Big Data, Privacy and Intangible Assets. IAPP Data Protection Intensive April 1 May London

Transcription

1 Big Data, Privacy and Intangible Assets IAPP Data Protection Intensive April 1 May London

2 Speakers Welcome & Introductions Ashley Winton, Partner, Data, Privacy & Cyber Security Group Chair, White & Case LLP Kelvin King, Managing Director, Valuation Consulting Co Ltd George Pappachen, Executive Vice President, Strategy at Kantar/WPP

3 Agenda Program Outline I. Welcome and Introductions II. The Premise The Economic Value of Personal Data III. Customer and Data Intangibles IV. Data Management in Practice V. Case Study The Desperate Shopper VI. Questions & Answers/Closing Remarks 3

4 The Premise The Economic Value of Personal Data Personal Data and Privacy has a value to both businesses and consumers From a consumer perspective it has a value as a currency to purchase goods or services: Sure I don t mind letting companies know a little about me, so that I can get free stuff on the internet <Anon> From a business perspective, knowing more about your customers and potential customers allows you to undertake more cost effective marketing and to identify new services Here at <Online Co> we are no different from a well run old fashioned department store, with staff that who knows its customers, knows what they like and can help them with their purchase <Anon> Intuitively, this information is an intangible asset which is dependent, in part, upon the legal rights and restrictions that apply to the data. 4

5 The Premise The Economic Value of Personal Data Arguably the systems that you have put in place that allow for analytics and profiling, even if personal data is not processed, are valuable to you <Online Co> comfortably beats it competitors because the analytics engine we have built allows us to predict more accurately the products which are customers are interested in <Anon> Here research and development has been used to build a knowledge based asset. Finally, good systems and governance that prevent a loss in value, have a real value to the company. We have seen the Ponemon Cost of Data Breach Survey estimate at $188 per record, and know that in over half of the cases the cause is human error or system glitch and so we have made significant investments to prevent data loss happening to us <Anon> Normally, the money spent here would be categorised as an expense, but could it be categorised as an investment in capital? Intuitively there is a value, but what is it and how is it determined? 5

6 Speakers Ashley Winton, Partner & Data, Privacy & Cyber Security Group Chair, White & Case LLP Kelvin King, Managing Director, Valuation Consulting Co Ltd George Pappachen, Executive Vice President, Strategy at Kantar/WPP

7 CUSTOMER AND DATA INTANGIBLES Clients Product customers Service customers Non-compete agreements Contracts Databases Brands Experience Negative knowledge R&D The spectrum of creative thought 7

8 STARTING AT THE TOP AND TRYING TO BUILD DOWN THE LAWS OF PHYSICS AND VALUATION SUGGEST THAT THIS IS NOT POSSIBLE 8

9 INTELLECTUAL PROPERTY AND INTANGIBLE ASSETS Those assets whose essential characteristics are derived from the Legal System, in this case the UK, supporting the economic proposition of Customers and Data Registered Rights Patents UK, EP(UK) (PA 1977, EPC 2000) Trade Marks UK, CTM (TMA 1994) Registered Designs UK, CDR (RDA 1949) Unregistered Rights Confidential Information (equitable and contract) Passing-off (common law) Design Right (CDPA 1988) Copyright (CDPA 1988 and Copyright Computer Software Amendment Act 1985) Miscellaneous: Performance Rights, Image Rights, Moral Rights, Database Rights, Malicious Falsehood (common law), Plant Variety Rights Rights and Relationships Contracts Workforce Customers, customer lists, customer contracts, customer relationships, customer agreements Experience and Trade Secrets 9

10 OCCASIONS FOR VALUING DATA Mergers & Acquisitions Portfolio review and risk assessment Arrange a loan securitisation Tax purposes Licensing Balance Sheet Joint Ventures and Technology Transfer Pension deficits Selling your IP Distress 10

11 VALUATION RULES Tax Valuation and Open Market Value Fair Value Fair Market Value Commercial Value Investment Value Owner Value 11

12 METHODS OF VALUATION Market based Comparable market transaction Unlike most intangibles sales of discreet customer intangibles do occur Price per customer etc. etc. Caveats Lack of information Separate values Special purchasers Different negotiating skills Assets not always comparable 12

13 METHODS OF VALUATION Cost based Historical or replacement cost Development Maintenance - calling, advertising, mailing, entertaining, files, records etc. Opportunity cost egg time cost in creation Caveats Economic Benefits Excluded Duration of benefit and economic life Obsolescence difficult to quantify 13

14 INCOME METHODS HOW MUCH CASHFLOWS? Discounted cash flows weighted to probabilities 14

15 TOOLS Gross Profit Differential Methods Premium Prices/Premium Profits Excess Profits Method Relief from Royalty Method 15

16 HOW LONG FOR AND USEFUL LIFE Physical Life age profiles reflect survivor curves Functional Life eg maintenance of directories Technological Life eg speed, efficiency Economic Life eg age of software Legal Life 16

17 17 AT WHAT RISK?

18 MONTE CARLO AND REAL OPTIONS Effectively a DCF multiplier uses inputs in ranges with relative probability Numerous DCF calculations accounting for various scenarios, say of revenue, market share, costs, internationality and other risks egg launch date 2018 (10% probability) 2019 (30%), 2020 (20%) etc. With just 4 scenario changes of the stated assumptions above this means 256 models! That is 4 values for each of income, different market share, costs, international penetration i.e. 4 x 4 x 4 x 4 = 256 Probability trees = snakes and ladders, develops the Monte Carlo analysis Develops terminations (snakes) if a route identifies problems to suggest failure 18

19 THE LICENSEE/PURCHASER & LICENSOR Four Calculations or Steps 'can Kelvin count' 1. Intrinsic value of Vendor 2. Intrinsic value of Purchaser PLUS 1. Intrinsic value of Vendor 2. Intrinsic value of Purchaser The capital values calculated are an essential step to calculate royalty rate or valuation - discuss 19

20 Ashley Winton, Partner & Data, Privacy & Cyber Security Group Chair, White & Case LLP Kelvin King, Managing Director, Valuation Consulting Co Ltd George Pappachen, Executive Vice President, Strategy at Kantar/WPP

21 Data Management is our PRESENT + future 21

22 A view into our business framework Media & Consumer Focus 22

23 The context for marketing decisions RELEVANT CONVENIENT RIGHT MESSAGE, RIGHT PERSON, RIGHT TIME TRUSTED CUSTOM/PERSONAL 23

24 OUR THREE DIMENSIONS KANTAR MEDIA PAID, OWNED, EARNED Investment Stimulus Search Action TOUCHPOINTS Awareness Opinion 24 Sharing Engagement

25 Business Conditions Digital & Data 25

26 Six forces driving business 1. Shift to mobile 2. Growth in e-commerce 3. Increasing volume of data 4. Growing importance of content 5. Increasing social/participatory nature of media 6. Rise of new gatekeepers 26

27 A new set of gatekeepers 27

28 All data are not created equally Our journey to full accountability 28

29 Data of interest to us Purchases Journey Attitudes Offline and Online data SKU level data Individual user data panel based data Breadth of categories Brand coverage Exposure to marketing Informational path to purchase Intermediating events Search influence Popular retail destinations Influence of trends Distractions and detractors Demographic segments Psychographic segments Sector based segments Brand Health Category Insights Impact of recommendation Advertising influence 29

30 Audience Consumer UNIFIED CONSUMER PROFILE 3 layers: Closer to center, higher the value Market 30

31 Giving consumers control of data we d like to! Some market examples: VRM 31

32 AT&T Your data in exchange for money ($30 annually) 32

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34 Speakers Ashley Winton, Partner & Data, Privacy & Cyber Security Group Chair, White & Case LLP Kelvin King, Managing Director, Valuation Consulting Co Ltd George Pappachen, Executive Vice President, Strategy at Kantar/WPP

35 Examples of Big Data value in M&A Desperate Shopper Desperate Shopper (in the EU) has developed a smart phone GPS/mapping application which gives shoppers turn by turn directions to bargains on the high street. Desperate Shopper (DS) collects consents when the app is installed and later when the user signs up for offers. It has an app install base of 250,000 and 180,000 registered users. DS has information about shopping preferences, preferred shops and other profiling information (not all personal data). DS has developed predictive technology which will predict what you will want to buy next. DS has already seen Law & Co, and believes that it is in full compliance with the law DS is keen to sell itself to Modor, a US social media company but it has yet to break even. 35

36 Desperate Shopper Questions Can DS use intangible asset valuation to affect its sale price? E.g. is the following realistic? 10 per unregistered app user and 20 per registered app user (700k+3.6m= 4.3m). The research costs spent on developing the predictive platform 500k The external legal and consulting fees for getting in good compliance 150k What could DS do to improve its intangible asset valuation? What should Modor do to depress the valuation and try and improve its negotiating position? How with the proposed General Data Protection Regulation affect the valuation? 36

37 Data Protection in the M&A lifecycle When acting for the purchaser Tailored due diligence questions might reveal weakness in intangible asset value Be clear about what the purchaser wants to do with the target post transaction can provide useful negotiating leverage Large quantities of user or consumer data? Gives increased price sensitivity to weak data protection compliance Weak compliance or uncertain rights over customer and prospective customer data indicate a reduction in intangible asset value and your bid can be reduced accordingly. When acting for the seller Being in reasonable compliance with the law can deflect awkward further investigations by the purchaser In English law transactions, having a good grip of what is happening in the business allows for good disclosure so deflecting pressure from difficult warranties in the sale and purchase agreement Ideally, the state of the privacy notice and/or applicable consents should be recorded as meta data with the personal data when it is recorded. 37

38 Speakers Thank You Ashley Winton, Partner, Data, Privacy & Cyber Security Group Chair, White & Case LLP Kelvin King, Managing Director, Valuation Consulting Co Ltd George Pappachen, Executive Vice President, Strategy at Kantar/WPP