A3 in customer experience: Possibilities for personalisation

The value of A3 in customer experience

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

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

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

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

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

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

A3 customer experience

Source: STL Partners, Charlotte Patrick Consult

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

Priorities in the contact centre

A3 Contact centre

Priorities in the digital channel

A3 Digital channel

Table of Contents

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

Related Research:

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DataSpark: Lessons on building a new telco (data) business

Data analytics as a new business

This case study looks at DataSpark, an autonomous business unit of Singtel (www.dsanalytics.com) and evaluates the benefits of creating a separate organisational structure within a telco to provide technology and support for the development of analytics, AI and automation as a new business. It is created after conversations with Shaowei Ying, Chief Operating Officer of DataSpark. The company’s activities include both the creation of internal capabilities and data monetisation capabilities for external customers.

DataSpark was formed in 2014 at a time when not many telcos were actively exploring new data business opportunities. The unit consisted of a small group of data professionals with skills around, particularly, location data. Singtel’s CEO was a strong supporter of leveraging telco data to establish competitive differentiation and therefore tasked them with looking at various location-related external monetisation opportunities. It was considered natural to create internal use cases for the data to defray the cost of the data preparation. In particular, the same mobility intelligence was of use to radio network planners optimising their network roll out using not just congestion, but now subscribers’ mobility patterns, too.

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DataSpark’s progress to date

Telcos’ external monetisation units, such as DataSpark, are not yet large enough to split out the revenues in their reports and accounts. However, in the 2018 and 2019 Management Discussion and Analysis DataSpark’s progress was reported to include:

  • Activity to bring mobility data to sectors such as transport and out-of-home media in Singapore and Australia
  • Partnership in out-of-home advertising with large players taking a data-as-a-service solution to optimise their assets
  • Provision of insights including first party enterprise data in the consumer goods sector to deliver new use cases in advertising and retail store inventory optimisation
  • Recent support for governments in predicting spread of Covid-19, including understanding the socio-economic impact of the virus.

Service example: COVID-19 insight for the Australian local government

COVID-19 data analytics innovation

Source: DataSpark

Table of Contents

  • Executive Summary
    • Two diverging strategies for a small, independent data unit
    • Scaling up the data business as an integrated unit
  • Introduction
    • DataSpark’s progress to date
  • DataSpark’s approach to building a data unit
    • What services does it offer?
    • Go-to-market: Different approaches for internal and external customers
    • Organisational structure: Where should a data unit go?
  • How to scale a data business?
    • The immediate growth opportunities
    • Following in others’ footsteps
    • Building new capabilities for external monetisation
  • Assessing future strategies for DataSpark
    • Scenario 1: Double down on internal data applications
    • Scenario 2: Continue building an independent business

 

Read more about STL Partners’ AI & automation research at stlpartners.com/ai-analytics-research/

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AI is starting to pay: Time to scale adoption

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AI adoption yields positive results

Over the last five years, telcos have made measurable progress in AI adoption and it is starting to pay off.  When compared to all industries, telcos have become adept at handling large data sets and implementing automation. Over the last several years the telecoms industry has gone from not knowing where or how to implement AI, to having developed and implemented hundreds of AI and automation applications for network operations, fraud prevention, customer channel management, and sales and marketing. We have discussed these use cases and operator strategies and opportunities in detail in previous reports.

For the more advanced telcos, the challenge is no longer setting up data management platforms and systems and identifying promising use cases for AI and automation, but overcoming the organisational and cultural barriers to becoming truly data-centric in mindset, processes and operations. A significant part of this challenge includes disseminating AI adoption and expertise of these technologies and associated skills to the wider organisation, beyond a centralised AI team.The benchmark for success here is not other telcos, or companies in other industries with large legacy and physical assets, but digital- and cloud-native companies that have been established with a data-centric mindset and practices from the start. This includes global technology companies like Microsoft, Google and Amazon, who increasingly see telecoms operators as customers, or perhaps even competitors one day, as well as greenfield players such as Rakuten, Jio and DISH, which as well as more modern networks have fewer ingrained legacy processes and cultural practices to overcome.

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Telecoms has a high AI adoption rate compared with other industries

AI pays off

Source: McKinsey

In this report, we assess several telcos’ approach to AI and the results they have achieved so far, and draw some lessons on what kind of strategy and ambition leads to better results. In the second section of the report, we explore in more detail the concrete steps telcos can take to help accelerate and scale the use of AI and automation across the organisation, in the hopes of becoming more data-driven businesses.

While not all telcos have an ambition to drive new revenue growth through development of their own IP in AI, to form the basis of new enterprise or consumer services, all operators will need AI to permeate their internal processes to compete effectively in the long term. Therefore, whatever the level ambition, disseminating fundamental AI and data skills across the organisation is crucial to long term success. STL Partners believes that the sooner telcos can master these skills, the higher their chances of successfully applying them to drive innovation both in core connectivity and new services higher up the value chain.

Contents

  • Executive Summary
  • Introduction
  • Developing an AI strategy: What is it for?
    • Telefónica: From AURA and LUCA to Telefónica Tech
    • Vodafone: An efficiency focused strategy
    • Elisa: A vertical application approach
    • Takeaways: Comparing three approaches
  • AI maturity progression
    • Adopt big data analytics: The basic building blocks
    • Creating a centralised AI unit
    • Creating a new business unit
    • Disseminating AI across the organisation
  • Using partnerships to accelerate and scale AI
    • O2 and Cardinality
    • AT&T Acumos
  • Conclusion and recommendations
  • Index

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Growing B2B2X: Taking telcos beyond connectivity and 5G

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The telecoms industry is looking to revive growth

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

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

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

Source: STL Partners

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

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

New purpose, new role

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

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

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

Figure 2: Potential telco roles beyond traditional connectivity

Source: STL Partners

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Clearer on the vision, unclear on the execution

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

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

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

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

Table of Contents

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

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

Why does edge assurance matter?

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

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

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

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

Source: STL Partners, Charlotte Patrick Consult

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

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

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

Forecast of Tier 1 operator edge servers by domain

Forecast of Tier-1 operator edge servers by domain

Source: STL Partners

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

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

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

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Table of contents

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

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

Getting to grips with A3

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

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

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

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

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

Introducing the telco A3 application map

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

Summarising the six types of problems A3 can help with:

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

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

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

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How telcos should use the A3 map

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

Table of contents

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

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Telco AI: How to organise and partner for maximum success

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

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

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

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

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

What is artificial intelligence?

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

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

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

Figure 1: Moving toward AI

The progression of AI maturity in four steps

Source: STL Partners

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

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

Cutting through the hype

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

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

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

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

Telcos’ current AI focus: Improving speed and efficiency

Key learnings on telco AI initiatives

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

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

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

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

Contents of the full report include:

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

Figures:

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


[1] Source: Telefónica

[2] Source: Cloudera

[3] Source: Vodafone

[4] Source: DataSpark

[5] Source: AT&T

Telco apps: What works?

Introduction

Part of STL Partners’ (Re)connecting with Consumers stream, this report analyses a selection of successful mobile apps run by telcos or their subsidiaries. It explains why mobile apps will continue to play a major in the digital economy for the foreseeable future before considering the factors that have made particular telco apps successful. Most of the apps considered in the report are from Asia, primarily because operators in that world have typically been more aggressive in pursuing the digital services market than their counterparts elsewhere. Note, the list of apps analysed in this report is far from exhaustive – there are other successful telco-run apps on the market.

The ultimate goal of this report is to explain how apps can engage customers and give telcos greater traction with consumers. Although many apps are rarely used and quickly discarded, the most popular apps, such as Instagram, Spotify and YouTube, have become an integral part of the daily lives of hundreds of millions of people. Some apps, such as Uber and Google Maps, regularly provide people with services and/or information that make their lives much easier – getting a taxi or navigating through an unfamiliar city is now much easier than it used to be. Indeed, a well-designed app dedicated to a specific service can deliver both relevance and revenues.

This report builds on previous STL research, notably:

Can Netflix and Spotify make the leap to the top tier?

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

AI on the Smartphone: What telcos should do 

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Why apps matter for telcos

Telcos’ most successful digital services, notably SMS, pre-date the smartphone app era.  Even more recent triumphs, such as the M-Pesa, the ground breaking mobile money service in Kenya, were originally designed to work on feature phones.  Many similar services, such as MTN Money and Orange Money, aimed at the large numbers of people without bank accounts in Africa and developing Asia, continue to be accessed largely through text-based menus via SIM toolkit.

But the widespread adoption of smartphones in developed and developing markets alike mean that telcos everywhere need to ensure all the consumer services they offer can be accessed via well-designed and intuitive apps with graphical user interfaces. By the end of 2017, there were 4.3 billion smartphones in use worldwide, according to Ericsson’s estimates. Moreover, smartphone adoption continues to rise rapidly, particularly in Africa, India and other developing countries. Ericsson reckons the number of smartphone subscriptions will reach 7.2 billion in 2023 (see Figure 3).

Figure 3: The number of smartphones in use is rising steadily across the world

Global App take up

Source: Ericsson Mobility Report, June 2018

Subscriptions associated with smartphones now account for around 60% of all mobile phone subscriptions, according to Ericsson, which says that 85% of all mobile phones sold in the first quarter of 2018 were smartphones.

With smartphones the default handset for people in developed markets and many developing markets, apps have become a major medium for interactions between consumers and service providers across the economy. Now approximately ten years old, the so-called app economy is worth tens of billions of dollars per annum.

Although there has been a backlash, as people’s smartphones get clogged up with apps, the sector still has considerable momentum.

The most popular apps, such as Uber and Amazon Shopping, combine ease of access (straightforward authentication), with ease-of-use and ease-of-payment, enabling them to attract tens of millions of users.

With some justification, proponents contend that apps will continue to be one of the main drivers of the digital economy for the foreseeable future. The broader app economy will be worth $6.3 trillion by 2021, up from $1.3 trillion in 2016, according to App Annie. Note, those figures include in-app ads and mobile commerce, as well as the revenues generated through app stores. In other words, this is the total value of the business conducted via apps, rather than the revenue accrued by app stores and developers. This dramatic forecast assumes the ongoing shift of physical transactions to the mobile medium continues apace: App Annie expects the value of mobile commerce transactions to rise from $344 per user in 2016 to $946 by 2021.

Although most of the leading apps are free, many do generate a subscription fee or one-off sales. Annual consumer spending in app stores is set to rise 18% between 2016 and 2021 to reach $139 billion worldwide, according to specialist app analytics firm App Annie, which also forecasts the total time spent in apps will grow to 3.5 trillion hours in 2021, up from 1.6 trillion in 2016.

In reality, some of these aggressive forecasts may prove to be too bullish, as consumers begin to make greater use of messaging services and voice-activated speakers to interact with local merchants and purchase digital content and services.  Even so, it is clear that the leading mobile apps will continue to be a major consumer engagement tool for many brands and merchants well into the next decade. In some cases, such as Spotify or the fitness app Strava, the user has typically put significant effort into creating a personalised experience, helping to cement their loyalty.

In developed countries, some telcos, notably AT&T and Verizon, have belatedly and expensively acquired a major presence in the app economy by buying leading digital content producers and service providers. With the $85.4 billion acquisition of Time Warner, AT&T is now the owner of HBO Now, which was the third highest app by consumer spend in the US in 2017, according to App Annie. HBO Now also ranked fifth in Mexico and eighth in the world on this measure. Having acquired Yahoo! and AOL apps over the past few years, Verizon ranked eighth among companies in terms of downloads in the US in 2017.

The delicate transition from SIM toolkit to app

But expensive acquisitions are not the only way into the app economy. For telcos that have developed consumer services from the ground-up, the rise of the smartphone offers opportunities to provide much richer functionality and a more intuitive interface, as well cross-selling and up-selling. In Kenya, Safaricom has been expanding the mobile money transfer service M-Pesa into a much broader financial services proposition, while prodding users to switch from the SIM toolkit to the app, which can properly highlight M-Pesa’s wider proposition. At the same time, the telco has integrated M-Pesa into its customer service app, mySafaricom, helping it to promote its broader telecoms offering to frequent users of its mobile money services.

However, Safaricom is well aware that it needs to tread cautiously, continuing to cater for those customers who are comfortable with the SIM toolkit experience. Its softly-softly approach is to reassure Kenyans that they can always fall back on the SIM toolkit, if they don’t like the app.  In a Safaricom-sponsored article from August 2017, Emmanuel Chenze wrote the following on the online site, Android Kenya:

“For over a year now, Safaricom has had the mySafaricom application available on the Google Play Store for users to be able to better manage the services they receive from the telecommunications company. However, it wasn’t until March this year when the application was updated to include M-PESA.

“With M-PESA finally integrated, the over 1 million smartphone users can now take full advantage and transact even faster thanks to the app. While good ol’ SIM toolkit still works wonders and remains a good backup option when you’re not connected to the internet or when the mySafaricom app is acting up, using the application, which has since been updated to reflect Safaricom’s recent rebranding, is way better than using the otherwise cumbersome SIM toolkit.”

If they can make their apps straightforward and easily accessible, Africa’s telcos could still become major players in the app economy – as Figure 4 indicates, the number of smartphones in use in sub-Saharan Africa could double between now and 2023. That gives telcos a major opportunity to promote their apps to first-time smartphone users as they buy their new handsets. Pan-Africa operator MTN is pursuing this strategy with its MTN Game+ , Music+ and video apps (see Figure 4).

Figure 4: MTN is pushing its entertainment apps to new smartphone users

Safaricom app chart

Source: MTN interim results presentation for the six months ended June 2018

In Asia, some telcos have successfully developed widely used apps from scratch, notably in the customer care space, as explained in the next section (continued in full report).

Table of Contents

  • Executive Summary
  • Introduction
  • Why apps matter
  • The delicate transition from SIM toolkit to app
  • Telcos can build on customer care
  • My AIS – a top ten app in Thailand
  • Takeaways
  • Information apps have traction
  • Call management apps prove popular in South Korea
  • T Map in top ten apps in South Korea
  • Takeaways
  • Telcos’ entertainment apps go regional
  • PCCW’s Viu plays in sixteen markets
  • Liberty Global
  • Takeaways
  • Turkcell: Using apps to up engagement
  • Competitive in communications
  • Takeaways

Table of Figures

  • Figure 1: Alternative routes for telcos to build out their app proposition
  • Figure 2: Overview of the telco-owned apps covered in this report
  • Figure 3: The number of smartphones in use is rising steadily across the world
  • Figure 4: MTN is pushing its entertainment apps to new smartphone users
  • Figure 5: My AIS supports payments and loyalty points, as well as usage monitoring
  • Figure 6: The True iService app has a clear and straightforward graphic interface
  • Figure 7: True Digital’s app portfolio covers everything from coffee to communications
  • Figure 8: WhoWho helps user manage incoming calls on phones and wearables
  • Figure 9: SK Telecom’s T map app for public transport covers trains, buses and taxis
  • Figure 10: KKBOX Claims Strong Customer Base Among iPhone Users
  • Figure 11: Turkcell’s broad portfolio of apps covers content and communications
  • Figure 12: Turkcell’s BiP Messenger is designed to be fun
  • Figure 13: Turkcell is focused on how much time customers spend in its apps
  • Figure 14: Turkcell’s foreign subsidiaries are much smaller than its domestic operation

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How Zain Bahrain simplified and digitised customer engagement

Introduction

Increasing pressure on the telecoms business model…

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

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

Source: Telegeography, STL Partners

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

 

Source: Telegeography, STL Partners

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

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

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

Figure 3: NPS by industry, 2018

Source: CustomerGauge

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

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

Source: STL Partners

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

Contents:

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

Figures:

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

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Network AI: The state of the art

Introduction

This report is part of a series exploring how telecoms operators can leverage artificial intelligence (AI) to improve their business operations, from customer experience to new services. Previous reports on AI in telecoms include:

This report explores the applications of AI for network operations, detailing the prerequisites and stages to implementing AI and automation in networks, real-world examples of what some telcos have done already, and their potential value across different application areas.

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We divide the applications for AI in telecoms networks into three main categories:

  • Fault detection, prediction and resolution: speeding up the process of identifying and resolving network faults, including predictive maintenance. This also includes identifying and mitigating network security risks, although security is a highly specialised field that merits its own report, so we do not cover it in detail here.
  • Network optimisation: optimising the use of network resources to mitigate the impact of network faults and adapt to or anticipate changes in demand. This is also the foundation for automated service provisioning in software defined networks, while insights on network usage and traffic could be valuable for new service creation.
  • Network planning and upgrades: optimising new infrastructure planning as well as the transition from legacy to next generation network solutions.

The first area is critical for all telcos, since service impairments are an inevitable element of running a network. The second is of immediate value for telcos that are still in the process of expanding existing network coverage and density, since it can enable operators to use their existing resources more efficiently. However, it is also increasingly tied into the first area of fault detection, since a large part of the fault resolution process is finding ways to re-route traffic from underperforming to underused assets, a process that is made easier with the adoption of SDN and NFV – processes can only be automated if they are software-based.

Compared with the first two categories, using AI for smarter network planning and upgrades is a nascent field. This is partially because many Tier 1 operators, who are leading the charge in adoption of AI elsewhere in network and business operations, completed the bulk of 4G deployments and have not yet fully embarked on 5G deployments. However, this report also looks at some innovative applications of image recognition models for network expansion in emerging markets.

While most of the data used for training and informing AI systems across network operations comes from operators’ own networks, telcos are also beginning to tap into new data sources to further refine their decision-making, such as using drones and image recognition to inspect towers, weather patterns and social media data.

Laying the foundations for AI in telecoms networks

Before jumping into how telcos are implementing AI for fault detection and resolution and in network operations, it is important to clarify what we mean by AI, and lay out the pre-requisites for any meaningful use of the technology.

What counts as AI? From automation to advanced AI

The term AI is nebulous – everyone has a different definition for it. Is it when a computer can make a faster, more accurate decision than a human?  Is it when a process is fully automated? Is it when the computer learns and continuously improves its decisions in real-time?

Wherever people draw the line between manual processes, (big) data analytics, automation and machine learning (ML) / AI, no company goes directly from manual to AI in one go. The transition is gradual. In this report we therefore use a broad definition of AI in this report, as outlined in Figure 1.

Figure 1: Not all AI is equal

Rules-based automation to machine learning

Source: STL Partners

Two transitions are happening in parallel as operators move from left to right on Figure 1. First, there is a shift towards increasingly intelligent analytics techniques, from rules-based automation, where policies outline if-then sequences of actions for the computer, to machine learning supported automation, where models are trained to fulfil an intent (a goal) based on guidelines from experts and historical data.

The second transition that occurs in the move towards more sophisticated AI systems relates to decision-making. In rules-based automation, computers don’t have any decision-making power, they can only take pre-defined actions in specific circumstances. Making the transition from telling computers how to do something to what you want them to do means giving computers decision-making power. Telcos can do this gradually, by requiring humans to verify and approve recommended decisions before they are implemented. But in the promised future 5G and ‘sliceable’ networks, human approval for routine decisions would require more network engineers than operators could profitably employ, or drastically slow down network operations. This is not just a technical issue for telcos but also a cultural one that demands clarity from management teams on the evolving role of network engineers.

Contents:

  • Executive Summary
  • Making the shift from manual operations to autonomous, intelligent networks
  • Recommendations
  • Introduction
  • Laying the foundations for AI in telecoms networks
  • What counts as AI? From automation to advanced AI
  • AI works at two levels for network operations
  • Data: The bridge between rules-based automation and ML
  • Fault detection, prediction and resolution
  • What is it worth?
  • How does it work?
  • Real-world example of a recommendation model: AT&T Tower Outage and Network Analyzer
  • Next step: From fixed to self-learning policies
  • Optimising network capacity
  • What are self-optimising networks worth?
  • Use case overview
  • How to do it
  • From self-optimising to knowledge-defined networks
  • AI for network planning
  • Telefónica case study
  • Driving automation internally versus partnering with vendors
  • Reasons for developing solutions internally
  • Reasons for partnering with a vendor
  • Vendor profiles
  • How AI fits with SDN/NFV
  • Conclusions and recommendations

Figures:

  • Figure 1: Not all AI is equal
  • Figure 2: Rules-based automation versus machine learning
  • Figure 3: A snapshot of rules-based automation versus machine learning
  • Figure 4: Overview of automation and AI in network operations
  • Figure 5: Telemetry is faster and uses less compute power than SNMP
  • Figure 6: Elisa growth of automated trouble ticket handling
  • Figure 7: Tupl results for automatic customer complaints resolution AI platform
  • Figure 8: Implementing fixed policies for fault detection and resolution
  • Figure 9: Visualisation of network alert clustering tool
  • Figure 10: A self-healing network
  • Figure 11: Elisa self-optimising network results
  • Figure 12: Elisa maintained flat capex intensity throughout 4G deployment
  • Figure 13: Finland 4G network performance, August 2018
  • Figure 14: Self-organising network example use cases
  • Figure 15: Numerous applications of machine learning and AI for 5G networks
  • Figure 16: Break self-optimising networks down into mini loops
  • Figure 17: The knowledge-defined network
  • Figure 18: Facebook TCO savings over traditional multilayer planning
  • Figure 19: Telefónica image recognition for network planning
  • Figure 20: Ciena Blue Planet overview
  • Figure 21: Google SDN layers
  • Figure 22: Overview of cross-industry initiatives relating to network AI and automation
  • Figure 23: Telefónica network automation roadmap
  • Figure 24: Overview of SK Telecom Advanced Next Generation OSS (TANGO)

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Telco economics: The price of loyalty

The Cost of Churn for Mobile Operators

Customer churn continues to present a significant and costly challenge to the mobile industry. Churn rates for MNOs can range from less than 0.75% per month (c. 9% pa) to over 5% per month (80% pa). Postpay rates of churn are usually lower and typically lie between 0.75% and 3% per month (c. 9–43% pa), whereas prepay churn typically lies between 3% and 5% (30–80%pa), although it can be as low as 1% in some circumstances, for example when number portability is not permitted.

The costs of churn are felt in several ways. The major costs come from lost revenues from customers churning away and the costs of acquiring new customers to replace them. During periods of high growth operators can also lose significant market share, and hence revenues and profit, if much of their expenditure on acquiring new customers is devoted to replacing customers that have churned away, rather than on growing their subscriber base.

Analysis of data published by operators shows that average costs of acquisition (CoA) are about four times average monthly ARPU, and it will therefore typically take over four months’ revenue to repay the SAC incurred. The figure is slightly higher on average for postpay customers at about 4½, whereas prepay CoA is on average between 1½ and 1¾ times ARPU.

We estimate that the industry average EBITDA is around 25%, so for an individual postpay subscriber it will take on average 17 months to repay the investment from EBITDA. With typical contract lengths of 24 months, this does not leave much time to generate a positive margin.

These costs mean that it is important for operators to find ways of minimising churn and of maintaining it at a low level.

Some level of churn is inevitable, since customers may move to a new region or country, die, or perhaps acquire a new phone and subscription from their employers as part of their job. Other forms of churn are largely voluntary, and operators have to focus their efforts on these if they are to contain their costs of doing business.

In doing so, operators can find it worthwhile to take into account the different characteristics of their customers. Some people show a much greater propensity to churn and are always seeking improved tariffs or a better deal, but many others remain loyal. If the latter churn, they are more likely to do so for other reasons, such as poor quality of service.

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Mobile Operators Strategies for Reducing Churn

In an attempt to find an effective means of reducing churn, mobile operators have adopted a variety of churn reduction strategies. These include:

  • Offering financial or other incentives to customers who are about to churn, such as discounted handsets or tariff bundles.
  • Monitoring usage and using data analytics to predict which customers are likely to churn in the near future and offering them incentives and improved service bundles.
  • Using more flexible contracts to allow early upgrades or other changes.
  • Giving bonuses (e.g. extra minutes or increased data allowances) or other rewards for loyalty.
  • Offering multiple services, such as quad play, to increase the stickiness of their service.
  • Offering additional and popular services, such as Spotify or Netflix, at attractive rates or bundled with basic services, or discounted entry to events.
  • Improving overall customer experience and service quality to reduce the triggers for churn. This can include significant organisational and cultural changes and efficiency improvements including increased automation. Changes include transfer of customer support functions to marketing, and the introduction of chatbots and apps to speed up and improve handling of routine customer enquiries.

Causes of Customer Churn

This report reviews the causes of churn and the characteristics of customers that are most likely to churn. It draws on examples from operators’ experiences to illustrate different strategies used by operators to reduce churn and to establish which approaches have proved most successful in delivering reductions in the level of churn or in maintaining low levels that have already been achieved. It also looks at the costs associated with churn and their impact on revenues and profitability. Operators discussed include TELUS, O2 and Telstra, which provide examples of MNOs that have achieved low levels of churn, and Globe, which provides useful insight into the different customer behaviours found in a predominately prepay and multi-SIM market and an example of the relationship between churn and SAC.

Contents:

  • Executive Summary
  • Actions of successful operators
  • Financial implications of churn
  • Benchmarks
  • Introduction
  • Causes and costs of churn and remedies
  • Customer behaviours
  • Costs of churn
  • Common approaches to reducing churn
  • Case studies and results
  • TELUS: churn fell over five years
  • O2 outsourcing: changing approach to customer experience
  • Telstra: analytics and customer experience
  • Globe Telecom: costs of churn
  • Cricket: reducing churn in low-cost prepay
  • Adjacent and complementary services
  • Conclusions
  • Customer behaviours
  • Costs of churn
  • Actions of successful operators
  • Benchmarks
  • Resulting organisational and financial issues faced by operators
  • Recommendations

Figures:

  • Figure 1: Share of TELUS revenues taken by SAC and SRC
  • Figure 2: Costs of churn when CoA = 50% annual ARPU
  • Figure 3: Examples of reasons/triggers for customer churn
  • Figure 4: Mobile customer characteristics
  • Figure 5: Customer average lifetime versus lifetime value
  • Figure 6: Relative proportions of customer types in mature markets
  • Figure 7: Costs of churn when CoA = 50% annual ARPU
  • Figure 8: Costs of churn when CoA = 80% annual ARPU
  • Figure 9: Costs of churn when CoA = 10% annual ARPU
  • Figure 10: TELUS monthly churn
  • Figure 11: TELUS EBITDA
  • Figure 12: TELUS monthly ARPU 2007–2016
  • Figure 13: TELUS SAC and SRC % of revenues
  • Figure 14: TELUS costs of acquisition and ARPU
  • Figure 15: TELUS SAC, SRC and EBITDA
  • Figure 16: UK MNOs blended churn
  • Figure 17: O2 customer satisfaction
  • Figure 18: Telstra annual postpay churn 2012–2017
  • Figure 19: Telstra revenues by service
  • Figure 20: Telstra ARPU
  • Figure 21: Telstra EBITDA
  • Figure 22: Globe prepay customers 2011–2017
  • Figure 23: Globe postpay customers 2012–2017
  • Figure 24: Globe revenues 2012–2017
  • Figure 25: Globe and TM prepay and postpay monthly churn
  • Figure 26: Inverse relationship between Globe’s postpay SAC and churn
  • Figure 27: Examples of costs of churn and CoA

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Telcos and GAFA: Dancing with the disruptors

Introduction

Across much of the world, the competing Internet ecosystems led by Amazon, Apple, Facebook and Google have come to dominate the consumer market for digital services. Even though most telcos continue to compete with these players in the service layer, it is now almost a necessity for operators to partner with one or more of these ecosystems in some shape or form.

This report begins by pinpointing the areas where telcos are most likely to partner with these players, drawing on examples as appropriate. In each case, it considers the nature of the partnership and the resulting value to the telco and to the Internet ecosystem. It also considers the longer-term, strategic implications of these partnerships and makes recommendations on how telcos can try to strengthen their negotiating position.

This research builds on the findings of the Digital Partnerships Benchmarking Study conducted between 26th September and 4th November 2016 by STL Partners and sponsored by AsiaInfo. That study involved a survey of 34 operators in Europe and Asia Pacific. It revealed that whereas almost all operators expected to grow their partnerships business in the future, they differed on how they expected to pursue this growth.

Approximately half (46%) of the operator respondents wanted to scale up and partner with a large number of digital players, while the other half (49%) wanted to focus in on a few strategic partnerships.  Those looking to partner with a large number of companies were primarily interested in generating new revenue streams or increasing customer relevance, while many of those who wanted to focus on a small number of partnerships also regarded increasing revenues from the core business as a main objective (see Figure 1).

Figure 1: The business objectives differ somewhat by partnership strategy

Source: Digital Partnerships Benchmarking Study conducted in late 2016 by STL Partners and sponsored by AsiaInfo

Respondents were also asked to rank the assets that an operator can bring to a partnership, both today and in the future. These ranks were converted into a normalized score (see Figure 2): A score of 100% in Figure 2 would indicate that all respondents placed that option in the top rank.

Figure 2: Operators regard their customer base as their biggest asset

Source: Digital Partnerships Benchmarking Study conducted in late 2016 by STL Partners and sponsored by AsiaInfo

Clearly, operators are aware that the size of their customer base is a significant asset, and they are optimistic that it is likely to remain so: it is overall the highest scoring asset both today and in the future.

In the future, the options around customer data (customer profiling, analytics and insights) are given higher scores (they move up the ranks). This suggests that operators believe that they will become better at exploiting their data-centric assets and – most significantly – that they will be able to monetize this in partnerships, and that these data-centric assets will have significant value.

The findings of the study confirm that most telcos believe they can bring significant and valuable assets to partnerships. This report considers how those assets can be used to strike mutually beneficial deals with the major Internet ecosystems. The next chapter explains why telcos and the leading Internet players need to co-operate with each other, despite their competition for consumers’ attention.

Contents:

  • Executive Summary
  • Strategic considerations
  • Delivering bigger, better entertainment
  • Improving customer experience
  • Extending and enhancing connectivity
  • Developing the networks of the future
  • Delivering cloud computing to enterprises
  • Introduction
  • Telcos and lnternet giants need each other
  • Delivering bigger, better entertainment
  • Content delivery networks
  • Bundling content and connectivity
  • Zero-rating content
  • Carrier billing
  • Content promotion
  • Apple and EE in harmony
  • Value exchange and takeaways
  • Improving the customer experience
  • Making mobile data stretch further
  • Off-peak downloads, offline viewing
  • Data plan awareness for apps
  • Fine-grained control for consumers
  • Value exchange and takeaways
  • Extending and enhancing connectivity
  • Subsea cable consortiums
  • Free public Wi-Fi services
  • MVNO Project Fi – branded by Google, enabled by telcos
  • Value exchange and takeaways
  • Developing the networks of the future
  • Software-defined networks: Google and the CORD project
  • Opening up network hardware: Facebook’s Telecom Infra Project
  • Value exchange and takeaways
  • Delivering cloud computing to enterprises
  • Reselling cloud-based apps
  • Secure cloud computing – AWS and AT&T join forces
  • Value exchange and takeaways
  • Conclusions and Recommendations
  • Google is top of mind
  • Whose brand benefits?

Figures:

  • Figure 1: The business objectives differ somewhat by partnership strategy
  • Figure 2: Operators regard their customer base as their biggest asset
  • Figure 3: US Internet giants generate about 40% of mobile traffic in Asia-Pacific
  • Figure 4: Google and Facebook are now major players in mobile in Africa
  • Figure 5: Examples of telco-Internet platform partnerships in entertainment
  • Figure 6: BT Sport uses YouTube to promote its premium content
  • Figure 7: Apple Music appears to have helped EE’s performance
  • Figure 8: Amazon is challenging Apple and Spotify in the global music market
  • Figure 9: Examples of telco-Google co-operation around transparency
  • Figure 10: YouTube Smart Offline could alleviate peak pressure on networks
  • Figure 11: Google’s Triangle app gives consumers fine-grained control over apps
  • Figure 12: Examples of telco-Internet platform partnerships to deliver connectivity
  • Figure 13: Project Fi’s operator partners provide extensive 4G coverage
  • Figure 14: Both T-Mobile US and Sprint need to improve their financial returns
  • Figure 15: Examples of telco-Internet platform partnerships on network innovation
  • Figure 16: AWS has a big lead in the cloud computing market
  • Figure 17: Examples of telco-Internet platform partnerships in enterprise cloud
  • Figure 18: AT&T provides private and secure connectivity to public clouds
  • Figure 19: Amazon and Alphabet lead corporate America in R&D
  • Figure 20: Telcos need to be wary of bolstering already powerful brands
  • Figure 21: Balancing immediate value of partnerships against strategic implications
  • Figure 22: Different telcos should adopt different strategies

Telco digital customer engagement: What makes a winning strategy?

Introduction

Customer experience is at the centre of telcos’ digital transformation efforts

Telecoms is one of many industries that are transitioning towards becoming more digitalised businesses. More specifically within digital transformation, the need to be customer-centric, and improve customer engagement, has been a crucial theme in telco digital transformation efforts. This is exemplified by Orange’s CEO Stèphane Richard who recently claimed that users needed to be “at the core of systems”.

As revenue growth in the industry continues to decline and telecom operators’ core services become commoditised, customer experience remains as one of the few areas operators can differentiate themselves from their competitors and maintain relevance with consumers. This places greater need for operators to make customer engagement a priority.

The way in which telcos engage customers has changed dramatically in recent years through the growth of different channels and touch-points a customer has access to. This is often contributed to the rapid adoption of smartphones and tablets, initiated by the launch of the iPhone in 2007, and the speedy adoption of social media platforms like Facebook (launched 2004) and Twitter (launched in 2006). Customers now expect businesses to be digitally savvy, knowledgeable and “joined-up” in their interactions with them.

There is no shortage of commentators and technology providers extolling the virtues of a more customercentric focus, urging operators adopt an omnichannel approach. By integrating online, call centre and bricks-and-mortar store customer experiences – through omnichannel capabilities – the promise to operators is that they can deliver joined-up customer experiences: simultaneously improving the effectiveness of telecoms marketing by building a ‘single-view’ of the customer, reducing time spent on resolving customer service issues, and preventing data from getting stuck in specific siloes.

But are these investments in technology (and the considerable internal resource implications) really a priority for operators or just another example of technology vendors pushing operators to spend more on expensive capabilities that they will never benefit from? Our survey suggests that those operators who have built omnichannel capabilities are reaping the rewards. However, operators also appreciate that success is not just down to implementing fancy systems: it’s also about what you do with them and having the right skills.

Telcos’ benchmarks come from within and outside the industry

Although most telcos are investing in their efforts to digitise the customer experience, it may not be obvious where they should be concentrating their efforts and what targets they should be aiming for. For this, there is a need to determine what the relevant benchmarks are when it comes to best-practice for digital engagement, how well they stack up and how they should seek to close the gap.

Telcos are looking to learn from outside their industry as customer engagement is a domain that all businesses constantly seek to improve. Digital natives, companies such as Google, Facebook and Netflix that started off as digital businesses and did not have to make a transition from legacy practices, are often leading the way when it comes to offering customers a truly digitized experience. However, for a telco, it may seem like an unrealistic dream to replicate their efforts, therefore telcos often look for best-practice examples from other industries, which are undergoing a digital transformation and still have the burden of legacy services, systems, processes, people and infrastructure. These industries include finance, retail and media.

Nonetheless, when comparing telcos’ digital customer engagement to these industries, many different measures suggest that telcos are lagging behind. When looking at cross-industry Net Promoter Scores (NPS), telecoms operators come out at an average of 11% compared to an average of 50% for retail (which leads all industries). The next worst industry, insurance, has an average score of 23%, just over twice that of telecoms.

These statistics suggest there is room for improvement, but in which specific areas do the most critical gaps exist and how should telcos go about changing this?

So, STL Partners has attempted to answer two questions:

  1. What should telcos be aiming for?
  2. How well are telcos measuring up to their ambitions in digital customer engagement?

To address this, we created an online tool to benchmark telcos across various metrics in three domains related to digital customer engagement: commerce, marketing and sales & service.

The Digital Customer Engagement Benchmarking Study5 took place in two phases. The first phase was focused on commerce and took place over July and August 2016. In the second phase, the scope was expanded to include marketing and sales & service and took place in April and May 2017. In total, 70 respondents from 47 telecoms operators took part in the study.

For the purposes of this study, operators are categorised into 2 ‘peer groups’:

  • Mature Market: Medium-high income per user, predominantly post-pay, developed fixed infrastructure
  • Mobile First: Low-Medium income per user, predominantly pre-pay with limited fixed infrastructure

Figure 1: Respondents by region and peer group

chart on global customer experience survey

Source: STL Partners

Contents:

  • Preface
  • Executive Summary
  • Introduction
  • Characterising operators’ digital customer engagement strategies
  • Commerce: selling more digitally and selling digitally more
  • Telcos’ online channels are still not being used enough by customers and prospects
  • Revenue benefits from online channels are relatively lower
  • Leveraging digital channels to upsell customers is one way to help drive online revenue
  • Data use is the key differentiator for a successful digital commerce approach
  • What is best practice for commerce?
  • Commerce Case Studies
  • Marketing: this time it’s personal
  • A (good) personalised marketing approach is more likely to secure returns…
  • …but most telcos’ marketing still uses traditional customer segmentation
  • What is best practice for marketing?
  • Marketing Case Studies
  • Sales & Service: Delivering the promise
  • Customers of the Omnichannel operator group are most actively engaged on digital channels
  • Online service engagement requires adequate channels and functionality
  • Omnichannel operators add value to customer service by ensuring complete visibility of customers
  • What is best practice for sales & service?
  • Sales & Service Case Study
  • Conclusions

Figures:

  • Figure 1: Respondents by region and peer group
  • Figure 2: Mapping operator digital customer engagement strategies
  • Figure 3: On average, less than 20% of total sales are from online channels
  • Figure 4: Variation between average telco and best performer across online sales
  • Figure 5: ARPU tends to be higher for customers who purchase their core package on offline channels
  • Figure 6: Mature Market operators have higher online attachment rates than Mobile First
  • Figure 7: Most operators are offering at least one online channel for upgrades
  • Figure 8: Omnichannel operators out-perform in digital commerce
  • Figure 9: Our research shows a link between the levels of personalised marketing and online marketing conversion rate
  • Figure 10: Most operators are not using personalised marketing techniques
  • Figure 11: On average, most customer interactions are not contextual
  • Figure 12: Online marketing conversion rates are at 31% across operators
  • Figure 13: A minority of purchases are being scaled up
  • Figure 14: Omnichannel operators excel in app-based customer engagementrst
  • Figure 15: Omnichannel operators are ahead in the number of channels a customer can use to raise a ticket
  • Figure 16: Omnichannel operators excel in the functionality of their channel offerings
  • Figure 17: Omnichannel operators lead converged billing capabilities
  • Figure 18: Omnichannel operators are on average twice as likely to have complete and partial visibility of customers compared to Digital Nascent operators

Great customer experience: What’s the secret?

Introduction: How important is customer centricity for telecoms operators?

The need for improvement

Many network operators appreciate the need to improve their customers’ overall experience if their businesses are to prosper. Their executives understand the effect customer experience has on churn and customer lifetime value, and in turn on market share, operating costs, and revenues. This relationship is illustrated in Figures 2 and 3 for mobile telecoms, pay TV and internet. Using ‘Net Promoter Scores’ (NPS), the most widely accepted measure of customer satisfaction, it shows the relationship between NPS promoters (those more positive than negative and willing to promote the brand), passives (neither positive nor negative) and detractors (those who actively dissuade others), and churn and lifetime value.

Figure 2: NPS Promoters, Passives & Detractors vs Churn and Lifetime Value

Source: Bain & Co

Figure 3: Lifetime Value of Promoters, Passives and Detractors

Source: Bain & Co

While most appreciate in general terms what customer centricity means, it is not always well understood what good customer centric service should look like in practice, or how it can be achieved. Many would say that a service where all systems worked properly, customer queries were answered correctly, problems resolved quickly and few if any complaints were made to the national regulator, was providing a fully satisfactory service to its customers, and therefore providing a good customer experience. Given the complexities of delivering a mobile telecoms service, for many operators, delivering those would be an achievement.

However, that may not be what a customer regards as a good experience, and operators need to bear in mind that their customers compare them with other service providers, and not just other telecoms providers. They need to ask themselves if they should therefore aspire to something better than the satisfactory operation of their networks and services. To decide if that is the case, operators need to determine what a good customer experience is from a user’s standpoint, and establish means of assessing whether they have delivered that or not.

Contents:

  • Executive Summary
  • Introduction
  • How important is customer centricity for telecoms operators?
  • The need for improvement
  • What does customer centricity mean for operators?
  • Customer centric networks
  • Network performance to meet user needs
  • Customer premises networks
  • Customer centric services in a digital world
  • Improving service
  • Systems integration & AI
  • All channels to look and behave the same
  • Using AI to improve customer experience
  • Customer centric service enhancements
  • Customer centric service
  • Lessons from Ritz-Carlton, a premium service
  • Cricket: US MVNO increasing NPS, cutting churn
  • TELUS: Creating, recognising and measuring success
  • TELUS performance: Measuring success
  • Conclusions

Figures:

  • Figure 1: Key Steps to Deliver Satisfactory and Exceptional Service
  • Figure 2: NPS Promoters, Passives & Detractors vs Churn and Lifetime Value
  • Figure 3: Lifetime Value of Promoters, Passives and Detractors
  • Figure 4: US Consumer NPS Scores for Different Industries
  • Figure 5: Average NPS for Telecommunications Operators in 9 Developed Countries
  • Figure 6: Highest Scoring Companies in US for Their Sector 11Highest Scoring Companies in US for Their Sector
  • Figure 7: Importance of criteria for choosing a mobile internet provider
  • Figure 8: MobiNEX segmentation dimensions
  • Figure 9:  Mobinex H2 2016 – Average scores by country
  • Figure 10: Operator and Country Scores for Reliability and Speed
  • Figure 11: Cricket wireless tariff structure
  • Figure 12: Single customer view and omni-channel insights of CMOs
  • Figure 13: TOBi, Vodafone’s AI chatbot
  • Figure 14: Amelia functions and applications
  • Figure 15: Impact of AI on media company call handling
  • Figure 16: Change in cricket NPS score from Q3 2014 to Q3 2015
  • Figure 17: TELUS monthly churn
  • Figure 18: TELUS employee engagement
  • Figure 19: Number of complaints made to the CCTS by year
  • Figure 20: TELUS ARPU 2007 – 2016
  • Figure 21: TELUS EBITDA

MobiNEX: The Mobile Network Experience Index, H1 2016

Executive Summary

In response to customers’ growing usage of mobile data and applications, in April 2016 STL Partners developed MobiNEX: The Mobile Network Experience Index, which ranks mobile network operators by key measures relating to customer experience. To do this, we benchmark mobile operators’ network speed and reliability, allowing individual operators to see how they are performing in relation to the competition in an objective and quantitative manner.

Operators are assigned an individual MobiNEX score out of 100 based on their performance across four measures that STL Partners believes to be core drivers of customer app experience: download speed, average latency, error rate and latency consistency (the proportion of app requests that take longer than 500ms to fulfil).

Our partner Apteligent has provided us with the raw data for three out of the four measures, based on billions of requests made from tens of thousands of applications used by hundreds of millions of users in H1 2016. While our April report focused on the top three or four operators in just seven Western markets, this report covers 80 operators drawn from 25 markets spread across the globe in the first six months of this year.

The top ten operators were from Japan, France, the UK and Canada:

  • Softbank JP scores highest on the MobiNEX for H1 2016, with high scores across all measures and a total score of 85 out of 100.
  • Close behind are Bouygues FR (80) and Free FR (79), which came first and second respectively in the Q4 2015 rankings. Both achieve high scores for error rate, latency consistency and average latency, but are slightly let down by download speed.
  • The top six is completed by NTT DoCoMo JP (78), Orange FR (75) and au (KDDI) JP (71).
  • Slightly behind are Vodafone UK (65), EE UK (64), SFR FR (63), O2 UK (62) and Rogers CA (62). Except in the case of Rogers, who score similarly on all measures, these operators are let down by substantially worse download speeds.

The bottom ten operators all score a total of 16 or lower out of 100, suggesting a materially worse customer app experience.

  • Trailing the pack with scores of 1 or 2 across all four measures were Etisalat EG (4), Vodafone EG (4), Smart PH (5) and Globe PH (5).
  • Beeline RU (11) and Malaysian operators U Mobile MY (9) and Digi MY (9) also fare poorly, but benefit from slightly higher latency consistency scores. Slightly better overall, but still achieving minimum scores of 1 for download speed and average latency, are Maxis MY (14) and MTN ZA (12).

Overall, the extreme difference between the top and bottom of the table highlights a vast inequality in network quality customer experience across the planet. Customer app experience depends to a large degree on where one lives. However, our analysis shows that while economic prosperity does in general lead to a more advanced mobile experience as you might expect, it does not guarantee it. Norway, Sweden, Singapore and the US market are examples of high income countries with lower MobiNEX scores than might be expected against the global picture. STL Partners will do further analysis to uncover more on the drivers of differentiation between markets and players within them.

 

MobiNEX H1 2016 – included markets

MobiNEX H1 2016 – operator scores

 Source: Apteligent, OpenSignal, STL Partners analysis

 

  • About MobiNEX
  • Changes for H1 2016
  • MobiNEX H1 2016: results
  • The winners: top ten operators
  • The losers: bottom ten operators
  • The surprises: operators where you wouldn’t expect them
  • MobiNEX by market
  • MobiNEX H1 2016: segmentation
  • MobiNEX H1 2016: Raw data
  • Error rate
  • Latency consistency
  • Download speed
  • Average latency
  • Appendix 1: Methodology and source data
  • Latency, latency consistency and error rate: Apteligent
  • Download speed: OpenSignal
  • Converting raw data into MobiNEX scores
  • Setting the benchmarks
  • Why measure customer experience through app performance?
  • Appendix 2: Country profiles
  • Country profile: Australia
  • Country profile: Brazil
  • Country profile: Canada
  • Country profile: China
  • Country profile: Colombia
  • Country profile: Egypt
  • Country profile: France
  • Country profile: Germany
  • Country profile: Italy
  • Country profile: Japan
  • Country profile: Malaysia
  • Country profile: Mexico
  • Country profile: New Zealand
  • Country profile: Norway
  • Country profile: Philippines
  • Country profile: Russia
  • Country profile: Saudi Arabia
  • Country profile: Singapore
  • Country profile: South Africa
  • Country profile: Spain
  • Country profile: United Arab Emirates
  • Country profile: United Kingdom
  • Country profile: United States
  • Country profile: Vietnam

 

  • Figure 1: MobiNEX scoring breakdown, benchmarks and raw data used
  • Figure 2: MobiNEX H1 2016 – included markets
  • Figure 3: MobiNEX H1 2016 – operator scores breakdown (top half)
  • Figure 4: MobiNEX H1 2016 – operator scores breakdown (bottom half)
  • Figure 5: MobiNEX H1 2016 – average scores by country
  • Figure 6: MobiNEX segmentation dimensions
  • Figure 7: MobiNEX segmentation – network speed vs reliability
  • Figure 8: MobiNEX segmentation – network speed vs reliability – average by market
  • Figure 9: MobiNEX vs GDP per capita – H1 2016
  • Figure 10: MobiNEX vs smartphone penetration – H1 2016
  • Figure 11: Error rate per 10,000 requests, H1 2016 – average by country
  • Figure 12: Error rate per 10,000 requests, H1 2016 (top half)
  • Figure 13: Error rate per 10,000 requests, H1 2016 (bottom half)
  • Figure 14: Requests with total roundtrip latency > 500ms (%), H1 2016 – average by country
  • Figure 15: Requests with total roundtrip latency > 500ms (%), H1 2016 (top half)
  • Figure 16: Requests with total roundtrip latency > 500ms (%), H1 2016 (bottom half)
  • Figure 17: Average weighted download speed (Mbps), H1 2016 – average by country
  • Figure 18: Average weighted download speed (Mbps), H1 2016 (top half)
  • Figure 19: Average weighted download speed (Mbps), H1 2016 (bottom half)
  • Figure 20: Average total roundtrip latency (ms), H1 2016 – average by country
  • Figure 21: Average total roundtrip latency (ms), H1 2016 (top half)
  • Figure 22: Average total roundtrip latency (ms), H1 2016 (bottom half)
  • Figure 23: Benchmarks and raw data used

MobiNEX: The Mobile Network Experience Index, Q4 2015

Executive Summary

In response to customers’ growing usage of mobile data and applications, STL Partners has developed MobiNEX: The Mobile Network Customer Experience Index, which benchmarks mobile operators’ network speed and reliability by measuring the consumer app experience, and allows individual players to see how they are performing in relation to competition in an objective and quantitative manner.

We assign operators an individual MobiNEX score based on their performance across four measures that are core drivers of customer app experience: download speed; average latency; error rate; latency consistency (the percentage of app requests that take longer than 500ms to fulfil). Apteligent has provided us with the raw data for three out of four of the measures based on billions of requests made from tens of thousands of applications used by hundreds of millions of users in Q4 2015. We plan to expand the index to cover other operators and to track performance over time with twice-yearly updates.

Encouragingly, MobiNEX scores are positively correlated with customer satisfaction in the UK and the US suggesting that a better mobile app experience contributes to customer satisfaction.

The top five performers across twenty-seven operators in seven countries in Europe and North America (Canada, France, Germany, Italy, Spain, UK, US) were all from France and the UK suggesting a high degree of competition in these markets as operators strive to improve relative to peers:

  • Bouygues Telecom in France scores highest on the MobiNEX for Q4 2015 with consistently high scores across all four measures and a total score of 76 out of 100.
  • It is closely followed by two other French operators. Free, the late entrant to the market, which started operations in 2012, scores 73. Orange, the former national incumbent, is slightly let down by the number of app errors experienced by users but achieves a healthy overall score of 70.
  • The top five is completed by two UK operators: EE (65) and O2 (61) with similar scores to the three French operators for everything except download speed which was substantially worse.

The bottom five operators have scores suggesting a materially worse customer app experience and we suggest that management focuses on improvements across all four measures to strengthen their customer relationships and competitive position. This applies particularly to:

  • E-Plus in Germany (now part of Telefónica’s O2 network but identified separately by Apteligent).
  • Wind in Italy, which is particularly let down by latency consistency and download speed.
  • Telefónica’s Movistar, the Spanish market share leader.
  • Sprint in the US with middle-ranking average latency and latency consistency but, like other US operators, poor scores on error rate and download speed.
  • 3 Italy, principally a result of its low latency consistency score.

Surprisingly, given the extensive deployment of 4G networks there, the US operators perform poorly and are providing an underwhelming customer app experience:

  • The best-performing US operator, T-Mobile, scores only 45 – a full 31 points below Bouygues Telecom and 4 points below the median operator.
  • All the US operators perform very poorly on error rate and, although 74% of app requests in the US were made on LTE in Q4 2015, no US player scores highly on download speed.

MobiNEX scores – Q4 2015

 Source: Apteligent, OpenSignal, STL Partners analysis

MobiNEX vs Customer Satisfaction

Source: ACSI, NCSI-UK, STL Partners

 

  • Introduction
  • Mobile app performance is dependent on more than network speed
  • App performance as a measure of customer experience
  • MobiNEX: The Mobile Network Experience Index
  • Methodology and key terms
  • MobiNEX Q4 2015 Results: Top 5, bottom 5, surprises
  • MobiNEX is correlated with customer satisfaction
  • Segmenting operators by network customer experience
  • Error rate
  • Quantitative analysis
  • Key findings
  • Latency consistency: Requests with latency over 500ms
  • Quantitative analysis
  • Key findings
  • Download speed
  • Quantitative analysis
  • Key findings
  • Average latency
  • Quantitative analysis
  • Key findings
  • Appendix: Source data and methodology
  • STL Partners and Telco 2.0: Change the Game
  • About Apteligent

 

  • MobiNEX scores – Q4 2015
  • MobiNEX vs Customer Satisfaction
  • Figure 1: MobiNEX – scoring methodology
  • Figure 2: MobiNEX scores – Q4 2015
  • Figure 3: Customer Satisfaction vs MobiNEX, 2015
  • Figure 4: MobiNEX operator segmentation – network speed vs network reliability
  • Figure 5: MobiNEX operator segmentation – with total scores
  • Figure 6: Major Western markets – error rate per 10,000 requests
  • Figure 7: Major Western markets – average error rate per 10,000 requests
  • Figure 8: Major Western operators – percentage of requests with total roundtrip latency greater than 500ms
  • Figure 9: Major Western markets – average percentage of requests with total roundtrip latency greater than 500ms
  • Figure 10: Major Western operators – average weighted download speed across 3G and 4G networks (Mbps)
  • Figure 11: Major European markets – average weighted download speed (Mbps)
  • Figure 12: Major Western markets – percentage of requests made on 3G and LTE
  • Figure 13: Download speed vs Percentage of LTE requests
  • Figure 14: Major Western operators – average total roundtrip latency (ms)
  • Figure 15: Major Western markets – average total roundtrip latency (ms)
  • Figure 16: MobiNEX benchmarks

Lag Kills! How App Latency Wrecks Customer Experience

Executive Summary

  • STL Partners’ analysis shows that while latency and app errors are only weakly correlated across the whole of Europe, once outlying operators (SFR, Wind and those in Germany) are removed, there is a strong positive correlation between the two: as latency increases so do app errors.
  • Intuitively, this makes sense: apps ‘time out’ waiting for responses causing errors and crashes.
  • Latency and app errors both negatively affect customer experience – customers are more likely to abandon apps as responsiveness and error rates increase:
    • 48% of users would uninstall or stop using an app if it regularly ran slowly.
    • 53% of users would uninstall or stop using an app if it regularly crashed, stopped responding or had errors.
  • Historically, customers have tended to hold the app developer responsible for errors (55% of users blame the app for problems and only 22% the mobile operator) but mobile operators have a significant impact on how quickly an app runs and how likely it is to experience an error and, as understanding of the operators’ role grows, users may well use this as a criterion when selecting their mobile service provider.
  • Performance among Europe’s operators for app latency and errors varies widely:
    • The worst-performing operator in Europe (3 Italy) experiences over three times the amount of requests with poor latency compared to the best-performer (Bouygues Telecom).
    • The worst-performing operator in Europe (O2 Germany) results in over twice the number of app errors than the best-performer (Bouygues Telecom again).
  • Improving customer experience is rapidly becoming a mantra of operators globally and for several players (in Europe at least) improving latency performance and reducing app errors caused by latency and other factors should be a key priority. For without improvement, poor performing operators will find themselves at a disadvantage and may struggle to retain existing customers and recruit new ones.

Introduction

Key objectives

Network latency is a key driver of user experience. In applications as diverse as e-commerce, VoIP, gaming, video or audio content delivery, search, online advertising, financial services, and the Internet of Things, increased latency has a direct and negative impact on customers. With higher latency, customers fail to complete tasks, leave applications, or experience application errors. This, in turn, results poorer core business KPIs for the application provider – lower ratings, fewer subscribers, or reduced advertising fees.

As we showed in a recent report titled Mobile app latency in Europe: French operators lead; Italian & Spanish lag, with the modern Internet dominated by flows of small packets on fast networks, latency accounts for the biggest share of total load times and tends to determine the actual data transfer rates users see. And, as web and mobile applications increasingly consist of large numbers of requests to independent ‘microservices’, jitter – the variation in latency – becomes a more significant threat to the consumer experience. Furthermore, we benchmarked major European mobile network operators (MNOs) on average latency and the rate of unacceptably high-latency events (over 500ms).

In this second report on latency, which again uses data provided by app analytics specialist Apteligent (formerly Crittercism), we look at the rate of app errors – evidently, something that could not impact user experience more directly – and its correlation with both latency, and the rate of unacceptable high-latency events. We explore how often apps fail across the same set of MNOs, test if latency is a driver of app errors, and then conclude whether or not our theory that it is a real driver of consumer experience is correct.

Source data and methodology

Our partner, Apteligent, collects a wide variety of analytics data from thousands of mobile apps used by hundreds of millions of people around the world in their every-day lives and work. To date, the primary purpose of the data has been to help app developers make better apps. We are now working with Crittercism to produce further insights from the data to serve the global community of mobile operators.

This data-set includes the average network latency experienced at the application layer, the percentage of network requests above 500ms round-trip time, the 5th and 95th percentiles, and the rate of application errors. All of these data points are useful in trying to understand the overall experience of customers using their mobile apps, and in particular the delays and problems they’ve experienced such as long screen wait times and applications failing to work.

We showed in the previous report how the longest round-trip delays or ‘app-lags’ (i.e. those over 500ms) are the most important KPI to look at when trying to understand customer experience. This is firstly because people really notice individual delays of this length. For people used to high speed broadband, it’s like going back to narrowband internet – it seems incredibly slow!

Importantly though, in modern apps, the distribution of delays is even more significant, as each app or web page typically makes multiple requests over the internet before it can load fully – and each of these requests will suffer some form of delay or latency.

A detailed explanation of this and of the collection methodology is available in the first report.

The Impact of latency on app errors

First glance: a positive correlation overall, but a weak one

The following chart shows the error rate per 10,000 app requests, plotted against the percentage of requests over 500ms round-trip time, by carrier. Each dot represents a week’s performance and we’ve looked at 12 weeks of data from 20 operators, from the week of 03/08/15 to the week beginning 19/10/2015. The hypothesis being that the more requests with unacceptable latency there are, the more app errors, because apps ‘time-out’ or key requests are not fulfilled in time causing an app error or, worse, a crash.

Figure 1: Latency and errors for the top 20 European MNOs over the last 12-weeks appear correlated, but there are some important outliers

Source: STL Partners, Apteligent

At first glance, there appears to be only a weak positive relationship between latency and error rates but there does seem to be a natural grouping found between the two hand-drawn dotted lines on the chart with the weeks above the upper boundary (potentially) being outliers, in which at least one other factor is driving application errors up.

The lower boundary seems to represent the underlying rate of app-errors that occur when there are no latency issues (between 20 and 50 errors per ten thousand plus an increasing error rate as higher latency kicks in. For example, when 10% of requests experience latency above 500ms, the minimum error rate is around 30 per 10,000 requests, rising to 50 at the 35% mark.

  • Executive Summary
  • Introduction
  • Key objectives
  • Source data and methodology
  • The Impact of Latency on App Errors
  • First glance: a positive correlation overall, but a weak one
  • Outliers are specific countries and operators
  • Strong positive correlation between latency and app errors once outliers are excluded
  • App Errors: The Impact on Customer Experience
  • Latency and errors – both bad for the customer
  • Appendix: Country Analysis
  • France: A Clear Relationship
  • The UK: Strong Latency-Error Correlation
  • Spain: A mixed picture, but latency is still predictive of app errors
  • Italy: Wind is a super-outlier
  • Germany: Nothing but Outliers?
  • STL Partners and Telco 2.0: Change the Game
  • About Apteligent (formerly Crittercism)

 

  • Figure 1: Latency and errors for the top 20 European MNOs over the last 12-weeks appear correlated, but there are some important outliers
  • Figure 2: 12-week average latency and app error performance by operator
  • Figure 3: After excluding the key outliers, high-latency events explain 75% of the app error rate across Europe’s top 20 operators
  • Figure 4: Expected number of errors when loading 20 web pages of Amazon
  • Figure 5: France shows both the best performers, and a very clear relationship between latency and app errors
  • Figure 6: The latency-error correlation is strongest in the UK
  • Figure 7: High variation in latency complicates the picture, but a third of app error variation is still driven by latency
  • Figure 8: Wind complicates the picture, but the trend is still there
  • Figure 9: Germany – is there any trend at all?
  • Figure 10: The source of the outliers – Germany in August