All Customers Are Not Equal<br />‘‘<br />80% of sales or <br />profit will come <br />from 20% of<br />customers<br />’’<b...
What We Do<br />We identify Your Best Customers<br />
We Build Detailed Pictures of Your Best Customers<br />  Profitability<br />  Attitudes<br />   Demographics<br />   Lifes...
Your Best Customers Online<br />Combining online and       offline data<br />
How We Do It<br />data<br />
Data Interpretation<br />What Is Your Data Telling You?<br />We will audit your current data and create interpretation fro...
turning your data into timely, relevant and meaningful                                                  information
turning that information into marketing advantage
Helping you ‘see the wood for the trees’                                </li></ul>data<br />
Data Analytics<br />What could your data be telling you?<br />We will undertake analysis on your data to build a fuller pi...
We Identify Profit Opportunities<br />Drill-down analysis<br />Venn diagramsused to detectselection overlap<br />Tracking ...
Data Strategy<br />What will your data allow you to do?<br />We develop data led business and marketing strategies to maxi...
Cross-sell & Up-sell Strategies
Data Collection & Data Partnerships Strategies
Creative Testing & Message Hierarchies</li></li></ul><li>Data Planning Process<br />short. medium and long term needs<br /...
What We Manage<br />Through a network of third party partners we will source and manage <br />Data Enhancement<br />Data C...
Who We Are<br />A data planning & analytics consultancy<br />Based in Yorkshire<br />5 core team members with a network of...
Peter Rivett-Jones - Director<br />20 years of data and marketing experience<br />Senior client services and planning posi...
Steve Raper - Director<br />A statistician with 25 years of data analysis and marketing experience<br />Started career wit...
What Makes Us Different?<br /><ul><li>We are marketeers first and data planners second
We turn numbers into words and pictures.
We answer the "so what?" 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 </li></li></ul><li>Sector Experience<br /><ul><li>NHS & Health
FMCG
Automotive
Industrial
B2B
Travel & Tourism
Airlines
Government</li></ul>Retail <br />Leisure<br />Office Equipment<br />Telecoms<br />Financial Services<br />Mail Order<br />...
Upcoming SlideShare
Loading in …5
×

Creds Latest

251 views

Published on

An introduction to The Data People

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
251
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Creds Latest

  1. 1.
  2. 2. All Customers Are Not Equal<br />‘‘<br />80% of sales or <br />profit will come <br />from 20% of<br />customers<br />’’<br />
  3. 3. What We Do<br />We identify Your Best Customers<br />
  4. 4. We Build Detailed Pictures of Your Best Customers<br /> Profitability<br /> Attitudes<br /> Demographics<br /> Lifestyle<br /> Location<br />Usage<br />A Marriage of all the Elements<br />
  5. 5. Your Best Customers Online<br />Combining online and offline data<br />
  6. 6. How We Do It<br />data<br />
  7. 7. Data Interpretation<br />What Is Your Data Telling You?<br />We will audit your current data and create interpretation from it<br /><ul><li>this often starts with the basics of quality and quantity
  8. 8. turning your data into timely, relevant and meaningful information
  9. 9. turning that information into marketing advantage
  10. 10. Helping you ‘see the wood for the trees’ </li></ul>data<br />
  11. 11. Data Analytics<br />What could your data be telling you?<br />We will undertake analysis on your data to build a fuller picture. For example:<br />Basket analysis - identifies products likely to be purchased together, usually for cross-selling<br />Propensity models - help maximise Return on Investment (ROI) by targeting the most suitable audience<br />Churn modelling - predicting the likelihood to lapse<br />Lifetime value - quantifies the overall value of each customer at a revenue, gross or net profit level <br />
  12. 12. We Identify Profit Opportunities<br />Drill-down analysis<br />Venn diagramsused to detectselection overlap<br />Tracking data/customer movement<br />Predictive modelling, scoring, CHAID…<br />Geographic segmentation forsales or franchise territories…<br />
  13. 13. Data Strategy<br />What will your data allow you to do?<br />We develop data led business and marketing strategies to maximise business growth<br /><ul><li>CRM, Acquisition & Retention Strategies
  14. 14. Cross-sell & Up-sell Strategies
  15. 15. Data Collection & Data Partnerships Strategies
  16. 16. Creative Testing & Message Hierarchies</li></li></ul><li>Data Planning Process<br />short. medium and long term needs<br />data<br />systems<br />business, marketing and communications objectives<br />accuracy<br />data <br />usage<br />roi<br />audit<br />evaluation<br />data<br />collection<br />retention<br />lapse<br />data <br />analysis<br />hygiene<br />strategy<br />acquisition<br />trial<br />data <br />quality<br />crm<br />data<br /> enhancing<br />analysis<br />database<br />single <br />customer <br />view<br />predictive<br />models<br />data:<br />profile,<br />cluster,<br />segment<br />data <br />needs<br />admin &<br />reports<br />build<br />strategy<br />future<br />proofing<br />define <br />the objectives<br />define <br />the problem<br />
  17. 17. What We Manage<br />Through a network of third party partners we will source and manage <br />Data Enhancement<br />Data Cleaning<br />Database Design & Build<br />List Purchase<br />Data Collection<br />Processing Data<br />Data Monetisation <br />Web Analytics<br />
  18. 18. Who We Are<br />A data planning & analytics consultancy<br />Based in Yorkshire<br />5 core team members with a network of associate consultants & partners<br />Working in the private and public sectors<br />Part of the Journey Group<br />
  19. 19. Peter Rivett-Jones - Director<br />20 years of data and marketing experience<br />Senior client services and planning positions in top DM agencies including Joshua, GGT Direct & EWA<br />Founded DM agency Made With Love (MWL) in 1999 which was later sold to Chemistry in 2003<br />Joined Poulters as Director & Shareholder in 2005 heading up all data and direct marketing accounts <br />Co-founded The Data People in 2009<br />
  20. 20. Steve Raper - Director<br />A statistician with 25 years of data analysis and marketing experience<br />Started career with British Gas in various sales and marketing positions<br />Went agency side in 1994 as Data Manager for Bedrock Communications <br />independent consultant since 1996 providing data strategy & data analysis for agencies and clients<br />Co-founded The Data People in 2009<br />
  21. 21. What Makes Us Different?<br /><ul><li>We are marketeers first and data planners second
  22. 22. We turn numbers into words and pictures.
  23. 23. We answer the "so what?" of data and statistics
  24. 24. We have vast experience in data and all its touch points
  25. 25. We are independent consultants with nothing to sell apart from our time
  26. 26. We turn the complexity of data into strategies that make sense
  27. 27. We champion simplicity </li></li></ul><li>Sector Experience<br /><ul><li>NHS & Health
  28. 28. FMCG
  29. 29. Automotive
  30. 30. Industrial
  31. 31. B2B
  32. 32. Travel & Tourism
  33. 33. Airlines
  34. 34. Government</li></ul>Retail <br />Leisure<br />Office Equipment<br />Telecoms<br />Financial Services<br />Mail Order<br />Utilities<br />Drinks<br />
  35. 35. Case Study 1<br />Alliance & Leicester<br />
  36. 36. The Brief<br />Alliance & Leicester had been using cold contact lists to direct potential customers to their web site, with limited success<br />Registered users of the site were segmented by answers to basic financial questions only upon registration<br />Communications to registered users had minimal tailoring<br />With results from nearly 2 years’ activity now available, our brief was to optimise results – <br />Increase visits to the site from dm activity<br />Maximise the potential value of visitors to the site<br />
  37. 37. The Solution<br />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<br />CHAID modelling based on each set of overlays was carried out and gains charts compared to improve targeting<br /> The client’s registered user base was segmented in terms of their long-term behaviour in relation to the site<br />The resulting 6 clusters were profiled in terms of their likely financial requirements and long-term value potential<br />The rules for optimum allocation to segments were modelled using discriminant analysis<br />
  38. 38. The Solution<br />Data Categories<br />Transaction, basket & tree analysis<br />Drill-down Charts& Tables<br />2, 3, 4 & 5 wayVenn Charts<br />A series of new questions at registration were identified to give the client data to allocate the new user immediately to the appropriate segment<br />
  39. 39. The Results<br />There was an immediate increase of over 100% in site visits generated from direct mail through the improved targeting<br />Value models within the segmentation allowed the client to estimate long-term potential value<br />Thus determining the products advertised and marketing investment for each segment<br />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<br />
  40. 40. Case Study 2<br />Holmes Place<br />
  41. 41. The Brief<br />Like many of its competitors, Holmes Place concentrated on acquisition during the unprecedented growth phase of the industry<br />Customer retention and improved targeting for acquisition were recognised as important business drivers as:<br />competition increased <br />cost of acquisition increased<br />attrition rates exceeded 50% per annum<br />Little was known about the customer, and no estimates of customer value and what drives it had been evaluated<br />The brief was to understand the customer better to allow for smarter and more efficient marketing activity<br />
  42. 42. The Solution<br />The first step was to take the client’s membership and transaction databases and combine them<br />Append demographic and lifestyle information<br />Identify valuable customers through data modelling – including length of membership and additional spend (e.g. personal training)<br />Profiles for each club by value band were compiled<br />Key variables – transactional and lifestyle - for predicting closure of membership were identified<br />The resulting churn model was applied to the customer base to predict the likelihood of attrition <br />
  43. 43. The Solution<br />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<br />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<br />
  44. 44. The Results<br />Targeting for new customers has been revitalised <br />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<br />Costs per new member have been reduced<br />Average value of each new member acquired was increased<br />Early indications are that the modelling of likely defectors, coupled with communications designed to retain them, is starting to reduce churn rates<br />
  45. 45. Case Study 3<br />Nescafe<br />
  46. 46. The Brief <br />A major development in the Nescafe Ultra Premium brand strategy was to narrow the target audience that for marketing communications<br />Extensive work by the brand team had re-defined the audience that Nescafe UP would target<br />Two target audiences called Roast & Ground Dippers and Instant Dippers had been identified – c1.7m HH’s<br />The brief was how, from a data perspective, do we find this audience to allow a major dm sampling campaign to take place<br />
  47. 47. The Solution<br />Nescafe did not have marketing data of their own<br />There was not sufficient volumes of external data to purchase that identified ‘dipping’<br />In order to get the quantity and quality of data needed we proposed data modelling<br />In simple terms, this meant creating a profile of the people we wanted and then finding lookalikes<br />The secret lay in having the most accurate profile at the start<br />
  48. 48. The Solution<br />We recommended using Tesco Clubcard data to create the profile that the data model would be built around<br />The model were built using CHAID and then applied to external lifestyle data sources <br />
  49. 49. The Results<br />The data model used in the direct marketing campaign proved to be highly successful<br />The mailing delivered £280k uplift in the first three months alone<br />The mailing had an impact on customers behaviour resulting in sustained change over a year – once customers had tried it they remained loyal<br />Customers moved from the targeted product areas of Freeze Dried and R&G proving the model’s accuracy<br />At a brand level customers were most likely to have moved from Kenco Ultra Premium and other Premium freeze dried coffees<br />
  50. 50. The Data People turn customer data into greater profits<br />
  51. 51. Thank You<br />

×