Data Driven Personas

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This is the tutorial I presented at this year's UPA covering how to do data-driven design research personas.

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Data Driven Personas

  1. 1. Data-Driven Design Research Personas UPA 2007
  2. 2. What. Why. Where.
  3. 3. What is a persona?
  4. 4. “A persona is a user archetype you can use to help guide decisions about product features, navigation, interactions, and even visual design.” - Kim Goodwin, Cooper
  5. 5. Personas are a representative behavior and activity profile for a customer base.
  6. 6. Personas are contextual and specific to the particular application or service.
  7. 7. Why do we have them?
  8. 8. The average user doesn’t exist.
  9. 9. Keeping the you out of user and customer.
  10. 10. What kinds of personas are there?
  11. 11. Marketing & design research
  12. 12. As a design research tool, they are a powerful way to communicate knowledge, activities, interests, influencers, goals, and pain points.
  13. 13. Design research personas can be used for design
  14. 14. Design research personas can be used for research
  15. 15. Design research personas can be used for recruiting
  16. 16. Design research personas can be used for customer service
  17. 17. Design research personas can be used for training
  18. 18. Design research personas are
  19. 19. Design research personas are data-driven
  20. 20. Design research personas are not a set of demographics
  21. 21. Knowledge. Activities. Interests. Influencers. Goals. Frustrations and pain points.
  22. 22. Where does the data come from?
  23. 23. Design research personas include multiple data sources
  24. 24. Both internal...
  25. 25. Key stakeholders, web analytics, and customer support.
  26. 26. ...and external
  27. 27. The customer, customer advocate, and someone you know.
  28. 28. Presenting the Data
  29. 29. Do Te ma in ch no Kn o lo gy wle K n dg ow e le Ex dg pe e In rie te nc e rn al Re se a 1 2 3 4 5 In rch T tra ne ool tS s ea rc Co m m Go h un og ity le Pa ge s Bl og s IM Em ai l Ph Sh o a re ne d Dr ive s Co nf id en ce 1 2 3 4 5 & T Pr od rust uc tiv it y Co m m Re u so Th nit ur y e ce M Sou an ag rce em en t
  30. 30. How do you test and validate your design research personas?
  31. 31. The reality check.
  32. 32. The someone you know check.
  33. 33. Did we have lunch together yesterday?
  34. 34. Have you spoken to them on the phone recently?
  35. 35. Stages of the Persona Life-cycle
  36. 36. Okay, so I have my design research personas. Now what?
  37. 37. Development
  38. 38. Birth
  39. 39. Maturation
  40. 40. Retirement
  41. 41. Common Pitfalls and Mistakes
  42. 42. Marketing vs. research design personas
  43. 43. Large persona sets
  44. 44. Too much personal information
  45. 45. The recycle trap
  46. 46. Not used throughout the design research process
  47. 47. Exercise: Persona Shootout
  48. 48. Michael Do Te ma in ch no Kn o lo gy wle Kn dg ow e le Ex dg pe e rie nc e Knowledge Pr od u 1 2 3 4 5 Ne ct T w ra in P Pr rod ing od uc Al uct t In f te rn Sel o ec at e tio n Co Sel e m pe ctio ns tit iv Ca e In Ac se S tel t ce ss udi or es y Qu Fin Activities and Interest de ot e Bu r ild e Re r po rts 1.0 Highly Seasoned Do Te ma in ch no Kn o lo gy wle Kn dg ow e le Ex dg pe e rie nc e Knowledge Pr od u 1 2 3 4 5 Ne c t T w ra in P Pr rod ing od uc Al uct t In f te rn Sel o ec at e tio n Co Sel e m pe ctio ns tit iv Ca e In Ac se S tel t ce ss udi e or yF s in Qu Activities and Interest de ot e Bu r ild e Re r po rts
  49. 49. Exercise: Segmentation and Patterns
  50. 50. The Hunter Gatherer
  51. 51. Do Te ma in ch no Kn o lo gy wle Kn dg ow e le Ex dg pe e In rie te nc e rn al Re se a 1 2 3 4 5 In rch T tra ne ool tS s ea rc De pa Go h o rtm en gle tP ag es Bl og s IM Em ai l Sh Ph o ar ed ne Dr ive s Co nf id en ce 1 2 3 4 5 & T Pr od rust uc tiv ity Co m m Re u so Th nit ur y e ce M Sou an ag rce em en t
  52. 52. The Worker Bee
  53. 53. Do Te ma in ch no Kn o lo gy wle Kn dg ow e le Ex dge pe rie In te nc e rn al Re se a 1 2 3 4 5 In rch T tra ne ool tS s ea rc De pa Go h o rtm en gle tP ag es Bl og s IM Em ai l Sh Ph on ar e ed Dr ive s Co nf id en ce 1 2 3 4 5 & T Pr od rust uc tiv ity Co m m Re u so Th nit ur y e ce M Sou an ag rce em en t
  54. 54. The Hive Tender
  55. 55. Do Te ma in ch no Kn o lo gy wle Kn dg ow e le Ex dge pe rie In te nc e rn al Re se a 1 2 3 4 5 In rch T tra ne ool tS s ea rc De pa Go h o rtm en gle tP ag es Bl og s IM Em ai l Sh Ph on ar e ed Dr ive s Co nf id en ce 1 2 3 4 5 & T Pr od rust uc tiv ity Co m m Re u so Th nit ur y e ce M Sou an ag rce em en t
  56. 56. The Librarian
  57. 57. Do Te ma in ch no Kn o lo gy wle K n dg ow e le Ex dge pe rie In te nc e rn al Re se a 1 2 3 4 5 In rch T tra ne ool tS s ea rc De pa Go h o rtm en gle tP ag es Bl og s IM Em ai l Sh Ph o ar ed ne Dr ive s Co nf id en ce 1 2 3 4 5 & T Pr od rust uc tiv ity Co m m Re u so Th nit ur y e ce M Sou an ag rce em en t
  58. 58. The Supporter
  59. 59. Do Te ma in ch no Kn o lo gy wle Kn dg ow e le Ex dge pe rie In te nc e rn al Re se a 1 2 3 4 5 In rch T tra ne ool tS s ea rc De pa Go h o rtm en gle tP ag es Bl og s IM Em ai l Sh Ph on ar e ed Dr ive s Co nf id en ce 1 2 3 4 5 & T Pr od rust uc tiv ity Co m m Re u so Th nit ur y e ce M Sou an ag rce em en t
  60. 60. Exercise: Creation
  61. 61. Create personas for a Company Intranet
  62. 62. Create personas for a mobile communication product/service
  63. 63. Don’t forget their Knowledge. Activities. Interests. Influencers. Goals. Frustrations and pain points.
  64. 64. Ready. Set. Go.
  65. 65. Exercise: Presentation and Critique
  66. 66. Data-Driven Design Research Personas UPA 2007

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