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Cs applied v2


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Keynote address for Content Strategy Applied in London, Feb. 9, 2017.

It's all about how IBM is building cognitive content strategies.

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Copyright, James Mathewson, IBM Distinguished Technical Marketer, Content

Published in: Marketing
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Cs applied v2

  1. 1. © 2015 IBM Corporation Cognitive Content Strategy How to use AI to model audience intent James Mathewson IBM Distinguished Technical Marketer @James_Mathewson
  2. 2. Basic communication model in semantics P P A B Assumptions: • No deceit intended • B needs to know P • P is a fact Assumptions: • B trusts A • B is interested in P • B is capable of grasping P marketing marketing marketing marketing marketing marketing
  3. 3. Why search-first content strategy
  4. 4. Google is the best source of information on your target audience
  5. 5. 3,000,000,000,000 queries in 2016
  6. 6. 7 What is audience intent?
  7. 7. 8 Audience intent is what people need to do with the information they seek
  8. 8. Informational Navigational Transactional Three categories of audience intent I don’t know the topic, but I want to start learning about it. Where do I start? I know the topic, but what’s the best page for reference on it? What’s the best place to buy this thing, or get help for that thing?
  9. 9. When users search with a “what is” query, they are just starting to learn about the topic EXAMPLE The best way to learn the audience intent for a query is by reading the search results, and gathering clues about the audience and infer what they are trying to do
  10. 10. 11 How do we model audience intent?
  11. 11. Two kinds of audiences: business people and specialists Business people tend to use informational queries to learn about topics and products that are not in their core areas of expertise They also use transactional queries to move towards purchase Specialists use a lot more navigational queries to get reference information about topics or products within their areas of expertise. They also use social networks to connect with influencers on these topics
  12. 12. Emily CMO Cynthia CEO Jan Digital strategist Loc Data scientist Marco Finance lead Jeff Procurement lead Sarah Marketing manager Specialist Business Person
  13. 13. Two kinds of queries: unbranded and branded Audiences start with unbranded queries, and only move to branded queries when they’re ready What is big data? Why is AI so important for big data? How does IBM solve big data problems with AI? How can I solve my big data problems with IBM? What is Watson Analytics? How is Watson Analytics better than the competition? How do I try Watson Analytics? How do I buy Watson Analytics?
  14. 14. Discover Learn Solve Try Buy What is big data? Big data for marketing Big data platforms Watson Analytics free trial Watson Analytics ROI Emily CMO
  15. 15. 16 How do we model audience intent at scale?
  16. 16. The challenge of enterprise search-first content strategy • 200K relevant English keywords • 10 other languages with at least 100K relevant keywords • 300 million pages • 100K assets: Videos, white papers, case studies, demos, etc. • Total opportunity = 300 million users • More than 100 personas • Uncertainty • How do we know that the 1.2 M keywords is even the right set? • Assuming it is, how do we prioritize opportunities? • How do we determine who should own the page/asset that is the best answer for the question implicit in the query?
  17. 17. Cognitive Keyword Classifier  Select seed keywords from KIS  Identify top-ranking pages (IBM and non-IBM) Select portfolio-segment Keywords Use AlchemyAPI to scrape SERPs and identify recurring Concepts Normalize Concepts to taxonomy Train Watson NLC on Keyword/Concept pairs Classify Keywords using Watson NLC API Refine training data to improve performance  Scrape text of ranking pages (60-70 pages / minute)  Apply NLP and machine learning algorithm  Identify clusters of co-occurring Keywords and Concepts  Consolidate Concepts  Harmonize to existing IBM vocabularies  Train Classifier on seed data  Measure Recall and Precision using test data  Submit keywords to Classifier  Process JSON output  Flag incorrect or low-scoring test classifications  Re-train the Classifier as needed
  18. 18. ...then helps them optimize pages or assets relevant to keywords that they add to their target set Acrolinx discovers existing SEO keywords as practitioners write Acrolinx is being integrated with the IBM Keyword Intelligence System (KIS) KIS supplies planned SEO keywords, content performance, and competitive insight to practitioners, while authoring, through Acrolinx
  19. 19. Acrolinx Analytics provides a wealth of visual dashboard reports and API-accessible metrics
  20. 20. Two cognitive applications: Tagging and internal search
  21. 21. Cognitive content dilemma Tagging or search: You can pay me now or you can pay me later Website Semantic Search
  22. 22. • Personas Job role, industry, digital fingerprints, cookies • Buy cycle states Content performance, link pathing, response scoring • Topics Keyword clusters, topic hierarchies • Product family > Product (with metadata: specs, pricing…) Types of tags
  23. 23. One IBM unit performance with tagging optimization KIS tagging optimization timeframe
  24. 24. 35% 34% 34% 33% 33% 39% 40% 40% 43% 43% 42% 30.0% 40.0% 50.0% 60.0% 70.0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec AxisTitle Axis Title Search Success Rate Infrastructure Analytics Cloud Commerce GBS Watson Security Mobile GTS Overall
  25. 25. Thank You!