Presented at ClickZ Live in Hong Kong, 2014.

1. Smart Analytics And
Metrics For Success
How to Monetize Customer Experience

2. 2
Agenda
Our perspective
The case for experience
How data works
Using data more effectively
Tools

3. Our perspective

4. 4
Kristin Low

5. 5
Agenda
Who
The case for experience
Analysis
Future Patti Hunt

6. 6
Community & Products
Over 800 members across 5
countries in Asia.
COMMUNITIES EVENTS
Creating 100 startups in 5
cities across Asia.
Helping startups in Hong
Kong, Shenzhen & Singapore

7. 7
We build our own products
Clean home. Clear conscience.
http://www.fairmaid.co
A marketplace for unique short-term
spaces.
http://www.whitespaces.co

8. The case for experience

10. “For most of us, design
is invisible. Until it fails.”
- Bruce Mau

11. 11
The only designers in the room?
Making people want things
vs.
Making things people want

12. 12
1.795%

13. 13
1.795%
http://www.mobify.com/insights/2014-mobile-conversion-rate-benchmarks/

14. 14
1.795%
The average conversion rate for mobile usage on e-commerce
websites across all categories for 2013.
http://www.mobify.com/insights/2014-mobile-conversion-rate-benchmarks/

15. 15
1.795%
http://www.mobify.com/insights/2014-mobile-conversion-rate-benchmarks/
As marketers, we spend a lot of
our life trying to make this
number go up.

16. 16
SAG

17. 17
SAG
98.205%

18. 18
98.205%
1.795%
Because this is broken.

19. 19
98.205%
1.795%
As designers, we look at this
other number.

20. 20
98.205%
1.795%
Design is about solving problems.

21. 21
Creating effective experiences means
addressing the service which underpins them.

22. 22
Experience is the perception of the
service we deliver.

23. 23
Service businesses are one of the largest
sectors in most developed economies.

24. 24

25. 25

26. 26
Are our priorities right?
$20 $1
http://www.adaptivepath.com/ideas/serious-service-sag/
Keeping

27. promises
Making

28. promises

29. 27
We should spend more on keeping the promises we make.
98.205%
But we spend an awful lot on 1.795%
making them.
We spend very little on actually
keeping our promises.

30. 28
This creates the Service Delivery Gap
The Service Delivery Gap: What companies think versus
whether they think their customers will agree.
80
60
40
20
0
14
80
Companies that believe they
provide an above-average
customer experience.
Companies who think their
customers agree.
https://www.linkedin.com/today/post/article/20140602114924-13943480-the-service-delivery-gap-you-are-missing

31. 29
This creates the Service Delivery Gap
The Service Delivery Gap: What companies think versus
whether they think their customers will agree.
80
60
40
20
0
14 11
80
Companies that believe they
provide an above-average
customer experience.
Companies who think their
customers agree.
https://www.linkedin.com/today/post/article/20140602114924-13943480-the-service-delivery-gap-you-are-missing
And

32. also

33. what

34.
customers

35. think!
Companies who ’s customers
agree.

36. How data works

37. 31
SAG

38. 32
SAG

39. 33
SAG
Most businesses don’t know
how to use data.

40. 34
SAG
“The point of data is intimacy.”
- Roger Martin

41. 35
It’s worth repeating.
Data can be qualitative and quantitative.

42. 36
It’s worth repeating.
Data can be qualitative and quantitative.
should

43. 37
All data is subjective.
It’s worth repeating.

44. 38
It’s worth repeating.
Data won’t make decisions for you.

45. 39
Data helps you do two things: discover
and validate.
It’s worth repeating.
Analysis
Synthesis

46. Using data more effectively

47. 41
Understand the difference between data
metrics.
Using data more effectively

48. 42
Understand the difference between data metrics
• Good metrics are comparative, such as a ratio or rate
• Good metrics are understandable
• Good metrics change the way you behave
• Good metrics should hint at what you need to do next

49. 43
Understand the difference between data metrics
This is not a metric.

50. 44
Understand the difference between data metrics
“Listings per seller for
the past 90 days.”
This is a metric.
Based

51. on

52. a

53.
time

54. period
Ratio
Referencing

55. a

56.
causal

57. relationship

58. 45
Understand your research toolkit
Market
Research
Design
Research
Learning what people like Learning what people do

59. Design Research (the approach taken for this project) relies primarily on qualitative research techniques to discover user
needs, which may then be validated using more quantitative research techniques.
Design Research
(Qualitative)
Market Research
(Most Often Quantitative)
Purpose To discover what is unknown, uncovering potential
opportunities and risks to increase chances of success.
To analyze and understand what is already known with a view to
optimizing success.
Investigative technique Researcher may only know roughly in advance what he/she is
looking for.
Researcher knows clearly in advance what he/she
is looking for.
Research tools Researcher is the data gathering instrument.
Researcher uses tools, such as questionnaires or equipment to
collect numerical data.
Data type used
Data is in the form of words, pictures or objects
(or a combination).
Data is in the form of numbers and statistics.
When to use? Recommended during earlier phases of research projects. Recommended during latter phases of research projects.
Table adapted from: http://wilderdom.com/research/QualitativeVersusQuantitativeResearch.html
46
Understand your research toolkit

60. 47
Research
Discover
Experiment
Operationalize

61. Tools

62. 49
Inspectlet Discover

63. 50
Inspectlet Discover
Heatmaps Session
Recording
Form
Analytics

64. 51
Optimizely Experiment

65. 52
Optimizely Experiment

66. 53
Geckoboard Operationalize

67. 54
Geckoboard Operationalize

68. 55
Bonus tools

69. www.on-off.com.hk
User Experience Design | Product Service Design | Business Model Innovation