My keynote to Econsultancy's Future of Digital Marketing (#fodm) 2009 conference. I take as my theme the contention that marketing's entering an 'age of magic', based upon a data/interaction model which goes beyond 'phenomena' to 'epiphenomena' - giving the appearance of magic. I then cover some of the challenges this gives in ecommerce and retail: the challenge to the store and point of sale in particular. Then I introduce the "Obama-Preedy Pricing Principle" (yes, there's an article coming to fully explain this...) and finish by proposing an encompassing KPI for this multichannel, multi-modal etail world: profit per engagement-second. The keynote was given to 350 delegates in London on 17 June 2009. http://econsultancy.com/events/future-of-digital-marketing

1. Magic
Making and measuring marketing magic
Econsultancy’s “Future of Digital Marketing”, London, June 2009
www.ianjindal.com

2. Future of Digital Marketing
1.Previous Futures
2.Epiphenomenology and Magic
3.Point of Sale
4.Obama-Preedy Pricing Principle
5.Metrics

3. Previous Futures
2006 - “Web 2.0”
“The future’s already
here...”
“The future’s already here: it’s just not
evenly distributed”

4. Previous Futures
2007 - “Harnessing
Social Media”
The “P” word...
Mobile from Korea...

5. Previous Futures
2008 - “Meta”
“Data, metadata,
attention”
“The year of mobile”.
Really.
“Meta”
The Future of Digital Marketing: London, June 2008
www.ianjindal.com
© Ian Jindal, contents and images unless noted.

6. Future Fatigue - now what?
• Still busy with Web2.0!
“The future’s already here: it’s just not
evenly distributed”
• Inwardly-focused
• Tools, cranking handles, over-
specificity.
But - a new synthesis heralds an
‘Age of Magic’
“Meta”
The Future of Digital Marketing: London, June 2008
www.ianjindal.com
© Ian Jindal, contents and images unless noted.

7. Epiphenomenology. It starts with data

8. Data: We need a framework!
Hyperdata
Metadata
Data
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

9. Data: We need a framework!
Hyperdata
Metadata
Data Business
analysis
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

10. Data: We need a framework!
Hyperdata
Metadata
Data Business
Services
analysis
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

11. Data: We need a framework!
Hyperdata
Metadata
Data Business Mashups
Services
analysis
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

12. Data: We need a framework!
★ Metadata
– Segmentation
and metadata for
increased
relevance.
– “Behaviour” is
useful metadata -
if you can capture
it - ‘ignore ratio’?
– Capture offline as
well as online -
warranties...

13. Data: We need a framework!
Hyperdata
Metadata
Data Business Mashups
Services
analysis
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

14. Data: We
4. Data need a framework!
Hyperdata Web2.0
Metadata
Data Business Mashups
Services
analysis
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

15. Data: We need a framework!
Hyperdata Web2.0
Metadata
Data Business Mashups
Services
analysis
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

16. Towards Epiphenomenology. No longer your father’s data

17. Data
Hyperdata Web2.0 Social
Metadata Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

18. Data
Hyperdata Web2.0 Social
APML
Metadata Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata Microformats
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

19. Data
• “Doing” not
“viewing”
– Making Social
Networks
“transactional”
– You can “do”, not
just “view”
– From web to
desktop
– Web to web

20. Data
– Google - hCard
format added to
Search results.
– One click to add
the result to your
address book.
– Could do the
same with
calendar items,
or
recommentations

21. Data
– Attention Profiling Markup Language - www.apml.org
– Beyond ‘personal’: interchange and exchange value of behaviour

22. Data
Is it better than last year?

23. Data
Hyperdata Web2.0 Social
APML
Metadata Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata Microformats
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

24. When the network knows more than anyone

25. When the network knows more than anyone
Hyperdata Web2.0 Social Network
Metadata
Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

26. When the network knows more than anyone
Hyperdata Web2.0 Social Network
Metadata
Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

27. When the network knows more than anyone
• Over 100 million customers profiled
• 40% active across multiple retailers
• 50 billion user-generated reviews
• 4 billion reviews served each month
• 525 retailers (May 2009)
• http://www.texastechpulse.com/bazaarvoice_releases_traffic_stats/s-0021683.html

28. When the network knows more than anyone
Hyperdata Web2.0 Social Network
Predictive
Metadata
Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

29. When the network knows more than anyone
Hyperdata Web2.0 Social Network Phenomena
Predictive
Metadata
Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

30. Predictive? You must need hypodata!

31. Hypodata
Hyperdata Web2.0 Social Network Phenomena
“Brain Scanners can predict a
Metadata
Predictive
decision up to 10 seconds Responsive and
self-configuring
before a person is aware of
Data Business Mashups
Services
analysis
making that decision”
Endodata
Responsive;
Nature Neuroscience, April 2008 ‘agents’
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

32. Hypodata - MIT’s “Sixth Sense”
Hyperdata Web2.0 Social Network Phenomena
Predictive
Metadata
Responsive and
self-configuring
Data Business Mashups
Services
analysis
Endodata
Responsive;
‘agents’
Hypodata
http://www.flickr.com/photos/whiteafrican/3253881037/
Pattie Maes’s TED talk on MIT’s “Sixth Sense”:
0
http://www.youtube.com/watch?v=blBohrmyo-I
1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

33. Hypodata - mashed up
“ABSTRACT
A headphone-type gaze detector for a full-time
wearable interface is proposed. It uses a Kalman
filter to analyze multiple channels of EOG signals
measured at the locations of headphone cushions
to estimate gaze direction. Evaluations show that
the average estimation error is 4.4® (horizontal)
and 8.3® (vertical), and that the drift is
suppressed to the same level as in ordinary EOG.
The method is especially robust against signal
anomalies. Selecting a real object from among
many surrounding ones is one possible
application of this headphone gaze detector.”
http://portal.acm.org/citation.cfm?doid=1125451.1125655 via http://liftlab.com/think/nova/
2006/06/09/wearable-gaze-detector-in-the-form-of-headphones/

34. But what about hyperdata?

35. Hyperdata: Location-specific
Plus gestures, image processing, bio-input...
(heartbeat? Match.com?...).

36. Any sufficiently advanced technology is indistinguishable from magic

37. The Gamut of data
Hyperdata Web2.0 Social Network Phenomena
Metadata
Data
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

38. 6. Next?
Hyperdata Web2.0 Social Network Phenomena
Metadata
Data
Endodata
Hypodata
0 1 3 10 100 1000s+
Number of freely connected nodes where exchange cost is near zero

39. Any sufficiently advanced technology is indistinguishable from magic

40. Any sufficiently advanced technology is indistinguishable from magic
Nodes

41. Any sufficiently advanced technology is indistinguishable from magic
Open Structured Data
Nodes

42. Any sufficiently advanced technology is indistinguishable from magic
ESP?
Epiphenomena
Open Structured Data
Phenomena
Network
Social
Web 2.0
Business
Nodes

43. Enabling epiphenomenology: device integration
• With us everywhere
• Accesses data
• Interlinking
• Connected
• Visualisation
• Touch interface...
• Location (GPS)
• Direction (compass)
• Image processing...

44. Bringing data alive
PlazeMarks
2D codes
Neat, but very “now”

45. Augmented Reality...
Layar.eu - overlay ‘realtor’ data on street scenes

46. Augmented Reality

47. Augmented Reality
• Easy to dismiss as hacked-up
geekery...
• But - contact lenses with LEDs being
developed
• Even without 3D AR, even coloured
‘confirmation lights’ could have impact.
University of Washington, 2008: http://uwnews.org/article.asp?Search=contact+lens&articleid=39094

48. How will this impact retail and eCommerce?

49. Basic level - it already does.

50. Basic level - it already does.
• AR barcode - by
SMS
• Price
comparison?
• Colour options?
• Buy online?

51. Shopping: present, yet elsewhere

52. Shopping: present, yet elsewhere

53. Shopping: present, yet elsewhere

54. Shopping: present, yet elsewhere

55. Shopping: present, yet elsewhere

56. Shopping: present, yet elsewhere

57. Shopping: present, yet elsewhere

58. Shopping: present, yet elsewhere

59. Shopping: present, yet elsewhere

60. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

61. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

62. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

63. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

64. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

65. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

66. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

67. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

68. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

69. Elswhere, yet present
• Oasis
– iPhone App (by
NN4M)
– Good integration
with product
– Newsletter and
storefinder
– Basket... but web
checkout for now
– Just very, very
ordinary and do-
able!

70. Yesterday’s future looks more prescient
• Nearly 2 years
old
• All
components
now exist and
are free
Copyright Icon Nicholson -
http://www.iconnicholson.com/
nrf07/

71. Challenges - some provocations

72. What’s the point of a store?
– “Concept Store” – taking control of the brand image, ambiance and
merchandising
– Training the customer
– Vendor Relationship Management and live prototyping opportunities

73. What’s the point of a store?
– Nappies - should be a subscription product.
Draw for supermarkets, but can’t be great
for Pampers...
– Razors - POS an “experience shave” or “info
point”
– Configuring a sofa: the resulting bar code is
an augmented reality image of the sofa,
with embedded voucher and tracking code
– Service point - think “Collect+”: delivered,
tested, accepted or returned... from
0700-2300 7 days a week

74. Obama-Preedy Pricing Principle
“It’s been a long time coming, but
tonight, because of what we did
1. on this day,
2. in this election,
3. at this defining moment
Change has come to America”
President-Elect Obama, Victory Speech, November 2008
http://news.bbc.co.uk/1/hi/world/americas/7434843.stm

75. Obama-Preedy Pricing Principle
“It’s been a long time coming, but
today, thanks to profit-algorithms and
data,
1. on this day,
2. in this location,
3. for this defining product
4. for this individual customer
Fixed pricing has ended in retail”

76. Obama-Preedy Pricing Principle
• Vouchers - expire when stock reaches 10 or
fewer (eg buy it now)
• Stacked discounts: sale, cashback, card-
holder, end of sale, channel... Why?
• According to availability
• Proximity of alternatives (time and space)
• Time limited: premium for first (eg 3GS),
nephew’s birthday
• Extent to which has shopped around

77. Performance
KPI type What Example
Volume Feed the machine Site visitors, viewers
Margin Extract maximum nutrients. Profit per visit, per session,
Chew well. per episode
Minimise waste Don’t dribble; don’t leave the Minimise out-of-stock visits
stalks
Return on capital employed Calories in > effort to eat. ie Return on inventory, return
“no salad” ;) on equipment, return on staff
Retention Eat again, more easily Customer satisfaction, order
(remember the potato patch) frequency, churn.
With thanks to Michael Ross, eCommera. Bad analogy is not his responsibility though...

78. eCommerce metric
Conversion.

79. eCommerce metric
Sales/Square foot?

80. eCommerce metric
π/εs

81. eCommerce metric
π/εs
Profit per engagement second.
http://www.internetretailing.net/news/editorial-video-on-profit-per-pixel-second

82. eCommerce metric
π/εs • Customer-
focused
Profit per engagement second. • Limits are
attention and
http://www.internetretailing.net/news/editorial-video-on-profit-per-pixel-
second display-space
• Pan-channel
• Commercial
• Cross-functional

83. Taking aim

84. Taking aim - today’s futures
• Unprecedented data, integration and access: challenges our
notions of ‘shop’ and of activity: “time” is the new “place”
• No substitute for wonderful, rich data - it’s the coal to the
industrial revolution. But watch the revolution, not the mine!
• Channibalisation and other conflict is laughable and inept.
• Use of the basics is free: now need retail ‘sparkle’ and
credible personality and products to win: for this customer, at
this time, for this product!

85. Thank you
“Magic”
Making and measuring marketing magic
Econsultancy’s “Future of Digital Marketing”
London, June 2009
www.ianjindal.com
www.internetretailing.net