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

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Consumer IoT: How telcos can create new value

Introduction: Trust is a must for consumer IoT – but is consumer IoT a must for telcos?

Lack of trust is a major barrier to mass-market consumer IoT adoption

There was an expectation two to three years ago that take-up of consumer Internet of Things (IoT) services was set to accelerate, and that we would soon witness the success of mass market consumer IoT offers in areas such as energy management (linked to roll-outs of smart metering), home automation and security, and health and wellness applications (linked to wearables such as smart watches, fitness trackers and medical condition sensors). It was also widely expected that telcos would play a leading role in this market.

Although growth has occurred in these product areas, it has generally been below expectations. Everett M. Rogers’ diffusion of innovations theory shows how the different stages of public acceptance a new product goes through, with successive groups of consumers adopting the new technology (shown in blue), so its market share (yellow) eventually reaches saturation level. Looking at this theory, STL believes that consumer IoT is still in the “early adopter” stage.

Figure 1: Rogers’ diffusion of innovations theory

Source: Rogers, E. (1962) Diffusion of innovations, image from Wikipedia

In addition to this, telcos have tended to play a peripheral part in the market thus far, limited largely to providing the wireless and broadband connectivity supporting third-party products developed by players focused on adjacent vertical markets. Already the focus of telcos’ IoT strategies seems to have been redirected to enterprise and industrial IoT applications, along with the rapidly maturing connected car and smart cities markets, judging from the wave of new product and partnership announcements in these areas at recent trade shows, such as this year’s Mobile World Congress (MWC). Despite this, we believe that consumer IoT could still represent a large addressable market for telcos, based on data presented in chapter 3.

There are many reasons for the levelling of the expected consumer IoT growth curve, some of which we will explore in this report. In terms of definitions, we are limiting the term ‘consumer IoT’ to ‘consumer-centric’ applications and services, whether these are deployed primarily in the home (such as home automation and security) or on the person (e.g. wearables, and health and wellness). We will not directly discuss connected car / autonomous vehicle and smart cities applications, even though they relate to consumer services and experiences, as the dynamics of these services and their technological challenges are quite distinct. In addition, we will only tangentially discuss healthcare IoT, as it is far from clear what sort of ‘consumer’ business model will be established in this sector (as opposed to a public service model); although it is likely that remote health and social care will play an increasingly central role in a prospective ‘second wave’ of consumer IoT services, based on trustworthy processing of intimate personal data to enable really useful services.

In addition, we make a distinction between ‘connected’ devices and homes, on the one hand, and ‘smart’ devices / homes and IoT services, on the other. A home is not smart, nor an IoT service present, until the connected devices or ‘things’ involved, and the data they generate, are integrated as part of an app that the user controls. As shown in Figure 2, in the existing IoT business model, this involves delivery of the data from multiple devices and sensors to a cloud-based service, enabling collection, aggregation and analysis of the data, and remote and automated performance of actions on those devices based on the analysis and on the user’s preferences.

Contents:

  • Executive Summary: Trust is king
  • Introduction: Trust is a must for consumer IoT – but is consumer IoT a must for telcos?
  • Lack of trust is a major barrier to mass-market consumer IoT adoption
  • Building trust with customers must be at the forefront of telcos’ consumer IoT offer and brand
  • Consumer IoT 1.0: opportunities and threats for telcos; telco strengths and weaknesses
  • Opportunities: The addressable market for telcos is potentially huge
  • Threats: do consumers buy it?
  • Established telco strengths can help offset the risks
  • Weaknesses: IoT exemplifies the challenges of digital innovation in general
  • Conclusion: consumer IoT is a huge challenge but also a huge opportunity that plays into telcos’ strengths
  • Deutsche Telekom’s consumer IoT platform and services
  • Deutsche Telekom and the Qivicon platform
  • Efforts to address the data security and privacy issues of consumer IoT 1.0
  • Avast: telcos can play a role as part of a cross-industry approach
  • Orange: transparency over use of data is key
  • Atomite: consumer consent and rewards for sharing data with third parties
  • Telefónica’s AURA: cognitive intelligence but an immature business model
  • Consumer IoT 2.0: A move to a (data) sharing economy
  • GDPR: A change in the rules that looks set to change business models
  • Databox: “privacy-aware data analytics platform”
  • IoT and the personal data economy: putting ‘me’ at the centre of my internet of things
  • Conclusion: Telcos need to be in the consumer IoT 1.0 game to win in consumer IoT 2.0
  • A massive potential market, with a large slice of the pie available to telcos…
  • … but do the risks outweigh the potential benefits?
  • Telcos need to play the consumer IoT 1.0 game to reach consumer IoT 2.0

Figures:

  • Figure 1: Rogers’ diffusion of innovations theory
  • Figure 2: Consumer IoT 1.0
  • Figure 3: Consumer concerns about connected devices
  • Figure 4: Strengths, weaknesses, opportunities and threats for telcos in consumer IoT
  • Figure 5: Connected home installed base and penetration EU and North America, 2013–19
  • Figure 6: Companies most trusted with personal data
  • Figure 7: The Qivicon consumer IoT platform
  • Figure 8: Orange ‘Trust Badge’ – what personal and usage data is collected, and why
  • Figure 9: Key functionality of the Meeco personal data portal