1. Big Data and Competition Policy Market power,
Personalised pricing and advertising
CERRE Executive Seminar, Brussels, 16 February 2017
Prof. Marc Bourreau, CERRE and Telecom ParisTech
Prof. Alexandre de Streel, CERRE and University of Namur
Dr. Inge Graef, Tilburg University and KU Leuven

2. 1. Big Data Value chain 2. Regulatory Landscape
Outline of the Report
1. Big Data Value chain
2. Regulatory Landscape
3. Data and Market Power
4. Personalised Prices
5. Target Advertising

3. 1. Big Data Value Chain 3 main parts Data collection Data storage
Directly from users (often against ‘free’ services) or from machines
Indirectly: data brokers and data marketplaces
Data storage
Large data centres
But fixed costs can be made variable with cloud computing
Data analytics
Applications and algorithms (more and more self-learning): 4 V’s: volume, variety, velocity and value
Need data, skills, capital
Relationship between data-driven innovation and productivity

4. Big Data Value Chain Value chain evolves and is shaped
Mainly by the technological progress
Progress in processing and computing technologies made 4 V’s possible
Impact of AI remains to be better understood
And by customers preferences: understanding, behaviour and trust
But also by rules on data collection and use (the ‘data regulations’)

5. 2. EU rules on data collection and use
Typologies of data
Personal/non-personal, User-generated/machine-generated, Raw data/processed data
Rules applicable to all data
Intellectual property and trade secrets
Competition rules
Specific rules mandating data sharing, esp. for publicly-owned data
Consumer protection
Rules applicable to personal data
General Data Protection Regulation
Telecom specific data protection rules, which are stricter
Human right of privacy
Those rules can increase or lower the barriers for data collection or use

6. 3. Data and Market Power Analytical framework 3 principles
Data is one input - important but not unique - to develop applications and algorithms
Big data value chain - and the broader data ecosystem - exhibit many network effects
‘Holistic’ competition analysis
Each big data application/algorithm is different
Case-by-case analysis
2 questions

7. Data and Market Power Data availability: Costs of data collection
Direct collection
Data is non-rival and may be everywhere, but technical, legal, contractual barriers may increase collection costs
Data can be collected against direct payment or against ‘free’ services
Data can be collected for their own sake or as by-product
Indirect collection
Antitrust assessment
Merger cases: use of data for online advertising
Concentrations of user data are not anti-competitive
Abuse cases: data cross-subsidisation between monopolised and competitive services
Customers lists are not replicable

8. Data and Market Power Data value for the analysis Volume of data
What is the relationship between data quantity and application/algorithm quality?
Variety of data
What is the relationship between data variety and application/algorithm quality?
Depreciation value of data
Transient and less transient data
For less transient data, how can they be ‘capitalised’ in application development or improvement?
Impact of Artificial Intelligence on the experience curve of algorithmic development

9. Data and Market Power
Relationship between data collection and data analysis
Feedback loops may decrease costs of collection
Existence of the feedback loop ?
Effects of the feedback loop ?

10. Data and Market Power Governance framework for competition agencies
Improve understanding of the big data value chain
Structured dialogue and possibly sector enquiry (which should not create an unreasonable burden)
Strengthen expertise in computer and data science
Close cooperation with agencies in charge of consumer protection, data protection and intellectual property
Understand common problems, understand the effects of the ‘data regulations’
Achieve consistency in decisions
But no legal fusion as the different legal instruments are complement and not substitute

11. 4. Personalised Prices
Personalised prices: individual prices (first-degree price discrimination) or group pricing (third-degree price discrimination) with small targeted groups
Presumption that more data on consumers’ characteristics or behaviour should make personalised prices more prevalent
Technically possible (according to OFT report)
But little empirical evidence (CNIL-DGCCRF/OFT reports)
Fear of consumer backlash and reputation concerns
Indirect ways to personalise prices: personalised discounts, steering

12. Impact on Firms and Consumers
Monopoly: a priori beneficial for the firm (but not necessarily true with repeated interactions)
Less clear under imperfect competition: the introduction of personalized prices by all firms can intensify competition
Asymmetric access to data can strengthen competitive advantage: the firm with access to data introduces personalized prices, to the detriment of its rivals
Impact on consumer surplus:
Under monopoly (group pricing): ambiguous effects
Appropriation vs. market expansion
Under imperfect competition: ambiguous too
Personalized pricing can reduce consumers’ trust in online markets, in particular if realized in a non-transparent way

13. Recommendations No rationale for banning personalised prices per se
Ambiguous effects on consumer surplus and welfare
Reputation concerns for large players
Transparency to ensure consumer trust in online markets
Risk that firms engaging in personalised pricing in non-transparent ways could undermine consumers’ trust in online markets
Monitoring of online prices (rather than audit of price algorithms)
Could be handled by consumer protection agencies upon complaints

14. 5. Targeted advertising
Advertising: a keystone for many business models in digital markets
Targeted advertising: when firms place ads that target a specific audience based on their estimated personal characteristics/interests
Targeting for display advertising: widespread now and quickly growing
Different levels of targeting
First-party targeting (from publisher)
Third-party targeting (ad networks, ad exchange)
Automated, real-time third party targeting (RTB…)

15. Impact on firms and consumers
For advertisers: presumption that targeted advertising is more effective than regular advertising, but not always true (details matter)
For publishers: potential trade-off between effectiveness and exposure
Impact on consumers:
For consumers, trade-off between receiving more relevant ads that match their interests and nuisance from intrusive ads
Potential concern with steering: targeted ads as a way to price discriminate

16. Recommendations Online advertising: a dynamic and innovative market
Firms in the online advertising ecosystem compete for the best algorithms, the best advertising technologies, to improve advertising effectiveness and limit consumer reactance
Complexity of the ecosystem may call for some monitoring
But anything beyond monitoring is not warranted as long as no evidence of abuse of market power
This ‘light’ approach works as long as no dominant player can block innovation by rivals (e.g., entrants)