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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Telco data monetisation: What is it worth?

Data revenue opportunities are variable

Monetisation of telco data has been an area of activity for the last six years. However, telcos’ interest levels have varied over time due to the complexity of delivering and selling such a diverse range of products, as well as highly variable revenue opportunities depending on the vertical. Telcos’ appetite to pursue data monetisation has also been heavily impacted by the fortunes of other new telco products, in particular IoT, owing to the link between many data/analytics products and IoT solutions.

This report assesses the opportunity for telcos to monetise their data and provide associated data analytics products in two parts:

  1. First, we look at the range of products and services a telco needs to create in order to deliver financial value.
  2. Then, we explore the main use cases and actual financial value of telco data analytics products across 12 verticals, plus horizontal solutions that apply to multiple verticals.

Telco data monetisation: Calculation methodology

The methodology used to model the financial value of telco data analytics is outlined in the figure below.

  • The starting point for this analysis is 210 data or data analytics use cases, spread across 12 verticals and the horizontal solutions applicable to multiple verticals.
  • We then assess how difficult it is for a telco to address each use case, based on pre-requisite supporting platforms and solutions, regulatory constraints, etc. (shown in red). This evaluation enables us to assess how likely telcos are to develop products for each use case.
  • Thirdly, we assess which types of telco are able to develop the use case (in yellow). For example, telcos in a market with particularly restrictive regulation around use of personal data are simply not able to create certain products.
  • Finally, it is necessary to understand whether the data/analytics products created for a use case can be offered as an independent, standalone product, or more likely to be provided as a bolt-on service to another, pre-existing solution. This question is primarily pertinent in the IoT space where basic data/analytics are likely to be included in the price of the IoT service.
    • For products that we expect to be sold independently, we calculate the potential revenue based on estimated pricing for the type of data product, where known, and likely volumes that a telco will sell in a year.
    • For data analytics products closely linked to IoT, we attach no monetary value.

Calculation methodology for the feasibility and value of telco data monetisation use cases

Rationale behind data monetisation potential

Source: STL Partners, Charlotte Patrick Consult

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Viewing the data

Underlying the analysis in this report is a database tool including a detailed assessment of each of the 210 data monetisation use cases we have identified, with numerical analysis and charting capabilities. We know many of our readers will be interested to explore the detailed data, and so have made it available for download on the website in the form of an Excel spreadsheet.

Full use case database and analysis available on our website

Source: STL Partners

Table of Contents

  • Executive Summary
  • Introduction
    • Calculation methodology
  • What is this market worth to telcos?
  • Creating products for data monetisation
    • Telco products for the ecosystem
    • Data and analytics for IoT
    • Use of location in data monetisation
  • Maximising value in different verticals
    • Advertising and market research
    • Agriculture
    • Finance
    • Government
    • Insurance
    • Healthcare
    • Manufacturing
    • Real estate and construction
    • Retail
    • Telecom, media and technology
    • Transportation
    • Utilities
    • Horizontal solutions for all verticals
  • Conclusion and recommendations
    • How to pick a winning project
  • Index

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Telcos and enterprise verticals: 5G is not the only opportunity

Introduction

This report outlines key challenges within selected industry verticals, and how telcos can help resolve them with three emerging networking technologies – 5G, IoT and edge computing.

This research builds on many previous reports:

Enterprise services evolve alongside communications and information technologies

The early days of 2G/3G

  • Basic M2M connectivity
  • Early versions of private networks, bypassing the internet for sensitive data transfer

Improving mobility and capacity with 4G and fibre

  • Better connectivity drives demand for video-conferencing and more sophisticated UCaaS
  • Mobile and fixed data connectivity is powerful enough to enable greater enterprise mobility and support the shift towards cloud-based services
  • Different verticals increasingly require bespoke solutions for unique needs – which are easier to deliver through the cloud

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Greater flexibility and frictionless experiences with 5G, IoT and edge computing

  • Increasing complexity of connectivity, IoT, cloud and IT ecosystems are driving demand for flexible yet seamless solutions:
    • Mobile connectivity across geographies without onerous roaming charges
    • Seamless mobile connectivity across multiple networks and technologies, especially in remote areas
    • Frictionless remote set-up of IoT devices shipped directly from manufacturer to live environment
    • Ability to migrate to new technologies seamlessly (e.g. public sector move from TETRA to cellular)
    • All of the above controlled and monitored on user-friendly, cloud-based dashboards
  • The shift from product to service-based business models means a growing number of enterprises want to embed connectivity into their offer to customers
    • i.e. demand for greater control over wholesale connectivity solutions
    • remote maintenance, asset-tracking, etc.

5G applications will arrive at different times…

evolution of 5G technology eMBB, URLLC, private 5G, massiv IoT

Source: STL Partners

…in the meantime, other technologies can help address enterprise needs

The interdependencies between 5G, IoT and edge computing

Source: STL Partners

The problem with 5G for enterprises

  • Most enterprises are not looking at 5G in isolation, but as one of many technologies that will help resolve pain points around efficiency and innovation. The Internet for Things (I4T) and edge computing are two other key technologies that many enterprises need, and which telcos could potentially provide
  • In the long term, STL Partners does not expect 5G connectivity on its own to deliver growth for telcos. So to grow enterprise revenues, telcos should also develop I4T and edge computing solutions
  • But developing expertise in 5G, I4T and edge computing will be expensive and complex to manage
  • Therefore, telcos should start by targeting their investments to meet specific enterprise pain points

This report helps telcos assess how to target their investments by highlighting key pain points in a selection of industry verticals, and how relevant 5G, I4T and edge computing are for solving them.

Sectors with strong demand for all three technologies hold the highest potential value, but this will be difficult for telcos to capture owing to strong competition in I4T and edge computing from other technology companies.

This report covers a selection of verticals that STL Partners has developed knowledge of through research and consulting activities: manufacturing, construction, utilities, agriculture, transport, automotive, healthcare, and sports, media and entertainment. 

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The IoT money problem: 3 options

Introduction

IoT has been a hot topic since 2010, but despite countless IoT initiatives being launched questions remain about how to monetise the opportunity.

This report presents:

  • A top-level summary of our thinking on IoT so far
  • Examples of 12 IoT verticals and over 40 use-cases
  • Case-studies of four telcos’ experimentation in IoT
  • Three potential roles that could help telcos monetise IoT

Overview

In the early days of the IoT (about five years ago) cellular connectivity was expected to play a major role – Ericsson predicted 50 billion connected devices by 2020, 20 billion of which would be cellular.

However, many IoT products have evolved without cellular connectivity, and lower cost connectivity solutions – such as SIGFOX – have had a considerable impact on the market.

Ericsson now forecasts that, although the headline number of around 50 billion connected devices by 2020 will remain the same, just over 1 billion will use cellular.

Despite these changes IoT is still a significant opportunity for telcos, but they need to change their IoT strategy to become more than connectivity providers as the value of this role in the ecosystem is likely to be modest.

Mapping the IoT ecosystem

The term IoT describes a diverse ecosystem covering a wide range of different connectivity types and use-cases. Therefore, to understand IoT better it is necessary to break it down into horizontal layers and vertical segments (see Figure 1).

Figure 1: A simplified map of the IoT ecosystem

Source: STL Partners

We are seeking input from our clients to shape our IoT research and have put together a short survey asking for your thoughts on:

  • What role telcos can play in the IoT ecosystem
  • Which verticals telcos can be successful in
  • What challenges telcos facing in IoT
  • How can STL support telcos developing their IoT strategy

To thank you for your time we will send you a summary of the survey results at the end of June 2017.

…to access the other 28 pages of this 31 page Telco 2.0 Report, including…

  • Introduction
  • Mapping the IoT ecosystem
  • Overview
  • Mapping the IoT ecosystem
  • IoT: A complicated and evolving market
  • Telcos are moving beyond connectivity
  • And use cases are increasing in complexity
  • IoT verticals – different end-customers with different needs
  • 12 examples of IoT verticals
  • What connectivity should telcos provide?
  • Four examples of IoT experimentation
  • Case study 1: AT&T: Vertically-integrated ecosystem architect
  • Case study 2: Vodafone: a ‘connectivity plus’ approach
  • Case study 3: SK Telecom: ecnouraging innovation through interoperability
  • Case study 4: Deutsche Telekom AG: the open platform integrator
  • Three potential monetisation strategies
  • Ecosystem orchestrator
  • Vertical champion
  • Trust broker
  • Conclusions

…and the following figures…   

  • Figure 1: A simplified map of the IoT ecosystem
  • Figure 2: Telcos moving beyond connectivity
  • Figure 3: IoT use cases are increasing in complexity
  • Figure 4: Use cases in manufacturing
  • Figure 5: Use cases in transportation
  • Figure 6: Use cases in utilities
  • Figure 7: Use cases in surveillance
  • Figure 8: Use cases in smart cities
  • Figure 9: Use cases in health & care
  • Figure 10: Use cases in agriculture
  • Figure 11: Use cases in extractive industries
  • Figure 12: Use cases in retail
  • Figure 13: Use cases in finance
  • Figure 14: Use cases in logistics
  • Figure 15: Use cases in smart home / building
  • Figure 16: Connectivity complexity profile for pay-as-you-drive insurance and rental services
  • Figure 17: Telco opportunity for deep learning pay-as-you-drive insurance and rental services