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Data Web Marketing

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.

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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