The Data People

2. All Customers Are Not Equal‘‘80% of sales or profit will come from 20% ofcustomers’’

3. What We DoWe identify Your Best Customers

4. We Build Detailed Pictures of Your Best Customers Profitability Attitudes Demographics Lifestyle LocationUsageA Marriage of all the Elements

5. Your Best Customers OnlineCombining online and offline data

6. How We Do Itdata

7. Data InterpretationWhat Is Your Data Telling You?We will audit your current data and create interpretation from itthis often starts with the basics of quality and quantity

8. turning your data into timely, relevant and meaningful information

9. turning that information into marketing advantage

10. Helping you ‘see the wood for the trees’ data

11. Data AnalyticsWhat could your data be telling you?We will undertake analysis on your data to build a fuller picture. For example:Basket analysis - identifies products likely to be purchased together, usually for cross-sellingPropensity models - help maximise Return on Investment (ROI) by targeting the most suitable audienceChurn modelling - predicting the likelihood to lapseLifetime value - quantifies the overall value of each customer at a revenue, gross or net profit level

12. Data StrategyWhat will your data allow you to do?We develop data led business and marketing strategies to maximise business growthCRM, Acquisition & Retention Strategies

13. Cross-sell & Up-sell Strategies

14. Data Collection & Data Partnerships Strategies

15. Creative Testing & Message HierarchiesData Planning Processshort. medium and long term needsdatasystemsbusiness, marketing and communications objectivesaccuracydata usageroiauditevaluationdatacollectionretentionlapsedata analysishygienestrategyacquisitiontrialdata qualitycrmdata enhancinganalysisdatabasesingle customer viewpredictivemodelsdata:profile,cluster,segmentdata needsadmin &reportsbuildstrategyfutureproofingdefine the objectivesdefine the problem

16. What We ManageThrough a network of third party partners we will source and manage Data EnhancementData CleaningDatabase Design & BuildList PurchaseData CollectionProcessing DataData Monetisation Web Analytics

17. Who We AreA data planning & analytics consultancyBased in Yorkshire5 core team members with a network of associate consultants & partnersWorking in the private and public sectorsPart of the Journey Group

18. Peter Rivett-Jones - Director20 years of data and marketing experienceSenior client services and planning positions in top DM agencies including Joshua, GGT Direct & EWAFounded DM agency Made With Love (MWL) in 1999 which was later sold to Chemistry in 2003Joined Poulters as Director & Shareholder in 2005 heading up all data and direct marketing accounts Co-founded The Data People in 2009

19. Steve Raper - DirectorA statistician with 25 years of data analysis and marketing experienceStarted career with British Gas in various sales and marketing positionsWent agency side in 1994 as Data Manager for Bedrock Communications independent consultant since 1996 providing data strategy & data analysis for agencies and clientsCo-founded The Data People in 2009

20. What Makes Us Different?We are marketeers first and data planners second

21. We turn numbers into words and pictures.

22. We answer the "so what?" of data and statistics

23. We have vast experience in data and all its touch points

24. We are independent consultants with nothing to sell apart from our time

25. We turn the complexity of data into strategies that make sense

26. We champion simplicity Sector ExperienceNHS & Health

27. FMCG

28. Automotive

29. Industrial

30. B2B

31. Travel & Tourism

32. Airlines

33. GovernmentRetail LeisureOffice EquipmentTelecomsFinancial ServicesMail OrderUtilitiesDrinks

34. Case Study 1Alliance & Leicester

35. The BriefAlliance & Leicester had been using cold contact lists to direct potential customers to their web site, with limited successRegistered users of the site were segmented by answers to basic financial questions only upon registrationCommunications to registered users had minimal tailoringWith results from nearly 2 years’ activity now available, our brief was to optimise results – Increase visits to the site from dm activityMaximise the potential value of visitors to the site

36. The SolutionThe first step was to take the client’s database of registered users, plus a sample file of non-respondents, and append lifestyle and demographic overlays to the dataCHAID modelling based on each set of overlays was carried out and gains charts compared to improve targeting The client’s registered user base was segmented in terms of their long-term behaviour in relation to the siteThe resulting 6 clusters were profiled in terms of their likely financial requirements and long-term value potentialThe rules for optimum allocation to segments were modelled using discriminant analysis

37. The SolutionA series of new questions at registration were identified to give the client data to allocate the new user immediately to the appropriate segment

38. The ResultsThere was an immediate increase of over 100% in site visits generated from direct mail through the improved targetingValue models within the segmentation allowed the client to estimate long-term potential valueThus determining the products advertised and marketing investment for each segmentIn addition, extra information about customers’ potential value are being added to the model as experience gives us more accurate information about the web-site’s longer term usage patterns and sales values

39. Case Study 2Holmes Place

40. The BriefLike many of its competitors, Holmes Place concentrated on acquisition during the unprecedented growth phase of the industryCustomer retention and improved targeting for acquisition were recognised as important business drivers as:competition increased cost of acquisition increasedattrition rates exceeded 50% per annumLittle was known about the customer, and no estimates of customer value and what drives it had been evaluatedThe brief was to understand the customer better to allow for smarter and more efficient marketing activity

41. The SolutionThe first step was to take the client’s membership and transaction databases and combine themAppend demographic and lifestyle informationIdentify valuable customers through data modelling – including length of membership and additional spend (e.g. personal training)Profiles for each club by value band were compiledKey variables – transactional and lifestyle - for predicting closure of membership were identifiedThe resulting churn model was applied to the customer base to predict the likelihood of attrition

42. The SolutionAlthough there are many factors affecting renewal of membership (such as moving away from the area), many members do not renew because of their lack of usage of the facilities availableThe models allowed us to identify the probability of each member renewing, and allows communication strategies to be put into practice for valuable but potentially disloyal customers

43. The ResultsTargeting for new customers has been revitalised After years of reducing returns from marketing targeted by demographics only, the new models coupled with data cleaning processes have resulted in a five-fold increase in response ratesCosts per new member have been reducedAverage value of each new member acquired was increasedEarly indications are that the modelling of likely defectors, coupled with communications designed to retain them, is starting to reduce churn rates

44. Case Study 3Nescafe

45. The Brief A major development in the Nescafe Ultra Premium brand strategy was to narrow the target audience that for marketing communicationsExtensive work by the brand team had re-defined the audience that Nescafe UP would targetTwo target audiences called Roast & Ground Dippers and Instant Dippers had been identified – c1.7m HH’sThe brief was how, from a data perspective, do we find this audience to allow a major dm sampling campaign to take place

46. The SolutionNescafe did not have marketing data of their ownThere was not sufficient volumes of external data to purchase that identified ‘dipping’In order to get the quantity and quality of data needed we proposed data modellingIn simple terms, this meant creating a profile of the people we wanted and then finding lookalikesThe secret lay in having the most accurate profile at the start

47. The SolutionWe recommended using Tesco Clubcard data to create the profile that the data model would be built aroundThe model were built using CHAID and then applied to external lifestyle data sources

48. The ResultsThe data model used in the direct marketing campaign proved to be highly successfulThe mailing delivered £280k uplift in the first three months aloneThe mailing had an impact on customers behaviour resulting in sustained change over a year – once customers had tried it they remained loyalCustomers moved from the targeted product areas of Freeze Dried and R&G proving the model’s accuracyAt a brand level customers were most likely to have moved from Kenco Ultra Premium and other Premium freeze dried coffees

49. The Data People turn customer data into greater profits

50. Thank You

The Data People

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The Data People