Privacy and Personalisation A Private Sector Perspective Clive Humby October 2014

1. Privacy and Personalisation A Private Sector Perspective Clive Humby October 2014

2. THE LOYALTY MYTH

3. A brief timeline of customer insights 2015 2005 1985 1975 1995 1955 1965 Big Data Social Media Digital Media Mobile Behaviours Browsing Behaviour You are what you are passionate about Customer Data TESCO Amex Visa Amazon You are what you buy Geodemo- graphics ACORN MOSAIC You are where You live Social Class AB C1 C2 DE You are the job you do Lifestyles Lists NDL Guarantee Cards You are what you say “Claimed & demographic data” “Actual & behavioural data”

4. Who wins… the retailer data model Benefits for the retailer, manufacturers and customers • MANUFACTURERS • improved Marketing ROI • better NPD • effective promotions • Increased brand loyalty • RETAILERS • higher footfall • increased share of wallet • better retail offer • new service development CUSTOMERS • promotions • better shopping trip • new meal solutions • products I want • better prices • Relevant offers

5. customer data desired customer experience customer experience customer insight insight enables action that drives behaviour to drive... to inform... enables... • Increased Customer Lifetime Value • Same-store sales growth • Same-store Margin growth • Market share growth • Basket segmentation • Basket size/value • Affluence • Shopping missions • Customer segmentations • Value & Loyalty • Lifestyles • Attitudes • Lifestage / Lifestyle • Enabling Better Business Decisions • Great Shopping Experience • Meaningful innovation • Relevant Marketing • Data sources • EPoS data • Credit & debit card data • Loyalty cards • 3rd party data • Building relationships with customers • Retention • Growth • Win back / prospecting Personal data is only need in small parts of this process

6. So what do you have to understand? MOTIVATIONS Describe customers by attitudes, influence and circumstances. Based on granular behavioural data. All customers measured on each motivation. SEGMENTATIONS Describe customers by demographics, usage behaviour and profit. Each customer has one segment.

7. value of insight v value of customer contact Well put together insight models… solve real problems are global for global brands create new co-operative business models need education

8. value of insight v value of customer contact Effective customer contact… creates win-win-win clears a direct path to the customer by dis-intermediating marketing agencies allows you to build trust through relevance, content and consent

9. Big data… shopping, mobile, entertainment, payment cards

10. the data myth: it’s all about big data

11. What is big? What is useful? 1 Terabyte Solid State Disk Store c 1,000 transactions for 10,000,000 people Or 4 transaction per second for every day of your life Read & process the entire dataset in about 30 minutes What do we want from it? Patterns & Decisions

12. useful data is not big, it’s tiny

13. When do we need private data in this process • PERSONALISATION & PRIVACY ARE DIFFERENT CONSTRUCTS • Finding patterns, assortment planning, product associations, price optimisation can all be done with “anonymised” data • Claimed attributes (age, children, car ownership) are all better MODELLED than actual; you are what you do NOT what you say • Commerce is fundamentally different to government and research objectives:- • we want to understand the behaviour of groups and tribes • we are not interested in the outliers and exceptions • we are concerned with efficient process, optimising mix • we are data rich; matching data sources less of an imperative • social log-on gives access to a rich public persona of the customer • much personalisation is about what you browse and click not YOU • managing “your data” is often a customer service benefit • Personalisation can be achieved without breaching privacy; only addressable communication needs to carry this risk

14. Red & Blue Data Blue data is analysed ETL Process PIDs Hashed Geocodes Red/Blue Split Personalsied Fulfilment When needed Red Data is hidden

15. Predictive v Descriptive Analytics v Fact Confirmation Credit Scoring Insurance Risk Behavioural Scoring Motivations Entitlement (eg Over 18) Membership

16. Other processes we employ • Back to “postcode” or small areas • proving very powerful for passions, like theatre going • allows merging of multiple venues without personal disclosure • perturb the data or use “barnardisation” methods • Recognise we have Consent • develop a customer charter spelling out what you will and will not do • Always work in the common good • Example: no switch messages on repertoire • Be cool not creepy • Apply commonsense rules • Someone will always take exception.. What is the “Daily Mail” test • It’s only junk when it’s not relevant • Customers understand the common standards and expect them • Using data badly or failing to use it carries as much reputational risk as highly targeted and relevant applications

17. Emerging Trends Personal Data Stores & Cloud Verification I don’t need your DOB, just proof & photo to show you are over 18 to buy alcohol User controlled consent & data access Share just the facts needed to fulfil the transaction car insurance, proof of entitlement MONETISE MY OWN DATA.. What will you give me to share my purchase history with you? Longer Term Benefits I know my family has a pre-disposition to this genetic disease, I will share my data with you as an act of philanthropy / self interest Organisations are avoiding unnecessary data

18. Moral Dilemma… Consumer fears Company Reputational Risk Car telematics… shows me you speed when you drive Shopping data…you buy more than 50 units of alcohol a week Smart Meters… you leave your lights on all the time Mobile phone… movement, location and social circles ISP… what and where you visit BUT I can change any of these suppliers at any time Government is not trusted with data; no consent process RIPA has damaged consumer trust in government use of data

19. Spend patterns are evolving towards digital and social 2014 is set to be the first year that consumers spend more time with digital media than they do with traditional media Source: eMarketer March 2014 US ad spending on the internet surpassed ad spending on broadcast television for the first time last year, increasing 17 per cent in 2013 to a record $42.8bn Source: IAB April 2014 Social Media spending is expected to be 21.4% of marketing budgets in five years Source: theCMOsurvey August 2014

20. Starcount’s pioneering Fan Science Platform tracks... You should know your customers better

21. Through Fan Science we created our core product offering… Social DNA Defines Content Social DNA Insights Vibe Returns Data External Consumer product Internal Insight Product Discover the communities, influencers and content that matters most to your customers Create intelligent, curated content streams for you customers.

22. Conclusions Have a clear customer charter Red / Blue split can protect against most risks, except criminality Commercial organisations only care about tribes not individuals Relevance is the key; personalised fulfilment can be driven from anonymised analytics Be cool not creepy; most commercial organisation are looking at ways of not storing personal data Big data is not a panacea; to be useful it needs to become actionable Social Media is rewriting the rules for getting your message to land Personal data stores will give the consumer control The future for Brands is engaging with customers via their passions