Getting creative with data

1. Getting creative with data

2. My experience Agencies • Ogilvy & Mather Direct • BMP • Aspen Direct • Craik Jones • HS&P Clients • Land Rover • Honda • Saab • Peugeot • Diageo • Unilever • Sony • Virgin Trains • COI • Boots The Chemist • Silverlink Trains • The Australian Tourist Commission • Save The Children

3. Data should be used creatively

4. What is data planning? Using data in your business to drive your marketing and sales Once called database marketing or data driven marketing But now has to incorporate data from within the whole organisation and outside sources

5. We should call it insight Customer data Media Digital Transactional data Insight Product Market research Econometrics Identify and target segments

6. Data planning • Understanding • Analysis, segmentation, modelling, quantitative research • Communication • Audience identification, means of engagement, messaging to drive response • Evaluation • Sales, media, ROI • Results interpretation • Learning

7. A mixed up world • Specific data planning disciplines • The newish world of digital • Rapidly developing consumer technology platforms • The law • The combination of planning skills to create true customer insight and understanding Sometimes this needs more than just data planning

8. What is MPP? • A team of independent marketing planners • Disciplines include: • Data • Digital • Brand • Specific skills: • Research • Econometrics • Analysis and modelling • Mobile marketing • Media planning

9. Informed decisions from independent thinking Data Insight Brand Digital

10. Boots case study

11. Finding those with potential Demystifying the model

12. So what is it all about • Getting Boots to move to a more targeted approach to customer communications. • What we set out to achieve. • Building the model and how it works. • Using the model in anger. • Results.

13. Boots problem Supermarkets Lloyds & local chemists Department Stores Under siege on many fronts Dixons Marks & Spencer

14. Boots needed to stand for something again To define itself and master an area To be focused and single minded

15. New focused proposition for Boots PERSONAL Shopping at Boots is a more engaging and intimate experience Clarifies the benefit of visiting Boots over supermarkets Recognises Boots employees as a critical element delivering this attention and service Health and beauty sits equally comfortably under this umbrella Clarifies the benefit of visiting Boots over supermarkets

16. Where Advantage Card fits

17. 1m high 10.5m active card holders 9.5m low & medium value, low redeemers 15m card holders 4.5m non activated card holders 4.5m cardholders, never active 19m potential card holders 19m Boots shoppers with no card

18. Information used on Existing value to Boots Message Universal, treat yourself Information not used on Spend outside Boots Propensity to change 8 segments Treat yourself not applied in segmented manner Cos IE CB YF MBB LC HAT HC Previous approach magazine tactical mini mag

19. What was working • Biggest • Most generous • Drives sales • New holders • Treats positioning • ATV • Frequency voucher • Magazine • Events • Supplier funded DM • Kiosks • Redeemers • Simplicity

20. What was working gain VIRTUOUS CIRCLE OF SPEND redeem

21. Areas to address Sales effect Kiosks Mature holders Highest value ring-fenced Magazine Frequency Non redeemers 30% not active Sales decline Appeal Generosity - 4 X more Treats fit with brand vision

22. Getting started

23. What we set out to achieve • Identify customer value and potential • To protect spend in Boots. • To steal share from competitors. • Change their behaviour • Identify the key behaviour triggers that influence spend. • Hard triggers - collect and redeem points, distance from store and store type, make up of family etc. • Soft triggers - attitudes to health and fitness, dieting etc. • Use the wealth of data in Boots to support a range of business planning functions.

24. The vision Score Ad Card customers and put into three groups for communications Protect & develop Steal share Suppress manage

25. Current value by category Propensity to change Value to market by category The vision Demographics: Database/ External sources Geography Taken from Ad Card database down to category Boots shopping behaviour- what, when, value • Attitudes: TGI • attitudes to health and • fitness • attitudes to personal • appearance/care • attitudes to environment • attitudes to shopping/ • brands • - attitudes to eating Based on 1,400 exit poll. Works at category level. Need more detail to go down to concept level - TGI overlay

26. The vision Every customer is given a score Value to Boots Propensity to change Value to market + + Scores are calculated at concept group level Profit by product Steal Protect Maximum flexibility for targeting/offer to stimulate change at a micro level

27. The realityBuilding the model

28. How? • The Boots Database contains over 14 million individuals, with details of every transaction for the last 2 years • This amount of data cannot be modelled, analysed or even held easily anywhere • In order to even start looking at overlaying a model a 5% sample had to be extracted and sent to a specialist analytical company

29. What? • A model was needed to identify who had the potential to buy specific products. • Past transactional behaviour is by far the best way to ascertain this • The analytics company took all data and monitored trends on • purchasing patterns within concept groups • demographic profiles • number of purchases • average time between repeat purchases • average spend • etc

30. The Result - A Summary • Two models were produced • This is based on likelihood to buy plus potential to spend • Used together they ensure best response/sales rates • This means increase in sales for Boots • Plus they ensure the best ROI is generated for the supplier

31. Affinity Model Tertiary Secondary Primary Hot Prospects Warm Prospects Coolest Prospects

32. Primary Affinity • Customers who have directly bought the promoted product in the past 12 months, ranked by frequency and total spend

33. Secondary Affinity • Customers who have NOT bought promoted product but have bought associated products in the past 12 months 

34. Age Tertiary Affinity • Customers who have a similar profile to the peer group of the product e.g 25-40 Gender e.g female Segment e.g Cosmopolitan Mosaic e.g stylish singles Lookalikes

35. Share of Wallet Model 2 example customers SOW for Dove Shampoo Customer X SOW = 50% Previous spend =£9.60 (High Water Mark) Current Spend = £4.80 - Customer Y Current Spend = £1.60 SOW = 33% Little previous spend, so Peer Group Average = £4.60 -

36. The Model used in Value Mailings • Offers are tailored to the individual based on their past transactions • Each person gets a score for every offer available, with the top 8 products defined as their most suitable being selected • This allows communications to be centred around ‘offers we know you like and some you want to try’

37. Finding offers • 20 million coupons were required for first mailing • Plus additional funding (to top up Boots budgets) • Targeted both Boots Category teams and Suppliers • Then cajoled, persuaded and bullied • The result - we generated 56 different offers for the first Value mailing

38. The Process of Value Mailings Step 1 • All Offers, approx 60+ per value mailing are matched to the entire Boots Database • The prime target audience is then identified, ie those who have the highest affinity to the offers available

39. The Process of Value Mailings Step 2 8 • The selected universe is then given a score for every offer available, based on the likelihood of them purchasing • This produces millions of stats, which then need to be optimized and ranked • To produce the top 8 offers for EVERY PERSON

40. The Personalisation of Value Mailings • The essence of Value is that each person gets offers applicable to them • To coincide with this, the rest of the mailing was personalised to produce a mailing tailored to each individual • The personalised information includes: • Number of points available • Number of redeemed points in the last 12 months • Favourite store to shop in • Total Value of coupons • Total points available on promotion • Redemption ideas based on number of points