MUXL was proud to present its third edition of the annual Mobile UX London Conference - https://mobileuxlondon.com 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 become to know you better than you know yourself. What opportunities does this give us for design? How can we design with these dataset to improve users lives beyond just simple recommendations. How do we avoid the pitfalls and shift with the boundaries of the new normal highly personalised world?

1. #MUXL
MOBILEUXLONDON.COM

2. Foundation Course
in UX & UCD
Evening Course
8 Weeks
Intermediate Course
in UX
Daytime Course
6 Weeks
Designing a
Chatbot
Evening Course
6 Weeks
In Person Training, Shoreditch
Prices from £850
Small Class Sizes of 10
Taught by Industry Leading Practitioners

3. GOING BEYOND
RECOMMENDATIONS
@hollielubbock

4. WHERE NEXT FOR DATA
DRIVEN DESIGN?

6. DATA IS THE FUEL FOR THE
NEXT GENERATION OF
PERSONALISED SERVICES

7. 1. DESIGN WITH DATA

8. 1. DESIGN WITH DATA
2. DESIGN BY DATA

9. 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

10. WHY NOW?

11. CHANGING EXPECTATIONS

12. WHAT I WANT, WHEN I
WANT IT, HOW I LIKE IT

13. IWWIWWIWI
MORE INFO
HTTPS://WWW.THEGUARDIAN.COM/FASHI ON/2016/FEB/14/FASHION-
INDUSTRY-IN-FLUX-END-OF-THE-RUNWAY-SHOW

14. FLUID JOURNEYS

15. IOT ALL THE THINGS

16. DIGITAL &
PHYSICAL
CHANNELS
HYPER
PERSONALISED
SERVICES
BIG DATA &
MACHINE
LEARNING
+ =
WE HOPE

17. PERSONALISATION IN
LUXURY FASHION
*Completed whilst at Code and Theory

18. DATA TO INSPIRE SERVICES

19. THICK DATA
BIG DATA WIDE DATA
WHO, WHY & HOW

20. 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

21. PERSONAL SHOPPER
INTERVIEWS
STAKEHOLDER
INTERVIEWS
USER
INTERVIEWS
THICK DATA
(QUALITATIVE STUDIES)

22. THICK DATA
(QUALITATIVE STUDIES)
VOC SURVEY SHOPPING
PATTERNS
PERSONAL SHOPPER
INTERVIEWS
STAKEHOLDER
INTERVIEWS
USER
INTERVIEWS
BIG DATA
(DATA YOU HAVE GATHERED)
READING PATTERNS

23. 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)

24. THE INSIGHT
WH O N OW

25. THE INSIGHT
WHO NEXTWH O N OW

26. THE INSIGHT
WHAT TO SAYWHO NEXTWH O N OW

27. THE INSIGHT
HOW TO SAY ITWHAT TO SAYWHO NEXTWH O N OW

28. THE INSIGHT
WHE RE AND WHENHOW TO SAY ITWHAT TO SAYWHO NEXTWH O N OW

29. BEHAVIOURAL MODES
DATA ENHANCED

30. CLOTHES ARE FUNDAMENTAL TO USERS'S SENSE OF
IDENTITY. FASHION BUYING BEHAVIOURS SHIFT
DEPENDING ON THE USERS CONTEXT.

31. CONTENT FRAMEWORK

34. CONTENT THAT SUPPORTS &
INSPIRES FASHION
CONFIDENCE AND DRIVES
COMMERCE

35. PUBLISHING MODEL

36. WHEN TO PUBLISH
Typical Weekday
Typical Weekend
SOCIAL MEDIA
BUYING PEAKS

37. PERSONALISATION AS
UNIQUE AS THEIR
CUSTOMERS
https://econsultancy.com/blog/68219-four-
things-brands-can-learn-about-content-marketing-
from-net-a-porter

38. DATA DRIVEN STRATEGY
FOR PERSONALISATION,
CONTENT CREATION &
DISTRIBUTION

39. WHO IS DOING IT WELL?

40. GOOGLE

41. HAS GONE FROM SIMPLY
ORGANISING THE WORLDS
INFORMATION TO
HARNESSING IT

43. NETFLIX

44. PERSONALISED RANKING, TOP-N
RANKING, TRENDING NOW, CONTINUE
WATCHING, VIDEO - VIDEO SIMILARITY,
PERSONALISED PAGE GENERATION,
SEARCH, PERSONALISED IMAGE
SELECTION

45. DATA DRIVES THE PRODUCT
&
THE CONTENT

46. THE CHALLENGES
WHY HAVEN’T WE ALL
GOT THERE?

47. GETTING THE DATA

48. 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

49. OFTEN COMBING MULTIPLE
SOURCES OF DATA GIVE FAR
MORE INSIGHT THAN ONE
SET ALONE

50. UNDERSTANDING THE
DATA

51. “ALL DATA IN AGGREGATE IS CRAP.”
GOOGLE ANALYTICS EVANGELIST AVINASH KAUSHIK

52. MACHINE LEARNING LOVES
FINDING PATTERNS

53. DESIGNING IN THE
INFINITE

54. MACHINES WILL
INCREASINGLY BE
MAKING DECISIONS
WITHIN USER
EXPECTATIONS

55. “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

56. POSSIBLE
CONSEQUENCES

57. 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?

58. THE 5 THINGS TO REMEMBER

59. 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

60. https://www.katepugsley.com/

61. Foundation Course
in UX & UCD
Evening Course
8 Weeks
Intermediate Course
in UX
Daytime Course
6 Weeks
Designing a
Chatbot
Evening Course
6 Weeks
In Person Training, Shoreditch
Prices from £850
Small Class Sizes of 10
Taught by Industry Leading Practitioners

62. #MUXL
MOBILEUXLONDON.COM