Tag: Uber

How telcos can provide a tonic for transport

5G can help revolutionise public transport

With the advent of 5G, STL Partners believes telcos have a broad opportunity to help coordinate better use of the world’s resources and assets, as outlined in the report: The Coordination Age: A third age of telecoms. Reliable and ubiquitous connectivity can enable companies and consumers to use digital technologies to efficiently allocate and source assets and resources.

In urban and suburban transport markets, one precious resource is in short supply – space. Trains can be crowded, roads can be congested and there may be nowhere to park. Following the enormous changes in working patterns in the wake of the pandemic, both individuals and policymakers are reviewing their transport choices.

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This report explores how the concept of mobility-as-a-service (MaaS) is evolving, while outlining the challenges facing those companies looking to transform public transport. In particular, it considers how telcos and 5G could support the development and deployment of automated shuttle buses, which are now beginning to appear on the world’s roads. Whereas self-driving cars are taking much longer to develop than their proponents expected, automated shuttle buses look like a more realistic mid-term prospect. Running on relatively short set routes, these vehicles are easier to automate and can be monitored/controlled by dedicated connectivity infrastructure.

This report also examines the role of 5G connectivity in other potentially-disruptive transport propositions, such as remotely controlled hire cars, passenger drones and flying cars, which could emerge over the next decade. It builds on previous STL Partners research including:

Where is transport headed?

Across the world, transport is in a state of flux. Growing congestion, the pandemic, concerns about air quality and climate change, and the emergence of new technologies are taking the transport sector in new directions. Urban planners have long recognised that having large numbers of half-empty cars crawling around at 20km/hour looking for somewhere to park is not a good use of resources.

Experimentation abounds. Many municipalities are building bike lanes and closing roads to try and encourage people to get out of their cars. In response, sales of electric bikes and scooters are rising fast. The past 10 years has also seen a global boom (followed by a partial bust) in micro-mobility services – shared bikes and scooters. Although they haven’t lived up to the initial hype, these sharing economy services have become a key part of the transport mix in many cities (for more on this, see the STL Partners report: Can telcos help cities combat congestion?).

Indeed, these micro-mobility services may be given a shot in the arm by the difficulties faced by the ride hailing business. In many cities, Uber and Lyft are under intense pressure to improve their driver proposition by giving workers more rights, while complying with more stringent safety regulations. That is driving costs upwards. Uber had hoped to ultimately replace human drivers with self-driving vehicles, but that now looks unlikely to happen in the foreseeable future. Tesla, which has always been bullish about the prospects autonomous driving, keeps having to revise its timelines backwards.

Tellingly, the Chinese government has pushed back a target to have more than half of new cars sold to have self-driving capabilities from 2020 to 2025. It blamed technical difficulties, exacerbated by the coronavirus pandemic, in a 2020 statement issued by National Development and Reform Commission and the Ministry of Industry and Information Technology.

Still, self-driving cars will surely arrive eventually. In July, Alphabet (Google’s parent) reported that its experimental self-driving vehicle unit Waymo continues to grow. “People love the fully autonomous ride hailing service in Phoenix,” Sundar Pichai, CEO Alphabet and Google, enthused. “Since first launching its services to the public in October 2020, Waymo has safely served tens of thousands of rides without a human driver in the vehicle, and we look forward to many more.”

In response to analyst questions, Pichai added: “We’ve had very good experience by scaling up rides. These are driverless rides and no one is in the car other than the passengers. And people have had a very positive experience overall. …I expect us to scale up more through the course of 2022.”

More broadly, the immediate priority for many governments will be on greening their transport systems, given the rising public concern about climate change and extreme weather. The latest report from the Intergovernmental Panel on Climate Change calls for “immediate, rapid and large-scale reductions in greenhouse gas emissions” to stabilise the earth’s climate. This pressure will likely increase the pace at which traditional components of the transport system become all-electric – cars, motorbikes, buses, bikes, scooters and even small aircraft are making the transition from relying on fossil fuel or muscle power to relying on batteries.

The rest of this 45-page report explores how public transport is evolving, and the role of 5G connectivity and telcos can play in enabling the shift.

Table of contents

  • Executive Summary
  • Introduction
  • Where is transport headed?
    • Mobility-as-a-service
    • The role of digitisation and data
    • Rethinking the bus
    • Takeaways
  • How telcos are supporting public transport
    • Deutsche Telekom: Trying to digitise transport
    • Telia: Using 5G to support shuttle buses
    • Takeaways
  • The key challenges
    • A complex and multi-faceted value chain
    • Regulatory caution
    • Building viable business models
    • Takeaways
  • Automakers become service providers
    • Volvo to retrieve driving data in real-time
    • Automakers and tech companies team up
    • Takeaways
  • Taxis and buses take to the air
    • The prognosis for passenger drones
    • Takeaways
  • Conclusions: Strategic implications for telcos

 

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Fighting the fakes: How telcos can help

Internet platforms need a frictionless solution to fight the fakes

On the Internet, the old adage, nobody knows you are a dog, can still ring true. All of the major Internet platforms, with the partial exception of Apple, are fighting frauds and fakes. That’s generally because these platforms either allow users to remain anonymous or because they use lax authentication systems that prioritise ease-of-use over rigour. Some people then use the cloak of anonymity in many different ways, such as writing glowing reviews of products they have never used on Amazon (in return for a payment) or enthusiastic reviews of restaurants owned by friends on Tripadvisor. Even the platforms that require users to register financial details are open to abuse. There have been reports of multiple scams on eBay, while regulators have alleged there has been widespread sharing of Uber accounts among drivers in London and other cities.

At the same time, Facebook/WhatsApp, Google/YouTube, Twitter and other social media services are experiencing a deluge of fake news, some of which can be very damaging for society. There has been a mountain of misinformation relating to COVID-19 circulating on social media, such as the notion that if you can hold your breath for 10 seconds, you don’t have the virus. Fake news is alleged to have distorted the outcome of the U.S. presidential election and the Brexit referendum in the U.K.

In essence, the popularity of the major Internet platforms has made them a target for unscrupulous people who want to propagate their world views, promote their products and services, discredit rivals and have ulterior (and potentially criminal) motives for participating in the gig economy.

Although all the leading Internet platforms use tools and reporting mechanisms to combat misuse, they are still beset with problems. In reality, these platforms are walking a tightrope – if they make authentication procedures too cumbersome, they risk losing users to rival platforms, while also incurring additional costs. But if they allow a free-for-all in which anonymity reigns, they risk a major loss of trust in their services.

In STL Partners’ view, the best way to walk this tightrope is to use invisible authentication – the background monitoring of behavioural data to detect suspicious activities. In other words, you keep the Internet platform very open and easy-to-use, but algorithms process the incoming data and learn to detect the patterns that signal potential frauds or fakes. If this idea were taken to an extreme, online interactions and transactions could become completely frictionless. Rather than asking a person to enter a username and password to access a service, they can be identified through the device they are using, their location, the pattern of keystrokes and which features they access once they are logged in. However, the effectiveness of such systems depends heavily on the quality and quantity of data they are feeding on.

In come telcos

This report explores how telcos could use their existing systems and data to help the major Internet companies to build better systems to protect the integrity of their platforms.

It also considers the extent to which telcos will need to work together to effectively fight fraud, just as they do to combat telecoms-related fraud and prevent stolen phones from being used across networks. For most use cases, the telcos in each national market will generally need to provide a common gateway through which a third party could check attributes of the user of a specific mobile phone number. As they plot their way out of the current pandemic, governments are increasingly likely to call for such gateways to help them track the spread of COVID-19 and identify people who may have become infected.

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Using big data to combat fraud

In the financial services sector, artificial intelligence (AI) is now widely used to help detect potentially fraudulent financial transactions. Learning from real-world examples, neural networks can detect the behavioural patterns associated with fraud and how they are changing over time. They can then create a dynamic set of thresholds that can be used to trigger alarms, which could prompt a bank to decline a transaction.

In a white paper published in 2019, IBM claimed its AI and cognitive solutions are having a major impact on transaction monitoring and payment fraud modelling. In one of several case studies, the paper describes how the National Payment Switch in France (STET) is using behavioural information to reduce fraud losses by US$100 million annually. Owned by a consortium of financial institutions, STET processes more than 30 billion credit and debit card, cross-border, domestic and on-us payments annually.

STET now assesses the fraud risk for every authorisation request in real time. The white paper says IBM’s Safer Payments system generates a risk score, which is then passed to banks, issuers and acquirers, which combine it with customer information to make a decision on whether to clear or decline the transaction. IBM claims the system can process up to 1,200 transactions per second, and can compute a risk score in less than 10 milliseconds. While STET itself doesn’t have any customer data or data from other payment channels, the IBM system looks across all transactions, countrywide, as well as creating “deep behavioural profiles for millions of cards and merchants.”

Telcos, or at least the connectivity they provide, are also helping banks combat fraud. If they think a transaction is suspicious, banks will increasingly send a text message or call a customer’s phone to check whether they have actually initiated the transaction. Now, some telcos, such as O2 in the UK, are making this process more robust by enabling banks to check whether the user’s SIM card has been swapped between devices recently or if any call diverts are active – criminals sometimes pose as a specific customer to request a new SIM. All calls and texts to the number are then routed to the SIM in the fraudster’s control, enabling them to activate codes or authorisations needed for online bank transfers, such as a one-time PINs or passwords.

As described below, this is one of the use cases supported by Mobile Connect, a specification developed by the GSMA, to enable mobile operators to take a consistent approach to providing third parties with identification, authentication and attribute-sharing services. The idea behind Mobile Connect is that a third party, such as a bank, can access these services regardless of which operator their customer subscribes to.

Adapting telco authentication for Amazon, Uber and Airbnb

Telcos could also provide Internet platforms, such as Amazon, Uber and Airbnb, with identification, authentication and attribute-sharing services that will help to shore up trust in their services. Building on their nascent anti-fraud offerings for the financial services industry, telcos could act as intermediaries, authenticating specific attributes of an individual without actually sharing personal data with the platform.

STL Partners has identified four broad data sets telcos could use to help combat fraud:

  1. Account activity – checking which individual owns which SIM card and that the SIM hasn’t been swapped recently;
  2. Movement patterns – tracking where people are and where they travel frequently to help identify if they are who they say they are;
  3. Contact patterns – establishing which individuals come into contact with each other regularly;
  4. Spending patterns – monitoring how much money an individual spends on telecoms services.

Table of contents

  • Executive Summary
  • Introduction
  • Using big data to combat fraud
    • Account activity
    • Movement patterns
    • Contact patterns
    • Spending patterns
    • Caveats and considerations
  • Limited progress so far
    • Patchy adoption of Mobile Connect
    • Mobile identification in the UK
    • Turkcell employs machine learning
  • Big Internet use cases
    • Amazon – grappling with fake product reviews
    • Facebook and eBay – also need to clampdown
    • Google Maps and Tripadvisor – targets for fake reviews
    • Uber – serious safety concerns
    • Airbnb – balancing the interests of hosts and guests
  • Conclusions
  • Index

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Can telcos help cities combat congestion?

Introduction

Part of STL Partners’ (Re)connecting with Consumers stream, this report explores how telcos could support the companies seeking to reinvent how people get around the world’s increasingly congested cities. It looks at the serious problems arising from congestion and the need for a multi-modal approach to urban travel (incorporating ride hailing, public transport, bike and scooter sharing). The report then considers the many challenges facing the new players trying to bring about this multi-modal future, before making creative and constructive suggestions as to how telcos can help address these challenges. Finally, it also outlines how some operators, such as M1 in Singapore, China Mobile and China Telecom, are already playing an enabling role in the personal transportation market.

In particular, the report explores whether telcos can help coordinate the provision of transportation, as well as providing the underlying connectivity that will enable travellers to get information and make bookings on the fly, while allowing the transport providers to monitor their assets.  In many respects, the provision of effective public transportation is a systems integration challenge that requires a wealth of highly accurate real-time information about what is happening across a city.

As explained in the STL Partners report: The Coordination Age: A third age of telecoms, telecoms networks and related services can help people and companies use assets, such as bikes, cars and roads, much more effectively than they have in the past.

This report also builds on other STL research, notably:

The financial and human costs of congestion

After decades of urbanisation, many affluent cities in North America, Europe and East Asia are gridlocked with traffic. In much of the developing world, people continue to migrate to urban centres in search of work, clogging up roads from Bangkok to Bogota. Urbanisation is at its most extreme in East Asia (see Figure 1) where internal migration over the past decade has seen cities across China expanding at breakneck speed.

Figure 1: People have been flocking into cities worldwide for the past five decades

urbanisation rate

Source: The World Bank

The population density in some major economic hubs in the developing world, such as Mumbai, Manila and Lagos, is higher than 10,000 people per square kilometre (see Figure 2), compared with 1,510 people per square kilometre in London. As the UK capital suffers from serious traffic congestion, many cities in the developing world simply do not have enough space to allow the car to be the primary form of transport for their citizens.

In any case, private cars are not a sustainable mode of transport. As well as reducing people’s productivity and quality of life, traffic congestion is damaging air quality and harming human health. Air pollution has become the fourth highest risk factor for premature deaths – one in 10 deaths worldwide is attributable to air pollution exposure, according to the World Bank. Moreover, the bank says the economic burden of pollution is immense for the world and for individual countries. It estimates that ambient particulate matter (PM2.5) air pollution alone cost the global economy US$5.7 trillion, or 4.4% of global GDP, in 2016.

Figure 2: Many cities in the developing world are very crowded and cramped

the biggest cities in the world

Source: UN

So where is traffic congestion at its worst? Of the 38 countries covered by the INRIX 2017 Traffic Scorecard, Thailand is top of the list. In Thailand, drivers spend an average of 56 hours in rush hour congestion, ahead of Indonesia (51 hours) and Columbia (49 hours), followed by Venezuela (42), and the U.S. and Russia both with 41 hours (see Figure 3). Among developed nations, U.S. and Russia have the most congested cities in the world.

Intriguingly, sales of cars fell in 2018 for the first time in almost 28 years in rapidly urbanising China, a symptom of both the economic slowdown and the frustration of trying to drive in the country’s congested cities. Traffic jams, parking difficulties and overcrowding on buses and subways are the top three problems for urban commuters in China, according to a 2018 report by think tank Tencent Financial Technology.

Figure 3: The countries where the most time is lost to traffic congestion

time people spend in congestion

Source: NRIX 2017 Traffic Scorecard

INRIX’s data shows that Los Angeles tops the list of the world’s most gridlocked cities, with commuting drivers spending an average of 102 hours in congestion in 2017, followed by Moscow (91 hours), New York (91 hours), San Francisco (79 hours) and Bogota (75 hours).

Figure 4: Most of the most gridlocked cities are in the developed world

cities with highest congestion

Source: NRIX 2017 Traffic Scorecard

 

Contents
  • Executive summary
  • Introduction
  • Disrupting urban travel
    • Similarities with telecoms
  • Bringing about a multi-modal future
    • The Amazon of transportation?
    • Uber’s competitors
    • Takeaways – why one company won’t win
  • The rise of e-bikes and e-scooters
  • The challenges confronting micro-mobility
    • Lack of profitability
    • The maintenance and charging conundrum
    • The threats of vandalism and theft
    • Safety and public order
    • Buying rather than renting
  • How telcos are getting involved
  • Conclusions
Figures
  1. People have been flocking into cities worldwide for the past five decades
  2. Many cities in the developing world are very crowded and cramped
  3. The countries where the most time is lost to traffic congestion
  4. Most of the most gridlocked cities are in the developed world
  5. An overview of the pros and cons of different modes of urban transport
  6. Lime and Bird are clear leaders in the US e-bike and scooter sharing markets
  7. Both Lime and Bird have reported rapid growth in the number of rides
  8. Lime claims using its products is far cheaper than using a private car
  9. Challenges facing providers of shared bikes and scooters
  10. Some Northern European countries have embraced cycling in urban areas
  11. Sales of bikes (including electric-bikes) continue to rise

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 20th 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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Valuing Digital: A Contentious Yet Vital Business

Introduction

Tech VC in 2014: New heights, billion-dollar valuations

Venture capital investment across the mobile, digital and broader technology sectors is soaring. Although it stumbled during the 2008/9 financial crisis, the ecosystem has since recovered with 2013 and 2014 proving to be record-breaking years. Looking at Silicon Valley, for example, 2013 saw deals and funding more than double compared to 2009, and 2014 had already surpassed 2013 by the half-year mark:

Figure 1: Silicon Valley Tech Financing History, 2009-14

Source: CB Insights Venture Capital Database

As Figure 1 shows, growth in funding has outstripped growth in the number of deals: consequently, the average deal size has more than doubled since 2009. In part, this has been driven by a small number of large deals attracting very high valuations, with some of the highest valuations seen by Uber ($41bn), SpaceX ($12bn), Dropbox ($10bn), Snapchat ($10-20bn) and Airbnb ($13bn). Similarly high valuations have been seen in Silicon Valley tech exits, with Facebook’s $19bn acquisition of WhatsApp and Google’s $3.2bn acquisition of Nest two high-profile examples. These billion-dollar valuations are leading many to claim that a dotcom-esque bubble is forming: what can possibly justify such valuations?

In some cases, these concerns are driven by a lack of publicly available information on financial performance: for example, Uber’s leaked dashboard showed its financials to be considerably stronger than analysts’ expectations at the time. In other cases, they appear to be driven by a lack of understanding of the true rationale behind the deal. See, for example, the Connected Home: Telcos vs Google (Nest, Apple, Samsung, +…) and Facebook + WhatsApp + Voice: So What? Executive Briefings.

The Telco Dilemma: What is it all worth?

Against this uncertain backdrop, telecoms operators are expanding into such new mobile and digital services as a means to fill the ‘hunger gap’ left by falling revenues from core services. They are doing so through a mixture of organic and inorganic investment, in different verticals and with varying levels of ambition and success:

Figure 2: % of Revenue from ‘New’ Telco 2.0 Services*, 2013

Source: Telco 2.0 Transformation Index
* Disclaimer: Scope of what is included/excluded varies slightly by operator and depends upon the ability to source reliable data
Note: Vodafone data from 2012/13 financial year 

However, this is a comparatively new area for telcos and many are now asking what is the real ‘value’ of their individual digital initiatives. For example, to what extent are Telefonica’s digital activities leading to a material uplift in enterprise value?

This question is further complicated by the potential for a new service to generate ‘synergy value’ for the acquirer or parent company: just as Google’s $3.2bn+ valuation of Nest was in part driven by the synergy Nest’s sensor data provides to Google’s core advertising business, digital services have also been shown to provide synergy benefits to telcos’ core communications businesses. For example, MTN Mobile Money in Uganda is estimated to have seen up to 48% of its gross profit contribution generated by synergies, such as core churn reduction and airtime distribution savings, as opposed to standard transaction commissions.

Ultimately, without understanding the value of their digital businesses and how this changes over time (capital gain), telcos cannot effectively govern their digital activities. Prioritisation, budget allocation and knowing when to close initiatives (‘fast failure’) within digital is challenging without a clear idea of the return on investment different verticals and initiatives are generating. Understanding valuation was therefore identified as the joint most important success factor for delivering digital services in STL Partners’ recent survey of telco executives:

Figure 3: Importance of factors in successfully delivering digital services (out of 4)

Source: Digital Transformation and Ambition Survey Results, 2014, n=55

Crucially, however, survey respondents also identified developing this understanding as more than two years away from being resolved. In order to accelerate this process, there are three key questions which need to be addressed:

  1. What are the pitfalls to avoid when valuing digital businesses within telecoms operators?
  2. How should telcos model the spin-off value of their digital businesses?
  3. How should telcos think about the ‘synergy value’ generated by their digital businesses?

This Executive Briefing (Part 1) focuses on question 1; questions 2 and 3 will be addressed by future research (Part 2).

 

  • Executive Summary
  • Introduction
  • Tech VC in 2014: New heights, billion-dollar valuations
  • The Telco Dilemma: What is it all worth?
  • Challenges in Valuing Any Business (Analog or Digital)
  • DCF: Theoretically sound, but less reliable in practice
  • All models are wrong, but some are more useful than others
  • DCF’s shortcomings are magnified with digital businesses
  • Practical Issues: Lessons from Uber, Google, Skype and Spotify
  • A Conceptual Issue: Lessons from Facebook
  • Proxy Models: An improvement on DCF?
  • The Synergy Problem: A challenge for any valuation technique
  • Synergies are Real: Case studies from mobile money, cloud services and the connected home
  • Synergies are Problematic: Challenges for valuation in four areas
  • Conclusions
  • STL Partners and Telco 2.0: Change the Game

 

  • Figure 1: Silicon Valley Tech Financing History, 2009-14
  • Figure 2: % of Revenue from ‘New’ Telco 2.0 Services, 2013
  • Figure 3: Importance of factors in successfully delivering digital services (out of 4)
  • Figure 4: Sensitivity of DCF valuation to assumptions on free cash flow growth
  • Figure 5: Different buyer/seller valuations support a range of potential sales prices
  • Figure 6: Impact of addressable market and market share on Uber’s DCF valuation
  • Figure 7: Facebook vs. yield businesses, EV/revenue multiple, 2014
  • Figure 8: Facebook monthly active users vs. valuation, Q1 2010-Present
  • Figure 9: Three potential investor approaches to modelling Facebook’s value
  • Figure 10: MTN Mobile Money Uganda, Gross Profit Contribution, 2009-12
  • Figure 11: Monthly churn rates for MTN Mobile Money Uganda users (three months)
  • Figure 12: Conceptual and practical challenges caused by synergy value