Tag: case studies

Telcos in health – Part 2: How to crack the healthcare opportunity

This report is a follow-up from our first report Telcos in health – Part 1: Where is the opportunity? which looked at overarching trends in digital health and how telcos, global internet players, and health focused software and hardware vendors are positioning themselves to address the needs of resource-strained healthcare providers.

It also build on in depth case studies we did on TELUS Health and Telstra Health.

Telcos should invest in health if…

  • They want to build new revenue further up the IT value chain
  • They are prepared to make a long term commitment
  • They can clearly identify a barrier to healthcare access and/or delivery in their market

…Then healthcare is a good adjacent opportunity with strong long term potential that ties closely with core telco assets beyond connectivity:

  • Relationships with local regulators
  • Capabilities in data exchange, transactions processing, authentication, etc.

Telcos can help healthcare systems address escalating resourcing and service delivery challenges

Pressures on healthcare - ageing populations and lack of resources
Chart showing the dynamics driving challenges in healthcare systems

Telcos can help overcome the key barriers to more efficient, patient-friendly healthcare:

  • Permissions and security for sharing data between providers and patients
  • Surfacing actionable insights from patient data (e.g. using AI) while protecting their privacy

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Why telcos’ local presence makes them good candidates to coordinate the digital and physical elements of healthcare

  • As locally regulated organisations, telcos can position themselves as more trustworthy than global players for exchange and management of health data
  • Given their universal reach, telcos make good partners for governments seeking to improve access and monitor quality of healthcare, e.g.:
    • Telco-agnostic, national SMS shortcodes could be created to enable patients to access health information and services, or standard billing codes linked to health IT systems for physicians to send SMS reminders
    • Partner with health delivery organisations to ensure available mobile health apps meet best practice guidelines
    • Authentication and digital signatures for high-risk drugs like opioids
  • Healthcare applications need more careful development than most consumer sectors, playing to telcos’ strengths – service developers should not take a “fail fast” approach with people’s health

Telcos have further reach across the diverse  healthcare ecosystem than most companies

The complexity of healthcare systems - what needs to be linked
To coordinate healthcare, you need to make these things work together

However, based on the nine telco health case studies in this report, to successfully help healthcare customers adopt IoT, data-driven processes and AI, telcos must offer at least some systems integration, and probably develop much more health-specific IT solutions.

Case study overview: Depth of healthcare focus

Nine telcos shown on a spectrum of the kind of healthcare services they provide
Where Vodafone, AT&T, BT, Verizon, O2, Swisscom, Telstra, Telenor Tonic and TELUS Health fit on a spectrum of services to healthcare,

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

Introduction

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

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

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

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Defining AI: An evolution in analytics

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

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

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

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

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

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

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

Contents:

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

Figures:

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

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Big data analytics – Time to up the ante

Introduction

Recent years have seen an explosion in the amount of data being generated by people and devices, thanks to more advanced network infrastructure, widespread adoption of smartphones and related applications, and digital consumer services. With the expansion of the Internet of Things (IoT), the amount of data being captured, stored, searched and analysed will only continue to increase. Such is the volume and variety of the data that it is beyond traditional processing software and is therefore referred to as ‘big data’.

Big data is of a greater magnitude and variety than traditional data, it comes from multiple sources and can be comprised of various formats, generated, stored and utilised in batches and/or in real-time. There is much talk and discussion around big data and analytics and its potential in many sectors, including telecommunications. As Figure 1 shows, analysis of big data can give an improved basis upon which to base human-led and automated decisions by providing better insight and allowing greater understanding of the situation being addressed.

Figure 1: Using Big Data can result in richer data insights

Source: STL Partners

This report analyses how telcos are pursuing big data analytics, and how to be successful in this regard.  This report seeks to answer the following questions:

  • When does data become ‘big’ and why is it an important issue for telcos?
  • What is the current state of telco big data implementations?
  • Who is doing what in terms of intelligent use of data and analytics?
  • How can big data analytics improve internal operational efficiencies?
  • How can big data be used to improve the relationship between telcos and their customers?
  • Where are the greatest revenue opportunities for telcos to employ big data, e.g. B2B, B2C?
  • Which companies are leading the way in enabling telcos to successfully realise big data strategies?
  • What is required in terms of infrastructure, dedicated teams and partners for successful implementation?

This report discusses implementations of big data and examines how the market will develop as telco awareness, understanding and readiness to make use of big data improves.  It provides an overview of the opportunities and use cases that can be realised and recommends what telcos need to do to achieve these.

Contents:

  • Executive Summary
  • Big data analytics is important
  • …but it’s not a quick win
  • …it’s a strategic play that takes commitment
  • How is ‘big data analytics’ different from ‘analytics’?
  • Opportunities for telcos: typically internal then external
  • Market development and trends
  • Challenges and restrictions in practice
  • What makes a successful big data strategy?
  • Next steps
  • Introduction
  • Methodology
  • An overview of big data analytics
  • Volume, variety and velocity – plus veracity and value
  • The significance of big data for telcos and their future strategies
  • Market development and trends
  • Challenges and restrictions
  • Optimisation and efficiency versus data monetisation
  • Telcos’ big data ecosystem
  • Case studies and results 
  • Early results
  • Big data analytics use cases
  • Examples of internal use-cases
  • Examples of external use cases
  • Findings, conclusions and recommendations

Figures:

  • Figure 1: Using Big Data can result in richer data insights
  • Figure 2: The data-centric telco: infusing data to improve efficiency across functions
  • Figure 3: Options for telcos’ big data implementations
  • Figure 4: Telco’s big data partner ecosystem
  • Figure 5: The components of a telco-oriented big data

Telefónica’s NFV: An Empire Divided?

Objectives and strategic rationale

There are two main strategic drivers behind Telefónica’s NFV play. First, there is a stark operational imperative around the need to accommodate increasing demands on the network while reducing costs. In a briefing to journalists that publicly launched Telefónica’s NFV transformation, the operator’s CTO of Global Resources Enrique Blanco explained that the operator could achieve 10% to 15% efficiency savings using its existing network technology deployments, whereas it faced a 25% to 30% annual growth in traffic, much of which occurred on the fixed network but was generated by wireless devices connected to WiFi. Behind these operational factors lie financial concerns, with highly challenged margins and poor shareholder returns over a number of years.

Telefónica believes it can achieve a reduction in opex of up to 30% through NFV, while the technology will also enable new services to be introduced more rapidly and network resources to be used more efficiently. For example, according to the operator, it can take up to four months to add capacity to a traditional IMS based on a single-vendor hardware and software solution. Using a virtualized IMS, it will in theory be possible to carry out the requisite upgrades in hours or even minutes, with the possibility to add capacity through simple software upgrades or swap-outs. In addition, a key factor for Telefónica – and one that appears to be front of mind in the CTO’s office – is the ability to rationalize the network and service platforms across its multinational operations, including its global enterprise networking business and numerous Latin American subsidiaries. From this perspective, NFV should enable network resources to be shared and standardized internationally, so that some markets would no longer need their own physical network elements.2 However, the parts of Telefónica’s organization driving the open-source NFV infrastructure model appear to take a different view on this (to be discussed further below).

The second main driver behind the program – and one that corresponds to the open-standards emphasis adopted by Telefónica’s I+D (R&D) department – is the aim of adding impetus to overall industry transformation around NFV. A global player such as Telefónica throwing its weight behind NFV sends an important signal to the industry that virtualization is moving from the laboratory testing stage to commercial deployment, and that it will effect a comprehensive transformation of operators’ networks and organizations. In this sense, Telefónica’s launch of its NFV program can be seen as intended to galvanize the industry, and in particular the vendor ecosystem, into collaborating seriously with it to develop production-ready, carrier-grade implementations of NFV.

A key aim here is to ensure that vendors build their VNFs (Virtual Network Functions) and NFV platforms around genuinely open standards, rather than proprietary implementations that risk locking operators in to particular vendors. This dependency on proprietary vendor solutions, with a consequent increase in costs and decline in operational agility, is one of the main things that Telefónica and the industry at large are seeking to eliminate through NFV. But to achieve this objective requires a healthy ecosystem of vendors working around common standards and models. As things have worked out, Telefónica has tried out both a lead-vendor and multi-vendor approach to realizing its objectives – corresponding to the two strategic and technological perspectives sketched out here – and Telefónica’s NFV journey has been marked by a confusing swing between one and the other.

…to access the other 14 pages of this 15 page Telco 2.0 Report, including…

  • Executive Summary
  • Objectives and strategic rationale
  • Progress and key milestones
  • Conclusion: bringing it all together – Where’s Alierta?

…and the following report figures…

  • Figure 1: Telefónica’s initial virtualization objectives
  • Figure 2: By March 2015, Telefonica’s virtualization timetable had changed significantly