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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Innovation leader case study: Telefónica Tech AI of Things

The origins of Telefónica Tech AI of Things

Telefónica LUCA was set up in 2016 to “enable corporate clients to understand their data and encourage a transparent and responsible use of that data”.

Before the creation of LUCA, Telefónica’s focus had been on developing assets and making acquisitions (e.g. Synergic Partners) to build strong internal capabilities around data and analytics – with some data monetisation capabilities housed within their Telefónica Digital unit (a global business unit selling products beyond connectivity, which was disbanded in 2016). Typical projects the team undertook related to using network data to make better decisioning for the network and marketing teams, and providing Telefónica Digital with external monetisation opportunities such as Smart Steps (aggregated, anonymised data for creation of vertical products) and Smart Digits (provision of consent-based data to the advertising industry).

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Creating the autonomous LUCA unit made a statement that Telefónica was serious about its strategy to offer data products to enterprise customers. Quoting from the original press release, “LUCA offered three lines of products and services:

The Business Insights area brings the value of anonymous and aggregated data on Telefónica’s networks for a wide range of clients. This includes Smart Steps, which is focused on mobility analysis solutions for more efficient planning. For example, to optimise transport networks and tourist management in cities, or in the case of a health emergency, in helping to better understand population movements and in limiting the spread of pandemics.

The analytical and external consultancy services for national and international clients will be provided by Synergic Partners, a company specialized in Big Data and Data Science which was acquired by Telefónica at the end of 2015.

Furthermore, LUCA will help its clients by providing BDaaS (Big Data as a Service) to empower clients to get the most out of their own data, using the Telefónica cloud infrastructure.”

The following table shows a timeline from the origins of LUCA in the Telefónica Digital business unit through to its merger into the Telefónica Tech AI of Things business in 2019 – illustrating the progression of its products and other major activities.

Timeline of Telefónica’s data monetisation business

Telefonica-data-monetisation-luca-AI-IoT

Source: STL Partners, Charlotte Patrick Consult

Points to note on the timeline above:

  • Telefónica stood out from its peers with the purchase of Synergic Partners in 2015 (bringing in 120 consultancy headcount). This provided not only another leg to the business with consulting capabilities, but also additional headcount to scope and sell their existing product sets.
  • Looking at the timeline, it took Telefónica two years from this purchase and the establishment LUCA to expand its portfolio. In 2018, a range of new, mainly IoT-related capabilities, were launched, built up from existing projects with individual customers.
  • Telefónica has added machine learning to its products across the timeframe, but in 2019 the development of NLP capability for use in Telefónica’s existing products, and an internal data science platform, were then productised for customers (see below discussion about its Aura product set).
  • As the number of products has expanded, the number of partnerships has also expanded, bringing specific platforms and capabilities which can be combined with Telefónica’s own data capabilities to provide added value (examples include CARTO which creates geographic visualisations of Telefónica’s data).
  • Looking at changing vertical priorities:
    • Telefónica has always been strong in the advertising sector, starting with products from O2 UK in 2012. The exact nature of what it has offered has changed over time and some capabilities have been sold, however, it still has a strong mobile marketing business and expects it data to become of more interest to brands/media agencies as the use of cookies diminishes across the next few years.
    • The retail sector offers opportunity, but has been challenging to target over the years. Although Telefónica has interesting data for retail companies, creating replicable products is challenging as the large retailers each have differing requirements and working with small cell data in-store can be expensive. The product set is therefore currently being simplified, as the pandemic has also reduced demand from retailers.

One of Telefónica’s key capabilities which is not clearly displayed in the timeline is the provision of services to the marketing teams of the various verticals it targets. These include analytics products which Telefónica has developed from its internal capabilities and other functionality such as pricing tools.

The formation of Telefónica Tech

In 2019, Telefónica LUCA became part of the newly formed, autonomous Telefónica Tech business unit. The organisation is split into two business areas: cybersecurity & cloud, and the assets from Telefónica LUCA combined with the IoT unit. The goal of Telefónica Tech is to:

  • Enable the financial markets to clearly see revenue progression. Telefónica’s stated aim is for sustained double digit growth, which it achieved with year-on-year growth of 13.6% in 2020, although the IoT and Big Data segment only grew 0.8% y-o-y in 2020, due to the impact of COVID-19 on IoT deployments, especially in retail. Showing signs of recovery, in H121 revenue growth in the IoT and Big Data segment rose to 8.1% y-o-y, and to 26% y-o-y for the whole of Telefónica Tech.
  • Coordinate innovation, particularly around post-pandemic opportunities such as remote working, e-health, e-commerce and digital transformation
  • Take advantage of global synergies and leveraging existing assets
  • Ease M&A and partnerships activity (it already has 300 partners to better reach new markets, including relations with 60 start-ups across products)
  • Build relationships with cloud providers (it has existing relationships with Microsoft, Google and SAP).

To better leverage existing assets, Telefónica LUCA was integrated with Telefónica’s IoT capabilities to create a more unified set of capabilities:

  1. IoT is seen as an enabling opportunity for AI, which can bring added value to Telefónica’s 10,000 IoT customers (with 35 million live IoT SIMs worldwide). Opportunities include provision of intelligence around “things” (for example, products to analyse sensor data) and then the addition of Business Insight services (i.e. analysis of aggregated, anonymised Telefónica data which adds further insight alongside the data coming from IoT devices).
  2. AI is now often a commodity discussion with C-Level prospects and Telefónica wishes to be seen as a strategic partner. Telefónica’s AI of Things proposition offers an execution layer and integration experts with security-by-design capabilities.
  3. Combining capabilities provides sales teams with an end-to-end value proposition, as the addition of AI is often complimentary to cloud transformation projects and the implementation of digital platforms.

There is a growing ecosystem in IoT and data which will generate more opportunities as both IoT solutions and ML/AI solutions mature, although it is not a straightforward decision for Telefónica on how to compete within this ecosystem.

Table of contents

  • Executive Summary
    • How successful has Telefónica been in data monetisation?
    • Learnings from Telefónica’s experience
    • Key success factors
    • Telefónica’s future strategy
  • Introduction
    • The origins of Telefónica Tech AI of Things
    • The formation of Telefónica Tech
  • Vision, mission and strategy
    • Scaling the business
    • Building a product set
    • Learnings from Telefónica Tech AI of Things
  • Organisational strategy
    • Where should the data monetisation team live?
    • Structure of Telefónica Tech AI of Things Team
    • External partnerships
    • Future plans
  • Data portfolio strategy
    • Tools and infrastructure
    • AI Suite
    • Vertical strategy
    • Product development beyond analytics
  • Conclusion and future moves

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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Telecoms data analytics – Where’s the real value?

Why telecoms data analtyics matters

Telecoms data analytics matter because telcos currently face big challenges. As connectivity services are increasingly commoditised, telcos are seeing a steady decline in core revenues. They are at risk of becoming seen as providers of basic utilities, rather than offering innovative services to their customers.

Improved analytics fuels benefits in multiple layers. Initially in the (human) management of operational performance, and then increasingly through using analytics and managed AI to control automation. This can apply to existing and new services.

Future-proofing: why data telecoms analytics are essential to today’s business, 5G and beyond

There is a lot of noise from the telecoms industry around fifth generation (5G) mobile networks and how 5G may provide a renewed source of revenue growth. There is no doubt that 5G will unlock new vertical opportunities for telcos, however, if telcos do not invest in developing additional services, revenues will primarily still come through connectivity. While some may gain a first-mover advantage, over time, 5G will experience the same diminishing returns per user that we have seen with previous generations (see Figure 1). 5G, through connectivity alone, is therefore likely to make only a short-term impact on telco revenue streams.

Figure 1: The effect of increasing 4G subscriber penetration on ARPUs

Source: Data from company filings, analysis by STL Partners

STL Partners has been writing about the commoditisation problem for many years and has seen that operators increasingly accept it as inevitable. Most, in one form or another, are looking beyond connectivity to improve the bottom line. Telcos are adopting two main strategies:

  • build or acquire new revenue streams outside of connectivity
  • cut costs.

The first of these is increasingly popular. Telcos worldwide have accepted the idea that they must develop new capabilities outside of their core service area and find ways to make money from them. These capabilities, and how well they link back to existing connectivity offers, vary widely. For example:

  • Some, realising telcos’ technical expertise, are developing end to end solutions based on new technologies such as multi-access edge computing and 5G. Although technologies such as 5G may not bring sustained growth through connectivity alone, they do offer the potential for telcos to access new areas of the value chain and derive new growth opportunities.
  • Some are developing new services in specific verticals. For example, TELUS in Canada and Telstra in Australia are both building service platforms in the healthcare sector, primarily through acquisitions of health-tech companies.

Unfortunately, due to heavy capex constraints and debt regulation, many telcos face challenges in investing in innovative technologies and only some have shown real success in building new offerings outside of traditional telecoms. All telcos are, however, implementing the second strategy, focussing on cutting costs and driving efficiencies throughout their organisations. Although exploring new verticals and areas of opportunity outside of connectivity is a must to drive sustained growth, in order to defend their territory against the likes of Amazon (who operate on razor thin margins), it is essential that telcos look internally and cut costs across their businesses.

While we see many variants and combinations of these two core strategies across the industry, there is one key element that ties them together. Operators are increasingly taking the view that the key to success – both in building new revenue streams and keeping costs down – is finding ways to make better use of data.

Through their networks and customer interactions, telcos collect a broad array of data. This data comes from both internal (for example data on network performance) and external (for example customer location data and usage data) sources. Telcos can extract and leverage insights from this data more accurately and more quickly through advanced analytics, informing key business decisions, creating efficiencies for internal processes, and unlocking data-enabled new service areas including the facilitation and adoption of technologies like 5G.

Building an advanced analytics capability

High ambitions: data and the AI continuum

When we talk with operators globally about data analytics, a key point of discussion is artificial intelligence (AI). “AI technology” is often cited as a powerful way to cut costs, increase ARPUs, and reduce churn – across an operator’s business. Indeed, at STL Partners we have written extensively about how this could be achieved. However, much of the discussion around AI in the industry is just that – discussion. Many AI solutions are still in their nascent phases and there is a lot more talk than live implementations that deliver measurable business value.

We raise two points to help cut through this hype and understand the real-world value for operators, both in the long and short-term.

  1. All AI is equal, but some AI is more equal than others”. It may seem out of place to paraphrase George Orwell, but the truth is that operators and vendors alike market an increasingly broad set of solutions to customers and the analyst community under the blanket term “AI” (“all AI is equal”). This is often misleading, if not erroneous. “AI” can mean different things depending on who you speak to, ranging from computers following simple instructions or rules set by humans, to more complex fully autonomous computer systems that learn and improve with limited human interaction (“but some AI is more equal than others”). These examples differ strongly – but both fit within a generic definition of “artificial intelligence”.

Agnostic of what you include in your definition of AI, there are clearly tiers of AI solution which are based on the algorithm’s complexity, its ability to implement decisions independently (in terms of rights/permissions and integration with automated processes), and the level of human interaction or guidance necessary. At STL Partners, we have written previously about how we see advanced data analytics and AI as a continuum, with stepping stones on a journey towards the fully autonomous telco (Figure 2) The detailed explanation and formulation of this continuum is more thoroughly explained in a previous instalment of our AI research series.

  1. Most live and scaled deployments fall under our definition of rules based automation. Operators speaking to us about AI tend to want focus on innovative AI use cases that fall in the right-hand side of Figure 2. Examples include automated and self-improving chatbots that can solve any customer query and translate a complaint into a sale, or self-healing networks that fix themselves with no need for engineers to intervene. It’s true that these use cases will deliver high-value for telcos and help to answer the big questions set out above. However, should telcos be prioritising these if their data systems cannot yet tell them which customers are having a poor experience, or give them a full, real-time view of network performance?

Where are operators compared to their AI aspirations

Source: STL Partners

In terms of real progress, we have seen only a handful of leading Tier 1 operators deploying telecoms data analytics solutions that truly fit under the ML/AI banner within our framework. Most operators are still much earlier on in the journey towards automation. Even those pioneer operators have deployed only in specific geographical regions and in specific parts of their business. They face problems in deploying more complex solutions at scale and deriving measurable value.

At STL Partners, we believe that too much focus on a poorly defined end-goal risks stalling necessary work that must be done up-front. Operators should strive for and research innovative uses of data, but we believe the focus in the short-term, for Tier 1 and 2/3 telcos alike, should be on laying the necessary groundwork to ensure that data is accessible and clean, with a clear governance structure, as well as building the analytics capabilities necessary to make full use of it.

Laying the groundwork: stepping stones toward data analytics

There are three key components to building even the most basic data analytics capabilities:

  1. Clean, unified data
  2. The skills and tools to process and analyse it
  3. The ambition and drive to do so – data-centricity

This may seem straightforward but telcos globally (including even the most advanced operators) have faced challenges in meeting these requirements. For example, 77% of the operators we have spoken to stated that data collection and management was a key issue for them in implementing an analytics strategy. Furthermore, over a third of the operators we spoke with mentioned a lack of both internal and external skills with regards to advanced analytics (see Figure 3).

Figure 3: Top 4 issues faced by telcos looking to make use of data

Source: STL Partners research programme, October 2018

In order to overcome the issues listed in Figure 3, and to build future-proof telecoms data analytics capabilities, telcos must develop the three components mentioned above. Without doing this in the short-term, operators will lack the underlying platform from which to springboard into developing innovative solutions that leverage AI or ML.

Contents:

  • Executive Summary
  • Future-proofing: what to do?
  • Building an advanced telecoms data analytics capability
  • High ambitions: data and the AI continuum
  • Laying the groundwork: stepping stones toward data analytics
  • In practice: Assessing real analytics use cases
  • Improve business as usual
  • Monetise user data
  • Enable next-generation services
  • Conclusions
  • Key recommendations
  • Conclusion

Figures:

  • Figure 1: The effect of increasing 4G subscriber penetration on ARPUs
  • Figure 2: The journey to AI and telco automation
  • Figure 3: Top 4 issues faced by telcos looking to make use of data
  • Figure 4: Telefónica’s data management structure across multiple opcos
  • Figure 5: What is your biggest challenge in leveraging analytics?
  • Figure 6: The opportunity areas for telcos in advanced analytics
  • Figure 7: A comparison of Iliad against the leading Italian operators
  • Figure 8: A graphical representation of KPN’s Data Services Hub
  • Figure 9: Where operators are compared to their AI aspirations