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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A3 for enterprise: Where should telcos focus?

A3 capabilities operators can offer enterprise customers

In this research we explore the potential enterprise solutions leveraging analytics, AI and automation (A3) that telcos can offer their enterprise customers. Our research builds on a previous STL Partners report Telco data monetisation: What’s it worth? which modelled the financial opportunity for telco data monetisation – i.e. purely the machine learning (ML) and analytics component of A3 – for 200+ use cases across 13 verticals.

In this report, we expand our analysis to include the importance of different types of AI and automation in implementing the 200+ use cases for enterprises and assess the feasibility for telcos to acquire and integrate those capabilities into their enterprise services.

We identified eight different types of A3 capabilities required to implement our 200+ use cases.

These capability types are organised below roughly in order of the number of use cases for which they are relevant (i.e. people analytics is required in the most use cases, and human learning is needed in the fewest).

The ninth category, Data provision, does not actually require any AI or automation skills beyond ML for data management, so we include it in the list primarily because it remains an opportunity for telcos that do not develop additional A3 capabilities for enterprise.

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Most relevant A3 capabilities across 200+ use cases

9-types-of-A3-analytics-AI-automation

Most relevant A3 capabilities for leveraging enterprise solutions

People analytics: This is the strongest opportunity for telcos as it uses their comprehensive customer data. Analytics and machine learning are required for segmentation and personalisation of messaging or action. Any telco with a statistically-relevant market share can create products – although specialist sales capabilities are still essential.

IoT analytics: Although telcos offering IoT products do not immediately have access to the payload data from devices, the largest telcos are offering a range of products which use analytics/ML to detect patterns or spot anomalies from connected sensors and other devices.

Other analytics: Similar to IoT, the majority of other analytics A3 use cases are around pattern or anomaly detection, where integration of telco data can increase the accuracy and success of A3 solutions. Many of the use cases here are very specific to the vertical. For example, risk management in financial services or tracking of electronic prescriptions in healthcare – which means that a telco will need to have existing products and sales capability in these verticals to make it worthwhile adding in new analytics or ML capabilities.

Real time: These use cases mainly need A3 to understand and act on triggers coming from customer behaviour and have mixed appeal to telcos. Telcos already play a significant role in a small number of uses cases, such as mobile marketing. Some telcos are also active in less mature use cases such as patient messaging in healthcare settings (e.g. real-time reminders to take medication or remote monitoring of vulnerable adults). Of the rest of the use cases that require real time automation, a subset could be enhanced with messaging. This would primarily be attractive to mobile operators, especially if they offer broader relevant enterprise solutions – for example, if a telco was involved in a connected public transport solution, then it could also offer passenger messaging.

Remote monitoring/control: Solutions track both things and people and use A3 to spot issues, do diagnostic analysis and prescribe solutions to the problems identified. The larger telcos already have solutions in some verticals, and 5G may bring more opportunities, such as monitoring of remote sites or traffic congestion monitoring.

Video analytics: Where telcos have CCTV implementations or video, there is opportunity to add in analytics solutions (potentially at the edge).

Human interactions: The majority of telco opportunities here relate to the provision of chatbots into enterprise contact centres.

Human learning: A group of low feasibility use cases around training (for example, an engineer on a manufacturing floor who uses a heads-up augmented/virtual reality (AR/VR) display to understand the resolution to a problem in front of them) or information provision (for example, providing retail customers with information via AR applications).

 

Table of Contents

  • Executive Summary
    • Which A3 capabilities should telcos prioritise?
    • What makes an investment worthwhile?
    • Next steps
  • Introduction
  • Vertical opportunities
    • Key takeaways
  • A3 technology: Where should telcos focus?
    • Key takeaways
    • Assessing the telco opportunity for nine A3 capabilities
  • Verizon case study
  • Details of vertical opportunities
  • Conclusion
  • Appendix 1 – full list of 200 use cases

 

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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.Request a report extract

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

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

Introduction

This is the first of two reports analysing the market position and strategies of four global technology companies – Netflix, Spotify, Tesla and Uber – that might be able to make the leap to become a top tier consumer digital player, akin to Amazon, Apple, Facebook or Google. The two reports explore how improvements in digital technologies and consumer electronics are changing the entertainment and automotive markets, allowing the four companies to cause significant disruption in their sectors.

The first part of this report considers Netflix and Spotify, which are both trying to disrupt the entertainment market. For more on the increasing domination of online entertainment by the big Internet platforms, read the STL Partners report Amazon, Apple, Facebook, Google, Netflix: Whose digital content is king?

This report considers how well Netflix and Spotify are prepared for the likely technological changes in their markets. It also provides a high-level overview of the opportunities for telcos, including partnership strategies, and the implications for telcos if one of the companies were able to make the jump to become a tier one platform.

STL Partners is analysing the prospects of Netflix, Spotify, Tesla and Uber because all four have proven to be highly disruptive players in their relevant industries.

The four are defined by three key factors, which set them aside from their fellow challengers:

  • Rapid rise: They have become major mainstream players in a short space of time, building world-leading brands that rival those of much older and more established companies.
  • New thinking: Each of the four has challenged the conventions of the industries in which they operate, leading to major disruption and forcing incumbents to completely re-evaluate their business models.
  • Potential to challenge the dominance of Amazon, Apple, Facebook or Google: This rapid success has allowed the companies to gain dominant positions in their relative sectors, which they have used as a springboard to diversify their business models into parallel verticals. By pursuing these economies of scope, they are treading the path taken by the big four Internet companies (see Figure 1). Google, Apple, Facebook and Amazon have come from very diverse roots (ranging from an Internet search engine to a mobile device manufacturer), but are now directly competing with each other in a number of areas (communications, content, commerce and hardware).

Figure 1: How the leading Internet companies have diversified

Source: STL Partners

The evolution of online entertainment

As broadband networks proliferate and households are served by fatter pipes, telecoms networks are carrying more and more entertainment content. While there are major players in every country and region, there are essentially only six online entertainment platforms meeting this demand on a global scale – Amazon, Apple, Facebook, Google, Netflix and Spotify. These six companies are delivering increasingly sophisticated real-time entertainment services that are generating a growing proportion of Internet traffic, at the expense of traditional web browsing, file sharing, download services and physical retail entertainment.

The six are building global economies of scale that can’t be matched by national/regional media companies and telcos. Global distribution is becoming increasingly important in the media industry, given the prohibitive costs of sourcing content and then packaging it and distributing it across multiple different devices and networks.

Scale is also important for another reason. As the volume of digital content proliferates, consumers increasingly rely on recommendations. The platform capturing the most behavioural data (people who watched this, also watched this) should be able to offer the best recommendations.

Although the platforms with scale have a competitive advantage, they are still vulnerable to disruption because the online entertainment market is evolving rapidly with providers, including rights owners, experimenting with new formats and concepts.

As outlined in the STL Partners report Amazon, Apple, Facebook, Google, Netflix: Whose digital content is king?, most of this experimentation relates to the following six key trends, which are likely to shape the online entertainment market over the next decade.

  1. Greater investment in exclusive content: The major online platforms are increasingly looking to either source or develop their own exclusive content, both as a competitive differentiator and in response to the rising cost of licensing third parties’ content. Exclusive content may be anything from live sports programming to original drama series and even blockbuster movies. This is an area in which both Netflix and Amazon Video have heavily invested, making the two direct competitors for talent in this space.
  2. Growing support for live programming: People like to watch major sports events and dramatic breaking news live. Some of the online platforms are responding to this demand by creating live channels and giving celebrities and consumers the tools they need to peercast – broadcast their own live video streams.
  3. The changing face of user-generated content: Although YouTube, Facebook and other social networks have always relied on user-generated content, advances in digital technologies are making this content more compelling. If they are in the right place, at the right time, even an amateur equipped with a smartphone or a drone can produce engaging video pictures.
  4. Increasingly immersive games and interactive videos: As bandwidth, latency, graphics processing and rendering technology all improve, online games are becoming more photorealistic making them increasingly akin to an interactive movie. Furthermore, virtual reality will enable people to adopt different viewpoints within a 360-degree video stream, enabling them to choose the perspective from which to watch a movie or a live sports event. For more info, please see the STL Partners’ report: AR/VR: Won’t move the 5G needle.
  5. Rising use of ad blockers and mounting privacy concerns: Many consumers are looking for ways to avoid video advertising, which is more intrusive than a static banner ad and uses more bandwidth. At the same time, many national and regional regulators are becoming increasingly alarmed by the privacy implications of the data mining of consumer services and products, leading to clashes between the major online advertising platforms and regulators.
  6. Ongoing net neutrality uncertainty: In many jurisdictions, net neutrality regulation is either still under development or is vaguely worded as regulators struggle to balance the legitimate need to prioritise some online services with the equally important need to ensure that small content and app developers aren’t discriminated against.

To read on about Netflix and Spotify’s strategies and implications for telcos, please login and download the report, or contact us to subscribe.

Contents:

  • Executive Summary
  • Netflix: much loved, but too narrow
  • Spotify: leading a formidable pack
  • Lessons for telcos
  • Conclusions for telcos
  • Introduction
  • The evolution of online entertainment
  • Netflix: Keeping it original
  • Right time, right proposition
  • Competitive clouds gathering
  • Economies of scale, but not scope
  • Strengths
  • Weaknesses
  • Opportunities
  • Threats
  • Spotify: The power of the playlist
  • Smaller than Netflix, but more rounded
  • Strengths
  • Weaknesses
  • Opportunities
  • Threats
  • Takeaways for telcos
  • Lessons for telcos
  • Next steps for telcos

Figures:

  • Figure 1: How the leading Internet companies have diversified
  • Figure 2: Netflix revenue and paid subscriber growth, 2015-2017
  • Figure 3: Netflix has grown much faster than its rivals in the US
  • Figure 5: Netflix from a monolithic website to a flexible microservices architecture
  • Figure 6: Netflix: SWOT analysis
  • Figure 7: Tailoring movie artwork to the individual viewer
  • Figure 8: Netflix’s addressable market is growing steadily
  • Figure 9: The number of mobile broadband connections is rising rapidly
  • Figure 10: How studio films aim to make money using release windows
  • Figure 11: Hulu’s broad proposition is a challenge to Netflix
  • Figure 12: Growth in digital music is now offsetting declining sales of physical formats
  • Figure 13: Spotify’s rapid revenue and paid subscriber growth
  • Figure 14: Spotify’s fast-growing premium service is the profit engine
  • Figure 15: A SWOT analysis for Spotify
  • Figure 16: Spotify has significantly lower ARPU and costs than Netflix
  • Figure 17: Spotify’s losses continue to grow despite rapid revenue rises
  • Figure 18: Spotify’s costs are rising rapidly
  • Figure 19: YouTube is a major destination for music lovers

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:

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

Telco 2.0: Report and analysis of the event

Telco 2.0: Event Summary Analysis. A summary of the findings of the Telco 2.0 Executive Brainstorm, 9th November 2011, held in the Guoman Tower Hotel, London. The Brainstorm explored telcos’ strategic options to grow in the fast changing digital economy. It also considered how telcos can defend their core voice and messaging business, while also examining the steps they can take to improve the customer experience.

Telco 2.0: Event Summary Analysis Presentation


Part of the New Digital Economics Executive Brainstorm series, the Telco 2.0 event took place at the Guoman Hotel, London on the 9th November and looked at telcos’ strategic options, the future of the core communications products telcos rely on for much of their revenue and how they can improve the customer experience both to reduce churn and attract new customers.

Using a widely acclaimed interactive format called ‘Mindshare’ the event
enabled 80 specially-invited senior executives from across the communications,
media, banking and technology sectors to.

This note
summarises some of the high-level findings and includes the verbatim output of
the brainstorm.

More information: email contact@stlpartners.com, or phone: +44 (0) 207 247 5003.

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Extracted example slide:

Telco 2.0: Event Summary Analysis Presentation