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Going beyond recommendations: Where next for data driven design?

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Your phone already knows you better than your family and friends. As AI and machine learning algorithms crunch through the huge datasets we generate our online personas definitely knows you better than you know yourself. What opportunities does this give us to design? How can we design with these datasets to improve users lives beyond just simple recommendations? How do we avoid the pitfalls and shift the boundaries of the new normal highly personalised world?

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Going beyond recommendations: Where next for data driven design?

  1. 1. GOING BEYOND RECOMMENDATIONS @hollielubbock
  2. 2. WHERE NEXT FOR DATA DRIVEN DESIGN?
  3. 3. DATA IS THE FUEL FOR THE NEXT GENERATION OF PERSONALISED SERVICES
  4. 4. 1. DESIGN WITH DATA
  5. 5. 1. DESIGN WITH DATA 2. DESIGN BY DATA
  6. 6. 1. DESIGN WITH DATA 2. DESIGN BY DATA 3. DESIGN FOR DATA MORE INFO 
 HTTPS://PROJECTSBYIF.COM/BLOG/DESIGN-FOR-DATA HTTPS://WWW.SLIDESHARE.NET/FJORDNET/FJORD-EQUINOX-DATA-DESIGN
  7. 7. WHY NOW?
  8. 8. CHANGING EXPECTATIONS
  9. 9. WHAT I WANT, WHEN I WANT IT, HOW I LIKE IT
  10. 10. IWWIWWIWI MORE INFO
 HTTPS://WWW.THEGUARDIAN.COM/FASHI ON/2016/FEB/14/FASHION- INDUSTRY-IN-FLUX-END-OF-THE-RUNWAY-SHOW
  11. 11. FLUID JOURNEYS
  12. 12. IOT ALL THE THINGS
  13. 13. DIGITAL & PHYSICAL CHANNELS HYPER PERSONALISED SERVICES BIG DATA & MACHINE LEARNING + = WE HOPE
  14. 14. PERSONALISATION IN LUXURY FASHION *Completed whilst at Code and Theory
  15. 15. DATA TO INSPIRE SERVICES
  16. 16. THICK DATA BIG DATA WIDE DATA WHO, WHY & HOW
  17. 17. THICK DATA = QUALITATIVE STUDIES BIG DATA = AN EXTREMELY LARGE DATA SET WIDE DATA = INDUSTRY TRENDS & PUBLIC DATA SETS Wide data can also be big data depending on the size of the data set Big data here refers to your product, marketing and customer data
  18. 18. PERSONAL SHOPPER INTERVIEWS STAKEHOLDER INTERVIEWS USER
 INTERVIEWS THICK DATA (QUALITATIVE STUDIES)
  19. 19. THICK DATA (QUALITATIVE STUDIES) VOC SURVEY SHOPPING PATTERNS PERSONAL SHOPPER INTERVIEWS STAKEHOLDER INTERVIEWS USER
 INTERVIEWS BIG DATA (DATA YOU HAVE GATHERED) READING PATTERNS
  20. 20. VOC SURVEY SHOPPING PATTERNS PERSONAL SHOPPER INTERVIEWS INDUSTRY TRENDS SOCIAL HABITSCOMPETITOR ANALYSIS STAKEHOLDER INTERVIEWS SEARCH TRENDS USER
 INTERVIEWS WIDE DATA
 (INDUSTRY TRENDS, COMPETITOR ANALYSIS DIRECT INDIRECT AND PERCEPTUAL, SEARCH TRENDS AND SOCIAL MEDIA HABITS) READING PATTERNS THICK DATA (QUALITATIVE STUDIES) BIG DATA (DATA YOU HAVE GATHERED)
  21. 21. THE INSIGHT WH O N OW
  22. 22. THE INSIGHT WHO NEXTWH O N OW
  23. 23. THE INSIGHT WHAT TO SAYWHO NEXTWH O N OW
  24. 24. THE INSIGHT HOW TO SAY ITWHAT TO SAYWHO NEXTWH O N OW
  25. 25. THE INSIGHT WHE RE AND WHENHOW TO SAY ITWHAT TO SAYWHO NEXTWH O N OW
  26. 26. BEHAVIOURAL MODES DATA ENHANCED
  27. 27. CLOTHES ARE FUNDAMENTAL TO USERS'S SENSE OF IDENTITY. FASHION BUYING BEHAVIOURS SHIFT DEPENDING ON THE USERS CONTEXT.
  28. 28. CONTENT FRAMEWORK
  29. 29. CONTENT THAT SUPPORTS & INSPIRES FASHION CONFIDENCE AND DRIVES COMMERCE
  30. 30. PUBLISHING MODEL
  31. 31. WHEN TO PUBLISH Typical Weekday Typical Weekend SOCIAL MEDIA BUYING PEAKS
  32. 32. PERSONALISATION AS UNIQUE AS THEIR CUSTOMERS https://econsultancy.com/blog/68219-four- things-brands-can-learn-about-content-marketing- from-net-a-porter
  33. 33. DATA DRIVEN STRATEGY FOR PERSONALISATION, CONTENT CREATION & DISTRIBUTION
  34. 34. WHO IS DOING IT WELL?
  35. 35. GOOGLE
  36. 36. HAS GONE FROM SIMPLY ORGANISING THE WORLDS INFORMATION TO HARNESSING IT
  37. 37. NETFLIX
  38. 38. PERSONALISED RANKING, TOP-N RANKING, TRENDING NOW, CONTINUE WATCHING, VIDEO - VIDEO SIMILARITY, PERSONALISED PAGE GENERATION, SEARCH, PERSONALISED IMAGE SELECTION
  39. 39. DATA DRIVES THE PRODUCT
 & 
 THE CONTENT
  40. 40. THE CHALLENGES
 WHY HAVEN’T WE ALL GOT THERE?
  41. 41. GETTING THE DATA
  42. 42. DON’T UNDERSTAND HOW PERSONAL INFORMATION IS USED 92% DO NOT TRUST ORGANISATIONS TO USE DATA RESPONSIBLY 57% http://www.bbc.co.uk/news/business-37476335
  43. 43. OFTEN COMBING MULTIPLE SOURCES OF DATA GIVE FAR MORE INSIGHT THAN ONE SET ALONE
  44. 44. UNDERSTANDING THE DATA
  45. 45. “ALL DATA IN AGGREGATE IS CRAP.” GOOGLE ANALYTICS EVANGELIST AVINASH KAUSHIK
  46. 46. MACHINE LEARNING LOVES 
 FINDING PATTERNS
  47. 47. DESIGNING IN THE INFINITE
  48. 48. MACHINES WILL INCREASINGLY BE MAKING DECISIONS WITHIN USER EXPECTATIONS
  49. 49. “HUMAN-CENTERED DESIGN HAS EXPANDED FROM THE DESIGN OF OBJECTS (INDUSTRIAL DESIGN) TO THE DESIGN OF EXPERIENCES (ADDING INTERACTION DESIGN, VISUAL DESIGN, AND THE DESIGN OF SPACES) AND THE NEXT STEP WILL BE THE DESIGN OF SYSTEM BEHAVIOR: THE DESIGN OF THE ALGORITHMS THAT DETERMINE THE BEHAVIOR OF AUTOMATED OR INTELLIGENT SYSTEMS,” HARRY WEST AT FROG
  50. 50. POSSIBLE CONSEQUENCES
  51. 51. WHAT IF? WHAT IF EVERYONE IN THE WORLD USED YOUR SERVICE HOW WOULD IT CHANGE SOCIETY? WHAT IS THE WORST THING THAT COULD HAPPEN IF IT GOES WRONG?
  52. 52. THE 5 THINGS TO REMEMBER
  53. 53. 1. DATA DOESN'T = INSIGHT 2. MATCH THICK DATA WITH BIG DATA & WIDE DATA 3. SET UP A DATA / MEASUREMENT STRATEGY 4. HYPOTHESISE & TEST (RIGHT DATA, RIGHT TIME) 5. BE TRANSPARENT & RESPONSIBLE WITH DATA 5 THINGS TO REMEMBER @hollielubbock
  54. 54. https://www.katepugsley.com/

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