What will marketing be like in the semantic web, the burgeoning new "web of data"? This presentation for the 2009 Semantic Technology Conference outlines a framework of 7 missions for data web marketing.

1. Marketing in the Semantic Web
(“Semantic Marketing” / “Data Web Marketing”)
Semantic Technology Conference
June 16, 2009
Scott Brinker
Marketing Technologist
Email: sbrinker@chiefmartec.com
Twitter: @chiefmartec
Blog: http://www.chiefmartec.com

2. Deafening silence
Sweet sorrow
Controlled chaos
Organized mess
Open secret
Same difference
Civil war
Forward retreat
Living dead
Semantic marketing

3. What will marketing be like
in the semantic web? *
* Depends on your definition of “marketing” and “semantic web”.

4. Official definition of marketing
from the American Marketing Association web site
Marketing is the activity, set of institutions,
and processes for creating, communicating,
delivering, and exchanging offers that have
value for customers, clients, partners, and
society at large.
(Approved October 2007)

5. Informal definition of marketing
from the top of my head
Marketing is what you do to find
and win new customers, grow
your relationships with existing
customers, differentiate yourself
from the competition, and build a
“brand” that helps achieve those
goals.

6. Peter Drucker on marketing
the father of modern management *
Because the purpose of business is to
create a customer, the business enterprise
has two — and only two — basic functions:
marketing and innovation.
* Drucker argued in a 1984 essay that CEO compensation should be no
more than 20 times what the rank and file make — especially at companies
where thousands of employees are being laid off. “This is morally and
socially unforgivable,” he wrote, “and we will pay a heavy price for it.”

7. Marketing is continually evolving.
In recent years, that evolution
has been accelerating — with
more changes ahead.

8. Marketing as a mission
spans the specific tactics
by which it is executed.

9. Marketing tactics,
circa 1900

10. Marketing tactics,
circa 2000

11. 100 years of
progress?

12. Emerging marketing
tactics, circa 2009
$1.4 billion of
SEO in 2008
1,254 APIs and
3,852 mashups

13. Search engine optimization (SEO)
and web APIs for mashups are a
qualitatively different kind of
marketing tactic.

14. Even
across
…previous
shifts in
marketing
medium…
tactics
were
crafted
directly
for
human
consumption.

15. These new tactics are different.
1st order:
Directly crafted for computer
consumption.
2nd order:
Indirectly crafted for human
consumption.

16. This opens the
door to
data web
marketing.

17. What is the semantic web?
from the W3C web site
The Semantic Web is a web of data.
The Semantic Web is about two things.
It is about common formats for
integration and combination
of data drawn from diverse
sources… It is also about
language for recording
how data relates to
real world objects.

18. iro•ny, noun, ˈī rə-
-
nē:
Debating the
meaning of
“semantic web”.

19. What is the semantic web?
3 broad spheres
Document
Disambiguation
Structured Linked
Data Data

20. • Semantic technology that
doesn’t necessarily require
Document publisher cooperation
Disambiguation
• Advances in text analysis for
context and sentiment
• “Semantic advertising”
(popular interpretation)
• Usually invisible to end-user
• Top-down semantic web
• Here today
…no fundamental change to marketing behavior, however.

21. Semantic marketing is about data
—and the spread of that data.
Structured Linked
Data Data

22. Semantic Marketing
=
Data Web Marketing
* Is this a better name for it?

23. Marketing
loves
data.

24. …but it has only flowed in.
• CRM
• Point-of-Sale
• Market research
• Web analytics

25. …really flowed in.

26. Semantic technology can help
organize this data… but again, no
fundamental change to marketing.

27. Data web marketing is
about data flowing out.
A simple yet
revolutionary change
in perspective.

28. Instead of data from the channel…
Data is the
channel.

29. Marketing
will make
data sexy.

30. Why should
marketing lead
the charge for
data web
adoption?

31. Somebody
needs to
consistently
beat the drum
for data web
initiatives.

32. Somebody needs to fund data web
support as an ongoing
commitment.

33. Who has the incentive?

34. If the data web can be
used to:
• help connect to new customers
• strengthen relationships
• differentiate from the competition
• build reputation and brand

35. Who does that align with?

36. Informal definition of marketing
from the top of my head
Marketing is what you do to find
and win new customers, grow
your relationships with existing
customers, differentiate yourself
from the competition, and build a
“brand” that helps achieve those
goals.

37. Marketing is willing
to experiment to
achieve its goals.

38. If marketing can tie the cause-and-
effect of data web initiatives to the
achievement of its objectives…
…there’s sustainable sponsorship.

39. Marketing
success will
cause business
to embrace the
data web.

40. So what exactly
should marketing
doin the dataweb?

41. Missions for
marketing in
the data web.

42. 7 missions of data web marketing:
1. Champion production of data for external consumption.
2. Drive semantic/data branding across the organization.
3. Distribute and promote your data. (SEO++)
4. Convert data web initiatives into business relationships.
5. Track and attribute semantic/data web initiatives.
6. Make your own data mash-ups.
7. Control data quality and protect data/brand standards.

43. #1.
Champion
production
of data for
external
consumption.

44. Not just “brochure data.”

45. Not (primarily) pricing data.

46. Semantic “bargain hunter”
agents are not attractive to
most marketers.
That vision of the
semantic web is
dystopian to
marketers.

47. Marketing is all about avoiding
commoditization.
• Price isn’t everything.
• Only one lowest-cost provider.
• Race to the bottom dynamics.
• Specs aren’t everything.
• Relationships have value.
• Quality matters.
• Context matters.
• Service matters.
• Trust matters a lot.

48. Pricing and product
specifications don’t do
justice to the potential
of sharing data in the
semantic web.

49. Discover data that is
valuable in:
• Domain of your expertise
• Domain of your partners’ expertise
• Domain of your customers’ expertise
• Application of your product/service
• Integration of your product/service
• Benchmarking related results

50. “You should take an inventory of what you
have got in the way of data, and you should
think about how valuable each piece of data
in the company would be if it were available
to other people across the company, or if it
were available publicly, and if it were
available to your partners.”
— Tim Berners-Lee in Talis 2008 interview, answering the question
from a CIO, “what does it mean, what should we do?”

51. Content Data is King

52. Ways to produce valuable data:
• Generate it internally
• Collect it from customers
• Collect it from partners
• License it externally

53. Thinking about
this kind of
data is hard —
because it’s
not been done
before.
But that’s the
opportunity.

54. Hypothetical example:
Major chain of nurseries producing the leading
reference of plant properties (climate, growth,
soil, water, feeding, compatibility, etc.) —
maybe specialized for a particular region.

55. Hypothetical example:
Marketing software company
aggregates performance data
across customers to offer
real-time industry
benchmarks.
(With permissions of
participants, of course.)

56. Look to
existing
“mash-up”
APIs as
inspiration for
data web
ideas.

57. Goals:
• Become the authoritative source.
• Popularize canonical references
to your products, categories,
competitive dimensions.
• Build reputation,
goodwill,
brand.

58. #2.
Drive
semantic/data
branding
across the
organization.

59. Framing data with the right metadata
— your data brand standards:
• Establish canonical URIs for products, properties.
• Establish the organizing ontologies.
• Determine the ideal granularity of data structures.
• Embrace and extend existing external standards.
• Encourage data linking in the organization (DRY).
• Lobby for standards beyond your organization.
• Maintain and evolve this architecture.

60. Building for
linked data is
an art.

61. Linked data success depends on:
• Consistency
• Logical organization
• Stability
• Trust
Data consumers must be
able to rely upon your data
to use it as a foundation for
their own applications.

62. Ontology
as
Strategy

63. Goal:
Shaping the
conversation in
your market
space.

64. Semantic spin
Framing references
with properties and
nomenclature that
are advantageous
to your brand.

65. Do the names of things
really matter that much?

66. Ask these brands:

67. Our
company is
redefining
the market.
A more literal
interpretation of an
old hype line.

68. First mover
advantage.

69. Authoritative
references generate a
positive feedback loop,
a virtuous cycle.

70. Gender bias in the semantic web?
A kind of semantic branding.
http://www.readwriteweb.com/archives/
will_the_semantic_web_have_a_g.php

71. There are fascinating parallels between
the concept of brands and the semantic
web… but that’s a story for another day.
http://www.chiefmartec.com/2008/03/brand-and-the-s.html

72. #3. Distribute and promote
your data. (SEO++)

73. Just as SEO was about visibility
(and ranking and authority) in the
document web…
…there will be
an analogous
need in the
data web.

74. SEO++
because it’s an
incremental
evolution of
SEO practices
now focused on
data objects.
Alternatives:
Semantic Web Optimization (SWO)
Data Web Optimization (DWO)

75. Spreading your data:
• Build external links to your data
• Link reciprocally to other data
• Increase the findability of your data
• Optimize the format of your data
• Shape and adopt standards
• Promote your data in other channels

76. As with SEO, this
mission will require
continual nurturing.

77. You want to join the data graph.

78. You want your data to be utilized.

79. You want your data to help others
find you.
For example, embedding data into your primary web site, such
as for Google Rich Snippets and Yahoo! Search Monkey.

80. And that’s going to
get harder as more
data comes online.

81. Surface web:
167 terabytes
Deep web:
91,000 terabytes
545-to-one

82. Bridge your
data with others
in semantic
communities
(data networks).
Networks:
• Global
• Vertical
• Private

83. Provide the glue to
connect to these
different networks.
This will probably be a
little messy for a while.

84. A new kind of semantic advertising
Paid inclusion in other
authoritative data networks.
A.K.A.
data advertising

85. Hypothetical example
semantic advertising
(data advertising)
semantic advertising
(paid inclusion)
I just made this idea up,
so whether or not Calais
actually does something
like this is purely
coincidental.
Sponsored
data

86. Goals:
• Include your data in more places
• Get links to your data in more places
• Win more overall visibility/authority

87. Data web marketing services
are a logical evolution for
search agencies.

88. #4.
Convert data
web initiatives
into business
relationships.

89. Information
asymmetry
will still be
alive and
well.
Data web marketing must strategically decide
how much to share, when, and with whom.

90. Some data may be
better harnessed as
an incentive for
other business
goals:
• Become a subscriber
• Become a lead
• Become a partner
• Become a customer
• Become a data buyer

91. A continuum of data access choices.
access if
you pay
value
access if
you qualify
access if
you sign-up
free
access
availability

92. Data for nothing and links for free.
(the SEO++ approach)
Capturing value via
visibility and authority.

93. Restricted “members only” data.
Exchange of value in permission
marketing, or an added benefit to
customers and partners.
Capturing value via lead
generation and customer
acquisition/retention.

94. Data as a direct revenue source.
• Data more pragmatic in standardized format.
• Paid data access as a stand-alone business.
• Paid data access as a “add-on” business.
Capturing value
the old-fashioned
way: people pay
for it.

95. Balancing the
trade-offs in
data value
capture is a
marketing
decision.

96. Multiple “data packaging” options.
The same underlying data may be
packaged differently depending on
access level:
• Granularity of data.
• Depth of data.
• Breadth of data.
• “Freshness” of data.

97. Goal:
Harness the
value in
your data.

98. #5.
Track and
attribute
semantic/dataw
eb initiatives.
“data web analytics”

99. How do you measure the
success of semantic
web/data web initiatives?
What are the right metrics?

100. Different than web analytics because…
…clients are not necessarily browsers.

101. Cookies—a staple of web analytics—may
not be as prevalent in data access tracking.

102. Referrer—a staple of web analytics—may
not be as prevalent in data access tracking.

103. Tracking is also
going to be hard
due to mashing
and caching.
How is data used
and redistributed
once someone gets
it from you?

104. Count subscribers to data feeds
or visits to URIs.
• Measures 1st-order reach.
• Measures frequency of access.
• Measures new vs. repeat access.
• But maybe limited to IP address.

105. Time-sensitive or frequently updated data
is one way to encourage more visits to
gauge usage.

106. Count inbound links to your URIs.
• Measure authority (DataRank).
• Measure findability.
• Limited by who indexes you.

107. New methods of
tracking and
attribution?
Particularly
among
cooperative
parties?

108. Inspiration from Daniel Weitzner (MIT) and the policy-aware
web (PAW).

109. Goals:
• Discovering what data is popular.
• Discovering
who your data
audience is.
• Discovering how
your audience
uses that data.
• Keeping track of competitors
and comparable benchmarks.

110. #6.
Make
your own
data
mashups.

111. Data web fluency is something
you learn by doing.
You must use data to understand how to use data.

112. Applications and mash-ups are where
data surfaces into the visible web.

113. Leveraging data in
your own value-add
mash-ups.
• For prospects
• For customers
• For partners
• For internal use
mashable
marketing

114. Mash-ups for prospects
and customers.
To assist, educate,
entertain, orinform.

115. Mash-ups beyond Google Maps.

116. Mash-ups for internal use.
• Market research
• Customer monitoring
• Marketing operations

117. Competitive intelligence mash-ups.
Uncovering the pros and cons of
data web marketing.

118. Opportunities for “joint venture”
data web initiatives — your
chocolate with someone else’s
peanut butter (exclusively?).

119. Goals:
• Cool applications for your customers.
• Advanced your own internal operations.
• By doing the above, better understand
how data is consumed
to be better at
producing
it.

120. #7.
Control data
quality and
protect
data/brand
standards.
“semantic police”

121. Data web marketing won’t be magic.
• Coordination challenges with distributed data management.
• Rules about what can be shared, when and with whom.
• Maintaining the accuracy of data (i.e., data entropy).
• Refereeing conflicting data silos coming together.
• Enforcing data brand standards.

122. Legal
questions:
Do we have the right
to share certain data?
What are the liabilities
from sharing data?
Does sharing certain data
constitute a risk to our
intellectual property?

123. “Marketing”
Determining
how much data
to share…
…or not to share.
“Legal”

124. Agreement on data standards may
be contentious among stakeholders.

125. When data was in
silos, inherently fewer
conflicts. As data web
marketing grows, this will
become a larger issue.

126. Remove bad or
expired data.
It’s much more unattractive
when the public has come in
for U-pick-it data.

127. Detect misuse
and data theft.

128. Goal:
Achieve balance between
openness vs.
protection, distributed vs.
controlled, standardized
vs. loosely-coupled data
relationships.

129. 7 missions of data web marketing:
1. Champion production of data for external consumption.
2. Drive semantic/data branding across the organization.
3. Distribute and promote your data. (SEO++)
4. Convert data web initiatives into business relationships.
5. Track and attribute semantic/data web initiatives.
6. Make your own data mash-ups.
7. Control data quality and protect data/brand standards.

130. Web 3.0 = Data Web 1.0

131. A more technical future for marketing?
A role for “marketing technologists”
in the organizational DNA of the
marketing department & agencies.
The new leaders of
data web marketing?

132. Thank you for running this
marathon presentation with me.
Scott Brinker
Marketing Technologist
Email: sbrinker@chiefmartec.com
Twitter: @chiefmartec
Blog: http://www.chiefmartec.com