets are built by collating multiple companies’ individual data submissions. It considers mechanisms to balance the common “data asymmetry” between internal telco management KPIs and metrics available to outsiders such as policymakers.

A poor metric often has huge inertia and high switching costs. The phenomenon of historical accidents leading to entrenched, long-lasting effects is known as “path dependence”. Telecoms reflects a similar situation – as do many other sub-sectors of the economy. There are many old-fashioned metrics that are no longer really not fit for purpose and even some new ones that are badly-conceived. They often lead to poor regulatory decisions, poor optimisation and investment approaches by service providers, flawed incentives and large tranches of self-congratulatory overhype.

An important question is why some less-than-perfect metrics such as ARPU still have utility – and how and where to continue using them, with awareness of their limitations – or modify them slightly to reflect market reality. Sometimes maintaining continuity and comparability of statistics over time is important. Conversely, other old metrics such as “minutes” of voice telephony actually do more harm than good and should be retired or replaced.

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Looking beyond operator KPIs

Throughout the report, we make a semantic distinction between industry-wide metrics and telco KPIs. KPIs are typically generated for specific individual companies, rather than aggregated across a sector. And while both KPIs and metrics can be retrospective or set as goals, metrics can also be forecast, especially where they link operational data to other underlying variables, such as population, geographic areas or demand (rather than supply).

STL Partners has previous published work on telcos’ external KPIs, including discussion of the focus on “defensive” statistics on core connectivity, “progressive” numbers on new revenue-generating opportunities, and socially-oriented datasets on environmental social and governance (ESG) and staffing. See the figure below.

Types of internal KPIs found in major telcos

Source: STL Partners

Policymakers need metrics

The telecoms policy realm spans everything from national broadband plans to spectrum allocations, decisions about mergers and competition, net neutrality, cybersecurity, citizen inclusion and climate/energy goals. All of them use metrics either during policy development and debate, or as goalposts for quantifying electoral pledges or making regional/international comparisons.

And it is here that an informational battleground lies.

There are usually multiple stakeholder groups in these situations, whether it is incumbents vs. new entrants, tech #1 vs. tech #2, consumers vs. companies, merger proponents vs. critics, or just between different political or ideological tribes and the numerous industry organisations and lobbying institutions that surround them. Everyone involved wants data points that make themselves look good and which allow them to argue for more favourable treatment or more funding.

The underlying driver here is policy rather than performance.

Data asymmetry

A major problem that emerges here is data asymmetry. There is a huge gulf between the operational internal KPIs used by telcos, and those that are typically publicised in corporate reports and presentations or made available in filings to regulators. Automation and analytics technologies generate ever more granular data from networks’ performance and customers’ usage of, and payment for, their services – but these do not get disseminated widely.

Thus, policymakers and regulators often lack the detailed and disaggregated primary information and data resources available to large companies’ internal reporting functions. They typically need to mandate specific (comparable) data releases via operators’ license terms or rely on third-party inputs from sources such as trade associations, vendor analysis, end-user surveys or consultants.

Table of content

Executive Summary
- Key recommendations
- Next steps
Introduction
- Key metrics overview
- KPIs vs. metrics: What’s in a name?
- Who uses telco metrics and why?
- Data used in policy-making and regulation
- Metrics and KPIs enshrined in standards
- Why some stakeholders love “old” metrics
- Granularity
Coverage, deployment and adoption
- Mobile network coverage
- Fixed network deployment/coverage
Usage, speed and traffic metrics
- Voice minutes and messages
- Data traffic volumes
- Network latency
Financial metrics
- Revenue and ARPU
- Capex
Future trends and innovation in metrics
- The impact of changing telecom industry structure
- Why applications matter: FWA, AR/VR, P5G, V2X, etc
- New sources of data and measurements
Conclusion and recommendations
- Recommendations for regulators and policymakers
- Recommendations for fixed and cable operators
- Recommendations for mobile operators
- Recommendations for telecoms vendors
- Recommendations for content, cloud and application providers
- Recommendations for investors and consultants
Appendix
- Key historical metrics: Overview
- How telecoms data is generated
Index

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Uber and Tesla: What telcos should do

Introduction

This report analyses the market position and strategies of Tesla and Uber, two of four Internet-based disruptors that might be able to break into the top tier of consumer Internet players, which is made up of Amazon, Apple, Facebook or Google. The other two challengers – Spotify and Netflix – were the subject of the recent STL Partners report: Can Netflix and Spotify make the leap to the top tier?

Tesla, Uber, Spotify and Netflix are defined by three key factors, which set them aside from their fellow challengers:

Rapid rise: They have become major mainstream players in a short space of time, building world-leading brands that rival those of much older and more established companies.
New thinking: Each of the four have challenged the conventions of the industries in which they operate, driving disruption and forcing incumbents to re-evaluate their business models.
Potential to challenge the dominance of Amazon, Apple, Facebook or Google: This rapid success has allowed the companies to gain dominant positions in their relative sectors, which they could use as a springboard to diversify their business models into parallel verticals. By pursuing these economies of scope, they are treading the path taken by the big four Internet companies.

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This report explores how improvements in digital technologies and consumer electronics are changing the automotive market, enabling Tesla and Uber to rethink personal transport almost from the bottom up. In particular, it considers how self-driving vehicles could become a key platform within the digital economy, offering a range of commerce services linked to transportation and logistics. The report also explores how the high level of regulation in transportation, as in telecoms, is complicating Uber’s efforts to build economies of scale and scope.

The final section provides a high-level overview of the opportunities for telcos as the automobile becomes a major computing and connectivity platform, including partnership strategies, and the implications for telcos if Uber or Tesla were able to make the jump to become a tier one player.

The report builds on the analysis in two previous STL Partners’ executive briefings that explore how artificial intelligence is changing the automotive sector:

Self-driving disruption

Uber, the world’s leading ride-hailing app, and Tesla, the world’s leading producer of all-electric vehicles, could evolve to become tier one players in the digital economy, as the car could eventually become a major control point in the digital value chain. Both companies could use the disruption caused by the arrival of self-driving cars to become a broad digital commerce platform akin to that of Amazon or Google. As well as matching individuals with journeys, Uber is gearing up to use self-driving vehicles to connect people with shops, restaurants, bars and many other merchants and service providers. With a strong brand, Tesla could potentially play a similar role in the premium end of the market as Apple has done in the PC, tablet and smartphone sectors.

However, Uber and Tesla are just two of the scores of technology and automotive companies jostling for a preeminent position in a future in which the car is a major computing and connectivity platform. As well as investing heavily in the development of self-driving technologies, many of these companies are splurging on M&A to get the skills and competences they will need in the personal transportation market of the future. For example, Intel bought Mobileye, a maker of autonomous-driving systems, for US$15.3 billion in March 2017. Delphi, a big auto parts maker, bought nuTonomy, an autonomous vehicle start-up, for US$450 million, and has since reinvented itself as an autonomous vehicle company called Aptiv.

Self-driving vehicles will change the world and the way people live in a myriad of different ways, just as cars themselves transformed society during the 20^th century. Some shops, hotels and restaurants could become mobile, while car parks, garages and even traffic lights could eventually become obsolete, potentially heralding new business opportunities for many kinds of companies, including telcos. But the most important change for Uber and Tesla will be a widespread shift from owning cars to sharing cars.

Contents:

Executive Summary
How Uber and Tesla are creating new opportunities for telcos
Uber’s and Tesla’s future prospects
Lessons for telcos
Introduction
Self-driving disruption
Making car ownership obsolete
From here to autonomy
The convergence of car rental, taxi-hailing and car making
Business models beyond transport
Opportunities for telcos
Uber: At the bleeding edge
Uber’s chequered history
Uber looks beyond the car
Uber’s strengths and weaknesses: From fame to notoriety
Tesla: All electric dreams
Tesla’s strengths and weaknesses: Beautiful but small
Conclusions and lessons for telcos
The future of Uber and Tesla
The future of connected cars
Lessons from Uber and Tesla

Figures:

Figure 1: Self-driving vehicles will become commonplace by 2030
Figure 2: The two different routes to self-driving vehicles
Figure 3: The first self-driving cars could appear within two years
Figure 4: Money is pouring into ride hailing and self-driving companies
Figure 5: Waymo is way ahead with respect to self-driving disengagements
Figure 6: Uber’s vision of a “vertiport” serving a highway intersection
Figure 7: Uber believes VTOL can be much cheaper than helicopters
Figure 8: Uber’s strengths, weaknesses, opportunities and threats (SWOT) analysis
Figure 9: Growth in Tesla’s automotive revenues has been subdued
Figure 10: Tesla’s strengths, weaknesses, opportunities and threats
Figure 11: Tesla loses money most quarters
Figure 12: Tesla is having to cut back on capex

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Autonomous cars: Where’s the money for telcos?

Introduction

Connected cars have been around for about two decades. GM first launched its OnStar in-vehicle communications service in 1996. Although the vast majority of the 1.4 billion cars on the world’s roads still lack embedded cellular connectivity, there is growing demand from drivers for wireless safety and security features, and streamed entertainment and information services. Today, many people simply use their smartphones inside their cars to help them navigate, find local amenities and listen to music.

The falling cost of cellular connectivity and equipment is now making it increasingly cost-effective to equip vehicles with their own cellular modules and antenna to support emergency calls, navigation, vehicle diagnostics and pay-as-you-drive insurance. OnStar, which offers emergency, security, navigation, connections and vehicle manager services across GM’s various vehicle brands, says it now has more than 11 million customers in North America, Europe, China and South America. Moreover, as semi-autonomous cars begin to emerge from the labs, there is growing demand from vehicle manufacturers and technology companies for data on how people drive and the roads they are using. The recent STL Partners report, AI: How telcos can profit from deep learning, describes how companies can use real-world data to teach computers to perform everyday tasks, such as driving a car down a highway.

This report will explore the connected and autonomous vehicle market from telcos’ perspective, focusing on the role they can play in this sector and the business models they should adopt to make the most of the opportunity.

As STL Partners described in the report, The IoT ecosystem and four leading operators’ strategies, telcos are looking to provide more than just connectivity as they strive to monetise the Internet of Things. They are increasingly bundling connectivity with value-added services, such as security, authentication, billing, systems integration and data analytics. However, in the connected vehicle market, specialist technology companies, systems integrators and Internet players are also looking to provide many of the services being targeted by telcos.

Moreover, it is not yet clear to what extent the vehicles of the future will rely on cellular connectivity, rather than short-range wireless systems. Therefore, this report spends some time discussing different connectivity technologies that will enable connected and autonomous vehicles, before estimating the incremental revenues telcos may be able to earn and making some high-level recommendations on how to maximise this opportunity.

Executive Summary
The role of cellular connectivity
High level recommendations
Contents
Introduction
The evolution of connected cars
How to connect cars to cellular networks
What are the opportunities for telcos?
How much cellular connectivity do vehicles need?
Takeaways
The size of the opportunity
How much can telcos charge for in-vehicle connectivity?
How will vehicles use cellular connectivity?
Telco connected car case studies
Vodafone – far-sighted strategy
AT&T – building an enabling ecosystem
Orange – exploring new possibilities with network slicing
SoftBank – developing self-driving buses
Conclusions and Recommendations
High level recommendations
STL Partners and Telco 2.0: Change the Game

Figure 1: Incremental annual revenue estimates by service
Figure 2: Autonomous vehicles will change how we use cars
Figure 3: Vehicles can harness connectivity in many different ways
Figure 4: V2X may require large numbers of simultaneous connections
Figure 5: Annual sales of connected vehicles are rising rapidly
Figure 6: Mobile connectivity in cars will grow quickly
Figure 7: Estimates of what telcos can charge for connected car services
Figure 8: Potential use cases for in-vehicle cellular connectivity
Figure 9: Connectivity complexity profile criteria
Figure 10: Infotainment connectivity complexity profile
Figure 11: In-vehicle infotainment services estimates
Figure 12: Real-time information connectivity complexity profile
Figure 13: Real-time information services estimates
Figure 14: The connectivity complexity profile for deep learning data
Figure 15: Collecting deep learning data services estimates
Figure 16: Insurance and rental services’ connectivity complexity profile
Figure 17: Pay-as-you-drive insurance and rental services estimates
Figure 18: Automated emergency calls’ connectivity complexity profile
Figure 19: Automated emergency calls estimates
Figure 20: Remote monitoring and control connectivity complexity profile
Figure 21: Remote monitoring and control of vehicle services estimates
Figure 22: Fleet management connectivity complexity profile
Figure 23: Fleet management services estimates
Figure 24: Vehicle diagnostics connectivity complexity profile
Figure 25: Vehicle diagnostics and maintenance services estimates
Figure 26: Inter-vehicle coordination connectivity complexity profile
Figure 27: Inter-vehicle coordination revenue estimates
Figure 28: Traffic management connectivity complexity profile
Figure 29: Traffic management revenue estimates
Figure 30: Vodafone Automotive is aiming to be global
Figure 31: Forecasts for incremental annual revenue increase by service

Tag: V2X

Network use metrics: Good versus easy and why it matters

Introduction

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Looking beyond operator KPIs

Types of internal KPIs found in major telcos

Policymakers need metrics

Data asymmetry

Table of content

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