Creds 030409


Published on

An overview of The Data People

Published in: Business, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Creds 030409

  1. 1. All Customers Are Not Equal ‘‘ 80% of sales or profit will come from 20% of ’’ customers
  2. 2. What We Do We identify the customers that matter from your data and develop strategies that take advantage of this knowledge
  3. 3. Who Are Your Best Customers? • The 80:20 Pareto Effect is alive and well and should be the driving force behind your marketing and business strategies • So who are your best customers? – Where they are – What they do – When they do it – How often do they do it – How to find more of them – How best to talk to them – How to keep them – How to cross-sell and up-sell to them – How to identify those that are unprofitable
  4. 4. We Build Detailed Pictures of Your Best Customers Profitability Attitudes Lifestyle Demographics Usage Needs A Marriage of all the Elements
  5. 5. We Unleash The Power Of Data Personal Personal Personal Database General General General Your Best Customers Customer Knowledge
  6. 6. Your Best Customers Online • Do you understand how your best customers behave online • Why should your site be all things to all people? Make it the most effective for those who matter most to the business. – If your most profitable customers jump around the site, design should enable that behavior – If those top-tier customers remain in one category, don't clutter their experience with links they'll never use • Do you have incentives that appeal to your best customers? • Combining profitable customer data with online behaviour data is the future
  7. 7. Who We Are • A data planning consultancy • Set up in 2009 • Based in Leeds and Bristol • 5 employees with a network of associate consultants • Working in the private and public sectors • Part of the Journey Group
  8. 8. Data Planning data
  9. 9. Data Interpretation • What Is Your Data Telling You? • We will audit your current data and create interpretation from it – this often starts with the basics of quality and quantity – turning your data into timely, relevant and meaningful information – turning that information into marketing advantage – Helping you ‘see the wood for the trees’ data
  10. 10. Data Analytics • What could your data be telling you? • We will undertake analysis on your data to build a fuller picture. For example: – models can be developed that predict which of your customers are most likely to churn or which are most likely to buy a specific product – segmentation models can be developed that group your customers into specific clusters to help refine your contact strategy – basket analysis models can be developed that analyse the combinations of products customers have purchased and help predict their next purchase
  11. 11. Data Strategy • What will your data allow you to do? • We develop data led business and marketing strategies to maximise business growth – CRM, Acquisition & Retention Strategies – Cross-sell & Up-sell Strategies – Data Collection & Data Partnerships Strategies – Creative Testing & Message Hierarchies
  12. 12. What We Manage • Through a network of third party partners we will source and manage – Large Data Analysis & Segmentation projects – Data Enhancement – Data Cleaning – Database Design & Build – List Purchase – Data Collection – Processing Data – Web Analytics
  13. 13. Our People Peter Rivett-Jones David Emslie Steve Raper Director Director Director
  14. 14. Peter Rivett-Jones - Director • 20 years of data and marketing experience • Senior client services and planning positions in top DM agencies including Joshua, GGT Direct & EWA • Founded DM agency Made With Love (MWL) in 1999 which was later sold to Chemistry in 2003 • Joined the board at Poulters in 2005 heading up all data and direct marketing accounts • Co-founded The Data People in 2009
  15. 15. David Emslie - Director • 25 years of data and marketing experience • Senior management positions in top agencies including Poulters & JDA • Head of Marketing for Strachan Bedrooms • Joined Equifax in 1998 where he spent 10 years in a variety of senior Sales and Business Development roles in Marketing Services and Consumer Risk • Co-founded The Data People in 2009
  16. 16. Steve Raper - Director • A statistician with 25 years of data analysis and marketing experience • Started career with British Gas in various sales and marketing positions • Went agency side in 1994 as Data Manager for Bedrock Communications • independent consultant since 1996 providing data strategy & data analysis for agencies and clients • Co-founded The Data People in 2009
  17. 17. What Makes Us Different? • We are marketeers first and data planners second • We turn numbers into words and pictures. • We answer the quot;so what?quot; of data and statistics • We have vast experience in data and all its touch points • We are independent consultants with nothing to sell apart from our time • We turn the complexity of data into strategies that make sense • We champion simplicity
  18. 18. Sector Experience • • NHS & Health Retail • • FMCG Leisure • • Automotive Office Equipment • • Industrial Telecoms • • B2B Financial Services • • Travel & Tourism Mail Order • • Airlines Utilities • • Government Drinks
  19. 19. Case Study 1 Alliance & Leicester
  20. 20. The Brief • Alliance & Leicester had been using cold contact lists to direct potential customers to their web site, with limited success • Registered users of the site were segmented by answers to basic financial questions only upon registration • Communications to registered users had minimal tailoring • With results from nearly 2 years’ activity now available, our brief was to optimise results – – Increase visits to the site from dm activity – Maximise the potential value of visitors to the site
  21. 21. The Solution • The 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 data • CHAID 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 site • The resulting 6 clusters were profiled in terms of their likely financial requirements and long-term value potential • The rules for optimum allocation to segments were modelled using discriminant analysis
  22. 22. The Solution • A series of new questions at registration were identified to give the client data to allocate the new user immediately to the appropriate segment
  23. 23. The Results • There was an immediate increase of over 100% in site visits generated from direct mail through the improved targeting • Value models within the segmentation allowed the client to estimate long-term potential value • Thus determining the products advertised and marketing investment for each segment • In 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
  24. 24. Case Study 2 Holmes Place
  25. 25. The Brief • Like many of its competitors, Holmes Place concentrated on acquisition during the unprecedented growth phase of the industry • Customer retention and improved targeting for acquisition were recognised as important business drivers as: – competition increased – cost of acquisition increased – attrition rates exceeded 50% per annum • Little was known about the customer, and no estimates of customer value and what drives it had been evaluated • The brief was to understand the customer better to allow for smarter and more efficient marketing activity
  26. 26. The Solution • The first step was to take the client’s membership and transaction databases and combine them • Append demographic and lifestyle information • Identify valuable customers – including length of membership and additional spend (e.g. personal training) • Profiles for each club by value band were compiled • In addition, value groupings by type of membership and by number of visits to clubs were made
  27. 27. The Solution • Key variables – transactional and lifestyle - for predicting closure of membership were identified • The resulting model was applied to the customer base to predict the likelihood of attrition • Although 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 available • The 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
  28. 28. The Results • Targeting 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 rates • Costs per new member have been reduced • Average value of each new member acquired was increased • Early indications are that the modelling of likely defectors, coupled with communications designed to retain them, is starting to reduce churn rates
  29. 29. Case Study 3 Nescafe
  30. 30. The Brief • A major development in the Nescafe UP’s brand strategy was to narrow the target audience that all marketing communications were aimed at. • Extensive work by the brand team had re-defined the audience that Nescafe UP would target. • Two target audiences called Roast & Ground Dippers and Instant Dippers had been identified – c1.7m HH’s • The brief was how, from a data perspective, do we find this audience to allow a major dm sampling campaign to take place
  31. 31. The Solution • Nescafe did not have marketing data of their own • There 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 modelling • In simple terms, this meant creating a profile of the people we wanted and then finding lookalikes. • The secret lay in having the most accurate profile at the start • We recommended using Tesco Clubcard data to create the profile that the data model would be built around • The model would then be applied to external lifestyle data sources
  32. 32. The Model TGI DATA VALUE DATA £ Build Matched to Data Claritas Model database Audience Characteristics
  33. 33. The Results • The data model used in the direct marketing campaign proved to be highly successful • The mailing delivered £280k uplift in the first three months alone • The mailing had an impact on customers behaviour resulting in sustained change over a year – once customers had tried it they remained loyal • Customers moved from the targeted product areas of Freeze Dried and R&G proving the model’s accuracy • At a brand level customers were most likely to have moved from Kenco Ultra Premium and other Premium freeze dried coffees
  34. 34. The Data People use customer data to deliver greater profits and more effective outcomes
  35. 35. Thank You