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This report presents data monetisation options for selected verticals, beyond traditional development in pure online markets.
The first part of the report provides an overview of the big data market and its impacts in terms of data monetisation opportunities open to all verticals.
The second part presents current initiatives for data monetisation around upsell and cross-selling, direct advertising/marketing, value-added services (servicisation), intermediation and data resale for four key verticals: finance, retail, telecom and connected objects.
The report provides an assessment of the major real opportunities in the selected verticals and their articulation with aggregators and platforms coming from the online world.

List of players reviewed
• Amazon
• Apple
• Axa
• Barclays
• Cardlytics
• Citibank
• Discovery
• emozia
• Facebook
• Google
• Intersec
• Intuit
• JP Morgan Chase
• Macy's
• MasterCard
• Mint
• Nordstrom
• NTT DOCOMO
• Open Bank Project
• Oracle
• Orange
• parks & honey
• Plaid
• RunKeeper
• SFR
• shopkick
• SingTel
• SundaySky
• Telefónica/O2
• Tesco
• Verizon
• Weve
• Withings

Slideshow contents

Data monetisation options for verticals
• Big data - a disruptive concept for data monetisation
• Big data technologies and market structure
• Big data market size

Opportunities for verticals
• Opportunities for verticals
• Differences between verticals
• Retail
• Finance
• Telecom
• Connected objects

Outlook
• Major opportunities for the four verticals
• Battle with aggregators

Table Of Contents

Data monetization - Opportunities beyond OTT: finance, retail, telecom and connected objects
1. Executive Summary

2. Methodology and definitions

3. Data monetisation options for verticals
3.1. Introduction to big data
3.1.1. Market description
3.1.2. Market structure
3.1.3. Market size
3.2. Opportunities for verticals
3.2.1. Major opportunities of big data for verticals
3.2.2. Data monetisation and verticals
3.2.3. Differences between verticals
3.3. The battle with aggregators

4. Data monetisation and finance
4.1. Type of data gathered
4.2. Privacy policies
4.3. Main opportunities

5. Data monetisation and retail
5.1. Type of data gathered in retail
5.2. Privacy policies
5.3. Main opportunities
5.3.1. Geo-fencing
5.3.2. Loyalty card programmes and customer insights
5.3.3. In-store tracking
5.3.4. Tracking buying emotions

6. Data monetisation and telcos
6.1. Type of data gathered
6.2. Privacy policies
6.3. Main opportunities

7. Data monetisation and smart products
7.1. Introduction
7.2. Types of data collected
7.3. Privacy policies
7.4. Opportunities
7.4.1. Development of new services associated with products (servicisation)
7.4.2. Insights and Aggregated data sales

Tables
Table 1: Type of data used by vertical
Table 2: Main potential uses of big data by vertical players, by type of activity
Table 3: Key options for data monetisation
Table 4: Summary view of major opportunities for the four verticals
Table 5: Data characteristics per vertical
Table 6: Key options for data monetisation in finance
Table 7: Type of data gathered in the retail segment
Table 8: Retailer policies for data collection
Table 9: Key options for data monetisation
Table 10: Key options for data monetisation for telcos
Table 11: Price comparison between non-connected and connected smart home systems
Table 12: Key options for data monetisation

Figures
Figure 1: Respective positioning of verticals regarding data monetisation
Figure 2: Variety of data sources
Figure 3: Technologies used to derive value from big data
Figure 4: Big data value chain
Figure 5: Big data landscape
Figure 6: Main use of the RTD platform provided by Oracle to its customers
Figure 7: Worldwide big data revenue forecasts, 2012-2015
Figure 8: State of big data investments
Figure 9: Big data and analytics software market, in 2016
Figure 10: Online advertising revenues, worldwide and regional, 2010-2018
Figure 11: SundaySky overview of bill explanation in video, for ATandT
Figure 12: Adoption of big data per vertical
Figure 13: Respective positioning of verticals regarding data monetisation
Figure 14: Retention rate of Apple is extremely high
Figure 15: 71% of financial companies realise competitive edge through big data
Figure 16: The types of personal information collected by MasterCard
Figure 17: Public trust levels in public services, banks, e-commerce and social networks with personal data, 2009, 2011 and 2013
Figure 18: How the personal data collected is used by MasterCard
Figure 19: The opt-out option from data anonymisation and analysis in MasterCard's privacy policy
Figure 20: Intuit privacy policy explaining that information is shared but not identifiable
Figure 21: The higher-end tariffs include more features, some of which rely on big data analysis
Figure 22: Bank of America using Cardlytics platform to provide ads within online statements
Figure 23: Mint's all-in-one interface for users to follow their finances
Figure 24: Mint personalised offers on finding savings
Figure 25: The Open Bank Project
Figure 26: Plaid Connect API
Figure 27: Barclays leaflet to inform changes to allow Barclays to aggregate and share data
Figure 28: The Citi Wallet
Figure 29: Credit card comparison with cashback rewards
Figure 30: Data collected by retailers in-store
Figure 31: iBeacon trialled by Tesco
Figure 32: shopkick application
Figure 33: Evolution in data use by Tesco
Figure 34: Nordstrom dashboard
Figure 35: Analysing emotional state before buying energy drinks
Figure 36: Types of data gathered by telcos
Figure 37: "How we use your information"? - privacy policy of O2
Figure 38: O2 'Bolt Ons' allow for additional sales on top of standard tariffs
Figure 39: Charge to Mobile API by BlueVia
Figure 40: Direct-to-bill on Facebook: some operators offer easy two-click process
Figure 41: The business cycle of Weve, integrating mobile wallet and financing
Figure 42: How PrecisionID works
Figure 43: i-concier service by NTT DOCOMO
Figure 44: Screenshot of a Smart Steps insight result
Figure 45: Different sensors on the human body
Figure 46: Type of data collected at Withings
Figure 47: Use of personal data at Withings
Figure 48: Multiple data measurement monitoring
Figure 49: Version comparison
Figure 50: Coaching service description
Figure 51: Partnership solutions for vertical companies
Figure 52: Home by SFR solution
Figure 53: Data resale business model description
Figure 54: Benefits and rewards description
Figure 55: Withings Pulse description

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