Dynamic, personalised pricing and the UCPD

1. Dynamic, personalised pricing and the UCPD Phil Evans, Senior Consultant May 28th 2013 Brussels

2. What are we talking about? • Dynamic pricing • charging consumers different prices for the same product or service • depending on particular characteristics of the transaction or the consumers. • Consumer characteristics? • Imagine a Souk or market – sellers reading people – price set by haggling • Old style – location, age, previous purchases • New style – evolving into personalised pricing - any factor with data attached • Location tracking through IP much more accurate; mass of data stored can be used in a more targeted way - e.g. travel, car hire, downloads • Current situation – mix of dynamic and personalised pricing: • The Souk with information asymmetries • Sellers may know much more about consumers! • In some markets dynamic pricing may stay • In some personalised pricing will take over

3. Why does dynamic pricing exist? • ‘Bums on seats’ – maximise per seat revenue for time limited products • Different consumers have different ‘willingness to pay’/price sensitivity • Think Eurostar between London and Brussels: need to flex prices • ‘Thick’ route times (by day and season) for business travellers • ‘Thin’ route times (by day and season) for tourists • Bank revenue in advance on fixed cost facilities – season tickets • Encourage loyalty and repeat custom: loyalty cards • Maximise profit from individual sales • Effectively catch everyone on the demand curve • Products have a price life cycle – start expensive, then come down in price

4. Where is dynamic pricing common? • Travel – airline, train, road tolls • Yield management, peak/off peak pricing • Profiling? Saturday night stay, FFPs, season tickets • Sports – time and profile specific • Season tickets, advance purchase discounts, bundles • Profiling – ‘fans’ – occasional purchasers • See www.qcue.com • Loyalty cards • Coupons, targeted discounts • Sales/discounting/product retail cycles • Launch of new games/products

5. Examples? • Tesco Clubcard • Personalised coupons based on Clubcard data – quid pro quo • Amazon 2000 DVD experiment • Mapped ability to pay by profiling purchase history and residence among other factors. • Displayed different pricing results based on browser used. • Orbitz 2012 • Noticed Mac users spent ave of 30% more on hotel rooms • So displayed higher priced rooms if you use a Mac • Expedia – car rental International Business Times • car rentals in San Francisco between Sept. 1 and Sept. 8 • UK VPN - $311 • US VPN similar search $1,118.

6. How does it fit with UCPD and ‘fairness’? • Historically: dynamic pricing markets are ‘quid-pro-quo’ markets • Retailers offer different deals ‘in return’ for something from a consumer • Travel, sports, retailer loyalty cards • Online and future: increasingly ‘willingness-to-pay’ markets • Increasing online retail sophistication and targetting • ‘Big Data’ gets personal – greater specific information assymetries • Upside • offers for regular purchase items, related items, advance offers, items of interest • Downside • Poor targeting, ‘unfairness’, favoured and unfavoured consumers, regressive pricing, need to game system

7. Is there a UCPD dimension? • Dynamic+personalised pricing changes things • Are two sorts of markets evolving: • ‘Fair’ trade off markets based on quid-pro-quo: airlines, loyalty cards, season tickets • ‘Unfair’ targeted markets based on ‘revealed consumer WTP+information asymmetry’: online flexed offers with no comparator or opaque offerings? • Bit like visiting the Souk and every trader knowing exactly what you have bought in the past, how much you paid, what you liked/disliked, the names of all your kids, friends, favourite bands – while you know nothing about them • The ‘Apple Enforcement’ conundrum • iTunes pricing complaint – first to OFT then to DG Comp – solved in Brussels for all EU consumers • Apple two year guarantee problem – tackled in Member States one by one – differential application of rights and results • If we have pan-EU enforcement in competition law and patents why not in consumer law? • Most dynamic pricing issues are likely to be felt individually but operated cross nationally!

8. Implications • Price discrimination needs • Market power • does information assymetriesin Dynamic Pricing/Personalised Pricing give every seller market power vis-à-vis consumers? • Understanding of consumer reference pricing • Definitely present in personalised pricing, dynamic pricing slightly less so • Ability to stop arbitrage • no –but plenty of other vertical restraints can achieve that – credit card use restrictions • Dynamic pricing • Everyone can generally access the different pricing • Dynamic/personalised pricing • Everyone gets a different price at different times • Built on asymmetric information • Built on untransparent personal data and modelling • Unequal access and not necessary to have quid-pro-quo

9. Conclusions • Dynamic pricing • Common, quid-pro-quo; consumers ‘learn’/predict most markets • Dynamic/personalised pricing • Experiments 10 yrs+ BUT Big Data facilitates greater use • Increasingly experimented online – eg Amazon algorithms based on availability • Asymmetric info undermines quid-pro-quo of DP and makes UCPD more relevant • Enforcement – the ‘Apple Conundrum’ – iTunes vs guarantees - same practice, many countries – national or EU enforcement? • Do we need to merge competition and consumer law approaches for UCPD? • At EU level – focus on poor commercial practice AND market power? • AT EU level – focus on horizontal practices applied in more than 3 Member States? • At national level - focus on poor commercial practice for local cases? • We are at very early stages and so should not over-react but study closely! • OFT recent report very good start in process – good dispassionate look at the issue

10. Thank You for your attention!Always happy to extend the conversation online phil.evans@fipra.com