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Marketing AI - How to Build a Keyword Ontology

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From a workshop presented at MinneWebCon 2018. Keywords are the life’s blood of a marketing enterprise. Most marketing organizations struggle to find the right keywords for their teams. At IBM we built a keyword ontology, which is a fancy name for a set of taxonomies related to the keywords our target audiences most often use in their search queries. We use the ontology to ensure that new pages are built with the language of the customer. This workshop discusses the use of AI-enhanced processes to drive keyword management, as well as practical methods for search and IA success.

Published in: Marketing

Marketing AI - How to Build a Keyword Ontology

  1. 1. © 2015 IBM Corporation Marketing AI: How to build a keyword ontology James Mathewson Michael Priestley Dan Segal MinneWebCon 2018
  2. 2. Presentation agenda Introductions and bios What is a keyword ontology? Why is it important? Some basics of ontology How do you build one? Use cases • Think Case study • Journey mapping • Autotagging Exercises
  3. 3. James Mathewson James Mathewson is IBM's Distinguished Technical Marketer for search. He has 20 years of experience in web editorial, content strategy, and SEO for large and small companies. A frequent speaker, lecturer and blogger, James has published more than 1600 articles and two books on how web technology and user experience change the nature of effective content. James has two advanced degrees on related subjects from the University of Minnesota.
  4. 4. Michael Priestley @ditaguy Michael Priestley is a product owner and content technology strategist, currently leading the IBM Marketing Taxonomy Guild to revise and align taxonomy initiatives across the marketing ecosystem. He has experience working with and across documentation, support, training, and marketing content as an enterprise content technology strategist. He was one of the original architects and editors of the DITA standard, was named an OASIS Distinguished Contributor in 2017, and is currently co-chairing the Lightweight DITA subcommittee.
  5. 5. Dan Segal Dan Segal is a Taxonomist on IBM's Marketing Platforms team. His work focuses on the use of cognitive technologies, including natural language processing and machine learning, for SEO and digital marketing. Dan has over 15 years of experience as a practitioner and consultant in the areas of taxonomy and ontology development, vocabulary management, semantic metadata, text analytics, and information retrieval. He holds BS and MLS degrees from Rutgers University.
  6. 6. What is a keyword ontology?
  7. 7. What is a keyword ontology? A keyword ontology is a knowledge graph describing relationships between the keywords your target audiences frequently use in search queries and the content about the products and services you sell
  8. 8. Keyword Data Topics Personas Job Roles Industries Products Solutions Events Buyer Journeys Narratives BUs Brands
  9. 9. Non-branded keyword group Non-branded keyword group Branded keyword group Business UnitProduct SegmentKeyword Groups (Taxonomy) Keywords
  10. 10. 10 A few ontology basics
  11. 11. Ontology: Formal Representation of Knowledge Resources: What is this thing? Properties: What characteristics does it have? Relationships: What connections does it have to other resources? Constraints: What rules apply to this resource?
  12. 12. Here is a Thing. Now Let’s Describe It. Property Value Name Diet Coke® Manufacturer Coca-Cola Container type Plastic bottle Container size 20 oz. Color Brown Flavor Saccharine Cola Sugar? No
  13. 13. Ontology: Representing Knowledge through Graphs makes made by Thing Y also known as name Diet Coke® size 20 oz. calorie s 0 name Coca-Cola Cherry® Cherry Coke® The Coca-Cola Company name Thing X makes made by Thing Z
  14. 14. Ontology: Semantic Triples Declarations are expressed as <subject , predicate, object> – Subject is a resource – Predicate is a property – Object can be another resource, or a data value Subject Predicate Object Resource Property Resource Data value or Adapted from McComb and Robe: Designing and Building Business Ontologies
  15. 15. Semantic Triples: Examples name Diet Coke® Thing X Subject Predicate Object Thing X hasName Diet Coke® made by Thing X Thing Y Subject Predicate Object Thing X isMadeBy Thing Y name The Coca-Cola Company Thing Y Subject Predicate Object Thing Y hasName The Coca-Cola Company Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 15
  16. 16. Expressing Triples in Markup Language <rdf:Description rdf:about="#ThingX"> <rdf:type rdf:resource="#Products"/> <rdfs:label xml:lang="en-us">Diet Coke</rdfs:label> <madeBy rdf:resource="#ThingY"/> <calories rdf:datatype="http://www.w3.org/2001/XMLSchema#integer">0</calories> <size rdf:datatype="http://www.w3.org/2001/XMLSchema#string">20 oz.</size> </rdf:Description> Here is our graph for “Thing X” (Diet Coke®), expressed in computer-readable language: Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 16
  17. 17. 17 Why keyword ontologies?
  18. 18. Google is the best source of information on your target audience
  19. 19. 3,000,000,000,000 queries in 2016
  20. 20. Why do we need a keyword ontology? Better content • Make better content decisions • Learn from others • Measure categories and combinations Less waste and confusion • Know what exists • Reuse or differentiate Drive experience • Navigation • Progression • Personalization
  21. 21. Agile development for better content, less waste and confusion Do we have it? Did it work? What do they want? Plan Measure Manage Publish
  22. 22. Requirements for agile development People ProcessTools Cultural shift from organization centric to customer centric Governance across tools and authoring communities Cognitive tagging for quality assurance and content coverage
  23. 23. Driving experience Navigation • What’s similar • Cognitive linking Progression • What's next • Ontologies • Cognitive CTAs Personalization • What's for me • Predictive linking • Cognitive experience
  24. 24. Cognitive delivery to drive customer experience Do we have it? Did it work? What do they want? Customer awareness Improvement in selection Content awareness Measures of success
  25. 25. Requirements for cognitive delivery Common customer data Common content metadata and ontologies Common linking components Plus common KPIs, common process for improvement...
  26. 26. More than just topics/keywords •What is the purpose of the content? Does it explain a concept, express an opinion, describe a case study? •Example types: Explainer, Opinion/POV, Case studyContent type •How is the content expressed? Is it a web page? A PDF? A video animation? •Example formats: Web article, Downloadable PDF, Video animationContent format •Where did the content come from? •Example publisher types: Client, Partner, AnalystPublisher type •What is the content about? •Example topics: blockchain, data science, cloud computingTopic •Who is the content for? •Example audiences: data scientist, CFO, doctorAudience/role •What industries is this content relevant in? •Example industries: aerospace, energyIndustry •What IBM offerings or offering categories does the content describe? •Example products/solutions: Watson HealthcareProduct/Solution •When is the content relevant to the intended audience? •Example stages: Learn, Try, BuyBuyer stage
  27. 27. The challenge of enterprise 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?
  28. 28. 29 How to build a keyword ontology
  29. 29. 30 Audience intent is what people need to do with the information they seek
  30. 30. 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?
  31. 31. 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
  32. 32. Three Steps to Building a Keyword Ontology Identify keywords Determine semantic clusters (Concepts) Associate Concepts to each other
  33. 33. Tools for Building Your Keyword Ontology Tool Use Examples Keyword research • Identify high-value keywords • Identify high-ranking URLs • BrightEdge • Google Keyword Planner • Ubersuggest Natural language processing (NLP) • Keyword / entity extraction • GATE • OpenNLP • Watson NLU Machine learning • Text classification • Keyword clustering • scikit-learn • Watson NLC Data transformation • Convert ML outputs (e.g. JSON) to RDF • OpenRefine Ontology management • Domain modeling • Keyword / Group associations • PoolParty • Protégé • TopBraid EVN
  34. 34. 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
  35. 35. Training Data: Keyword / Concept pairs “Data scientists spend 60% of their time on cleaning and organizing data. Collecting data sets comes second at 19% of their time, meaning data scientists spend around 80% of their time on preparing and managing data for analysis.” - Gil Press, Forbes magazine Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
  36. 36. Keyword Classification: Sample Workflow INPUT: keyword list (CSV) Bluemix API OUTPUT: Watson NLC classification (JSON) POST-PROCESSING: Watson NLC classification (transformed to CSV) Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
  37. 37. Classifier Output: Weeding Out Weak Matches and “Laughers” § Limit classifier confidence score to exclude: – Weak classification matches – Noisy or marginally-relevant keywords § Low scores can also indicate candidate Concepts § Test threshold score: ≥ 0.25 for “keepers” § Ask: Do the training data introduce bias? Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
  38. 38. Connecting the parts Keyword data (from autoclassifier) Keyword Group (from Topic taxonomy) Product Segment (from Product taxonomy)
  39. 39. Use cases
  40. 40. Uses: Microsite design Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 41
  41. 41. THINK Marketing site
  42. 42. Example flow – from first click to consult Content page Recommended page Pages on same topic CTA to event Register for event Personalized recommended pages Subscribe to newsletter and notifications CTA to consultation
  43. 43. Uses: Keyword governance Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 44
  44. 44. Uses: Keyword Tracking and Measurement Keyword Groups (defined in ontology; exported to SEO platform) SEO Platform (keyword reporting) Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 45
  45. 45. Uses: Journey mapping Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 46
  46. 46. 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 47
  47. 47. Emily CMO Cynthia CEO Jan Digital strategist Loc Data scientist Marco Finance lead Jeff Procurement lead Sarah Marketing manager Specialist Business Person
  48. 48. 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? 49
  49. 49. 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
  50. 50. Uses: Autotagging Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 51
  51. 51. Using Keywords to Drive Auto-tagging • Starting point: Topic taxonomy • Derived from content analysis and keyword research, as well as competitive benchmarking • Extended taxonomy with custom properties: • Text signals that are used to train the auto-classifier • Phrases derived from iterative analysis Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
  52. 52. Cognitive Tagging Service: Sample Output IBM Case Study Extracted text signals Call to NLU with custom annotation model via REST API Query via SPARQL endpoint to match extracted signals to controlled values using skos:prefLabel, skos:altLabel, etc. Metadata values (extracted text signals, normalized to standard vocabulary and relevance-ranked)Keyword-enriched taxonomy Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
  53. 53. Continuous Improvement and Validation Content creation Keyword research Unmatched entities Entity extraction / normalization Results logging and review Taxonomy enrichment Model optimization Document processing Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl
  54. 54. Group Exercises Marketing AI- How to Build a Keyword Ontology - MinneWebCon 2018 - Mathewson - Priestl 55
  55. 55. Scenario: • You are an entrepreneur with a vision to sell your specialized line of ice cream to health-conscious consumers. • The market is highly competitive, and you need your content to be findable. • As part of your digital marketing strategy, you need to map the knowledge domain of healthy frozen desserts so that you can identify high-value keywords and incorporate them into your content.
  56. 56. Exercise 1: Keyword Identification • Analyze Web content from your subject domain. • Examine a variety of sources: references, news, magazines, trade publications, blogs, reviews, etc. • Identify keywords that represent your domain. • Write each keyword on an index card. Resources: sites.google.com/view/keyword-ontology Seed keywords: • healthy ice cream • organic ice cream • low-fat ice cream • low-calorie ice cream • vegan ice cream Keyword research tools: • k-meta.com • serpstat.com • neilpatel.com/ubersuggest
  57. 57. Exercise 1: Keyword Identification: ”Quick and Easy” Example: k-meta.com • Search your seed keyword; e.g., healthy ice cream • On the Keyword Overview page, scroll down to Organic results • Click on top-ranking URL • Select high-volume keywords (record on cards) • Repeat for additional URLs and keywords Resources: sites.google.com/view/keyword-ontology Seed keywords: • healthy ice cream • organic ice cream • low-fat ice cream • low-calorie ice cream • vegan ice cream Keyword research tools: • k-meta.com • serpstat.com • neilpatel.com/ubersuggest
  58. 58. Exercise 2: Card Sorting • Review the keywords that you identified in Exercise 1. • Arrange the cards into groups that make sense. • Name each group. • Present your findings and lessons learned. Tip: Group together keywords that are: • Synonyms (ex.: cars, automobiles) • Stemmed forms (ex.: project management, project managers) • Conceptually related (ex.: apples, peaches, pears, cherries, etc.) Tip: You can arrange groups into a hierarchy. • Dogs • Hounds • Sporting breeds • Toy breeds • Terriers Remember: There is no single correct answer. The objective is to define organizing principles and then group the keywords accordingly.

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