Pedro Marques is a designer with over 10 years experience who sees design as a discipline under software engineering. As a Product Designer at Booking.com, he spends a lot of time working on machine learning. At UXDX Amsterdam, he shares insight on the what, why & hows behind prediction, machine learning and research.

1. Predictive Design
Pedro Marques
Product Designer @ Booking.com
@pmarques

3. Design,right?
- Dad

4. Design,right?
- Dad
02:47
- Mom on WhatsApp

6. What is Booking.com?
HQ in Amsterdam
15k staff
1.4m rooms per day
1.3m properties
150+ designers
We test everything
@stuartfrisby Building a test culture https://youtu.be/_sx5LV23hIE

7. Prediction
Machine Learning
Research

8. Prediction
Machine Learning
Research
WHAT

9. Prediction
Machine Learning
Research
WHAT
WHY

10. Prediction
Machine Learning
Research
WHAT
HOW
WHY

11. What?
Prediction?

12. What?
A prediction (Latin præ-, "before,"
and dicere, "to say"), or forecast, is a
statement about an uncertain event.
“
“

13. What?
Predicting
Anticipating

14. What?
Learning
Predicting
Anticipating

15. What?
Learning

16. What?

20. Machine Learning

21. Magic

22. Magic

23. Do the entire work

24. Do the entire work

26. Apple

28. Not an apple

30. Not an apple

32. Apple

37. Training Set

38. Color
Training Set

39. Color
Red/Green
Training Set

40. Color
Texture
Red/Green
Training Set

41. Color
Texture
Shape
Red/Green
Training Set

42. Color
Texture
Shape
Red/Green
Round
Training Set

43. Color
Texture
Shape
Red/Green
Round
Training Set Features

44. Color
Texture
Shape
Red/Green
Round
Training Set Features

45. Color
Texture
Shape
Red/Green
Round
Apple
Training Set Features

46. Color
Texture
Shape
Red/Green
Round
Apple
Training Set Features Label

47. Price sensitive
Location sensitive
Date Flexible

48. Why

49. Why
Touch points

50. Why
Friction Points

51. How

52. 5

53. Data
Driven
Data
InformedX

54. Data Driven
You might end up optmizing the wrong thing
[…] not everything is an
optimization problem.
- Andrew Chen

55. Data is one of the tools
It helps filling the gaps
Hello! We’re from Booking.com and we’d love 10–15' of your time to get your feedback about our website. We’ll
provide you with a phone and you don’t have to really book an actual hotel—we just want to see how you use our
site and how we can help improve it.

56. Data Informed
You don’t know the full story
Quantitative
A little instinctQualitative

57. Local Maximum
Global Maximum

58. We practice empathy

59. We talk to our customers
Online Survey Tools
Street level user testing
Usability tests
User research
Diary studies

60. We talk to our customers
Online Survey Tools
Street level user testing
Usability tests
User research
Diary studies

61. We talk to our customers
Online Survey Tools
Street level user testing
Usability tests
User research
Diary studies

63. Patterns

64. Customer clicks on X
Customer hovers Y
Customer buys product

65. Training set Features Label
Dates
Location
Past reservations
Bounces
Avg time on page
Destinations
…
Date flexible
Destination Flexible
Quality aware
Price sensitive
Booking.com

66. Real world examples

71. How can I do it?

72. Track EVERYTHING

73. Find patterns

74. Talk to your customers

75. See beyond your product

76. Be human.

77. Technology is Heartless

81. I didn’t go looking for grief this afternoon,
but it found me anyway, and I have
designers and programmers to thank for it.
— Eric Meyer

82. pedromarques.io
@pmarquees
hey@pedromarques.io