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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Fighting the fakes: How telcos can help

Internet platforms need a frictionless solution to fight the fakes

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

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

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

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

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

In come telcos

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

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

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Using big data to combat fraud

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

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

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

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

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

Adapting telco authentication for Amazon, Uber and Airbnb

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

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

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

Table of contents

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

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A3 for telcos: Mapping the financial value

What is analytics, AI and automation worth to telecoms operators?

This report is the second in a two-part series mapping the process and assessing the financial value of automation, analytics and artificial intelligence (AI) in telecoms. In the first report, The value of analytics, automation and AI for telcos – Part 1: The telco A3 application map, we outlined which type of technology was best suited to which processes across a telco’s operations.

In this report, we assess the financial value of each of the operational areas, in dollar terms, for an average telco. Based on our assessment of operator financials and operational KPIs, the figure below outlines our assumptions on the characteristics of an “average” telco used as the basis for our financial modelling. The characteristics of this telco are as shown below, with a slight skew towards developed market operator characteristics since this is currently where most industry proof points used in our modelling have been implemented.

The characteristics of an average telco

characteristics of an average telco

Source: STL Partners, Charlotte Patrick Consult

The first report in the series analysed how each A3 technology could be applied similarly across different functional units of a telecoms operator, e.g. machine learning or automation each have similar processes in network management, channel management and sales and marketing.

Evaluating AI and automation use cases in four domains

To measure financial impact, this report returns to a traditional breakdown of value by functional unit within the telco, breaking down into four key areas:

  1. Network operations: Network deployment, management and maintenance, and revenue management
  2. Fraud: Including services, online, and internal fraud risks
  3. Customer care: Including all assisted and unassisted channels
  4. Marketing and sales: Understanding customers, managing products, marketing programs, lead management and sales processes.

Through an assessment of nearly 150 individual process areas across a telecoms operator’s core operations, we estimate that A3 can deliver the average telco more than $1 billion dollars in value per year, through a combination of revenue uplift and opex and capex savings, equivalent to 7% of total annual revenues.

As illustrated below, core network operations management accounts for by far the greatest proportion of the value.

The relative value of automation, AI and analytics across telco operations

The relative value of AI, automation and analytics across telco operations

Source: STL Partners, Charlotte Patrick Consult

This likely still underrepresents the total, long term potential value of A3 to telcos, since this first iteration does not model the value of A3 processes in areas less unique to telecoms, including supply chain, finance, IT and HR. No doubt that even within the core area of operations, there are potential process areas that have yet to be discovered or proven, and which we have overlooked in our initial attempt to model the value of A3 to telcos. Meanwhile, this is focused purely on telco’s internal operations so also excludes any potential revenue uplift from new A3-enabled services, such as data monetisation or development of AI-as-a-service type solutions.

That said, operators cannot implement all of these processes at once. The enormous challenge of restructuring processes to be more automated and data-centric, putting in place the data management and analytics capabilities, training employees and acquiring new skills, among many others, means that while many leading telcos are well on their way to capturing this value in some areas, very few – if any – have implemented A3 across all process areas. As a benchmark, Telefónica is an industry leader in leveraging automation and AI to improve operational efficiency, and in 2019 it reported total operational savings of €429mn across the entire group. While this is primarily focused on customer facing channels, so likely excludes the value of A3 in many network operations processes, for instance energy efficiency which is delivering significant value to Telefónica and others, it suggests there remains lots of value still to capture.

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Methodology

The financial modelling for the value of A3 was done through an individual assessment of each of the 150+ process areas to understand the main activities within that area of operations, and how automation, analytics and/or machine learning and other AI technologies could be used within those activities. From there, we assess the value of integrating these technologies to existing operational functions to make them more efficient and effective. This means that we do not attribute any additional value to telcos from implementing new technologies that include A3 as a core element of their functionality, e.g. a multi-domain service orchestrator, implemented as part of software-defined networking.

Our bottom up assessment of each process is also validated through real-world proof points from operators or vendors. This means that more speculative areas of A3 application in operators are calculated to offer relatively limited value. As more proof points emerge, we will incorporate them into future iterations.

Table of contents

  • Executive Summary
    • Where is the largest financial benefit from A3?
    • What should telcos prioritise in the short term?
    • How long will it take for telcos to realise this value?
    • What next?
  • Introduction
    • Methodology
  • Breaking down the value of A3 by operational area
    • Network, OSS and BSS
    • Fraud management
    • Care and commercial channels
    • Marketing and sales
  • Conclusions and recommendations

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