AI & automation for telcos: Mapping the financial value

This is an update to STL Partners report A3 for telcos: Mapping the financial value, published in May 2020, which estimated the financial value of automation, AI and analytics (A3) through bottom up analysis of potential capex/opex savings or revenue uplift from integrating A3 into 150+ processes across a telco’s core operations.

The value is measured on an annual basis in dollar terms and as a proportion of total revenue for a “standard telecoms operator”. Access to the full methodology and definition of a standard telco is available in the report Appendix.

We categorise the value of automation, AI and analytics (A3) in telecoms across operational area, as well as type and purpose of A3 technology. Our graphic below summarises the value of A3 across the following six types of technology:

  1. Making sense of complex data: Analytics and machine learning used to understand large, mostly structured data sets, looking for patterns to diagnose problems and predict/prescribe resolutions.
  2. Automating processes: Intelligent automation and RPA to enable decision making, orchestration and task completion within telco processes.
  3. Personalising customer interactions: Analytics and machine learning used to understand customer data, create segmentation, identify triggers and prescribe actions to be taken.
  4. Support business planning: Analytics and machine learning used in forecasting and optimisation exercises.
  5. Augmenting human capabilities: AI solutions such as natural language processing and text analytics used to understand human intent or sentiment, to support interactions between customers or employees and telco systems.
  6. Frontier AI solutions: A number of individual AI solutions which have particular, specialist uses within a telco.

For further detail on this categorisation methodology, see STL Partners report The telco A3 application map

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What’s new in 2022

The colouring of the use case categories in the graphic below remains largely unchanged from May 2020. Some uses of A3 were reasonably mature in that timeframe and already rolled out in a typical telco, so their value was already well understood.

We estimate that the most valuable use case categories, primarily in networks and operations, deliver over $50 millions in annual benefits – and sometimes up to hundreds of millions. Throughout this report we express the value in dollar terms and as a percentage of savings within each domain. This is because while $50 million is clearly a significant sum, it accounts for just 0.33% of total revenues for our standard operator, so showing values for unique use case categories as a proportion of total revenues undermines the potential value A3 can add to individual teams, and in turn contribute to significant aggregate value across an operator.

Overview of the financial value of A3

financual-value-A3

Source: STL Partners, Charlotte Patrick Consult

In our May 2020 research, many of the more sophisticated uses of A3 were understood in theory but yet to be implemented. Researching these various newer uses cases throughout 2021 has revealed that many are now, at least partly, rolled out (although some are still waiting for cleaner data or more orchestration capabilities).

However, there were a few new case studies with financial benefits that necessitated more than small changes to the 2020 financial value calculations. Summarising the changes illustrated in the graphic above:

  • The most noticeable change in uptake for A3 was in the BSS domain. Vendors and telcos were not discussing much beyond RPA and basic analytics in 2020, but there are now a whole range of potential uses for ML (typically in the box labelled “Revenue management” in the graphic above). The question of how much additional financial value to assign to this is interesting – some of the A3 will ensure that the rating and charging systems can cope with the additional volume and complexity around 5G and IoT billing, so an allocation of revenue uplift has been assigned. However, this revenue benefit only accounts for around 6% of the additional $83 million in value from A3 in networks and operations estimated in this update.
  • We have added partner management as a new use case category, within operations. This is to allow A3 value to be added as telcos work with more partners and in new ecosystems, and accounts for 6% of additional value in networks and operations in this update.
  • An increase in the assumed value of A3 within marketing programs, owing to the addition of ML to improve the design of new offers.
  • The value of a previous use case category labelled “Troubleshooting” has been subsumed into “Unassisted channels”, as telcos find it difficult to implement troubleshooting tools for customers.
  • Some increase in financial benefit around customer chatbots and field services, due to new case studies showing financial value.

Our report includes a section for each of the first three columns of the graphic above (Networks and operations, customer channels, marketing and sales). The final column (other functions) doesn’t currently have financial calculations underpinning it as values are thought to be insubstantial in comparison to the first three columns.

Table of contents

  • Executive summary
  • Overview of the financial value of automation, AI and analytics (A3)
  • Financial value by business unit
    • BSS, OSS and networks
    • Customer channels
    • Sales and marketing
  • Appendix
    • Methodology for Calculating Financial Value
    • Augmented Analytics Capabilities

Related Research

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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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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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