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Designing for Context

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The chance to design for 43 different languages and 200+ countries comes with the challenge of balancing a lot of information per pixel. Booking.com gets millions of users everyday with different needs and goals, and designers need to understand how to best approach them without bringing more complexity to the product. In this talk we will go through how Booking.com deals with that problem using design to best leverage Machine Learning with personas, user journeys and AI to build a personalized experience to our customers.

The difference between designing for everyone and designing for context is in the understanding of who your customers are, their intent with your product and what can we do to anticipate their needs without disrupting the experience. Data Science is a powerful tool to get to that goal, but without a well thought of design behind it the intended experience will probably not be fulfilled.

Published in: Design
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Designing for Context

  1. 1. DESIGNING FOR CONTEXT Maria Klökner @marialigiak
  2. 2. oi
  3. 3. oi
  4. 4. hi.
  5. 5. countries 220+
  6. 6. properties 1.4 mi
  7. 7. languages 43
  8. 8. employees 15k
  9. 9. empathy how the machine learns where is machine learning UX + ML UX + ML at booking.com
  10. 10. empathy
  11. 11. Empathy is patiently and sincerely seeing the world through the other person’s eyes. It is not learned in school; it is cultivated over a lifetime." Albert Einstein
  12. 12. machine learning
  13. 13. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
  14. 14. machine learning spotify
  15. 15. machine learning netflix
  16. 16. machine learning google’s self driving car
  17. 17. machine learning skin cancer detection
  18. 18.  Any sufficiently advanced technology is indistinguishable from magic " Arthur C. Clarke
  19. 19. machine learning e.t.
  20. 20. machine learning what is an apple? apple
  21. 21. machine learning not an apple what is an apple?
  22. 22. machine learning apple what is an apple?
  23. 23. machine learning what’s an apple? not an apple
  24. 24. machine learning
  25. 25. machine learning apple!
  26. 26. machine learning
  27. 27. machine learning nah
  28. 28. machine learning nah
  29. 29. color red/green texture Shape round taste
  30. 30. apple color red/green texture Shape round taste
  31. 31. training set apple color red/green texture Shape round taste
  32. 32. training set features apple color red/green texture Shape round taste
  33. 33. training set features label apple color red/green texture Shape round taste
  34. 34. training set features label
  35. 35. training set features label dates platform time of day ip …
  36. 36. training set features label date flexibility dates platform time of day ip …
  37. 37. yes is this an apple? models
  38. 38. yes is this an apple? models are you flexible on dates? yes
  39. 39. apple is this an apple, peach or banana? models
  40. 40. apple is this an apple, peach or banana? models are you more price, location or quality sensitive? price
  41. 41. based on this apple, here are others you might like models
  42. 42. based on this apple, here are others you might like models based on the destination you searched for, here are others you might like London paris ?
  43. 43. based on this apple, here are others you might like models based on the destination you searched for, here are others you might like London paris Amsterdam
  44. 44. based on this apple, here are others you might like models based on the destination you searched for, here are others you might like nyc ?
  45. 45. based on this apple, here are others you might like models based on the destination you searched for, here are others you might like nyc Brooklyn
  46. 46. travel world Predict a fact about the future Where do customers travel after going to rio de janeiro? Guess a fact about the present What dates are current visitors likely to travel on? Automate human tasks Fraud detection, photo tagging
  47. 47. user experience
  48. 48. user experience + machine learning
  49. 49. user experience + machine learning
  50. 50. patterns
  51. 51. patterns user experience
  52. 52. patterns machine learning
  53. 53. booking.com ml + UX
  54. 54. booking.com
  55. 55. empathy
  56. 56. ML can’t learn empathy because humans are too chaotic, and that’s where UX can help, by bringing the human empathy and chaos to the organized, patterned thinking of a machine.
  57. 57. THANK YOU Maria Klökner @marialigiak workingatbooking.com

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