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Lunch & Learn Intent Guide

Lunch & Learn Intent Guide. Welcome . Lunch & Learn Intent Guide. 10.00-10.30 Opening & Introduction Intent Guide by Marcel Smit, VP Natural Language Search Solutions 10.30-11.30 Research & Product Management by Leonoor van der Beek, Manager Research 11.30-11.45 Break (Atrium)

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Lunch & Learn Intent Guide

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  1. Lunch & Learn Intent Guide • Welcome

  2. Lunch & Learn Intent Guide • 10.00-10.30 Opening & Introduction Intent Guide by Marcel Smit, VP Natural Language Search Solutions • 10.30-11.30 Research & Product Management by Leonoor van der Beek, Manager Research • 11.30-11.45 Break (Atrium) • 11.45-12.30 Business Case by Arjo van Oosten, Director Business Consulting Services • 12.30-13.00 Q&A and discussion • 13.00-14.00 Networking lunch (Atrium)

  3. A New Way To Engage With Your Customer RightNow’s Intent Guide Marcel E. Smit VP Natural Language Solutions 12 mei 2011

  4. Agenda • Introductions • The RightNow Intent Guide Solution • Q-go – How it all started • Introduction to Intent Guide • Strategic Drivers • Solution Example – KLM • Intent Guide: technology, integration, and roadmap

  5. Q-go - How it all started

  6. 1) CX Intent Guide Customers

  7. Introduction to Intent Guide

  8. RightNow CX Intent Guide Web / Mobile Self-Service Chat / Co-Browse Email Management Intent Guide Web / Mobile Self-Service Chat / Co-Browse Email Management CX for Facebook Support Communities Innovation Communities Cloud Monitoring CX for Facebook Support Communities Innovation Communities Cloud Monitoring Voice Experience Management Multi-Channel Agent Desktop Voice Experience Management Multi-Channel Agent Desktop Customer Feedback Customer Feedback Service Sales Marketing Service Sales Marketing Analytics Analytics App Builder Knowledge Foundation | Natural Language Technology Mission Critical Operations

  9. How does it work:CX Intent Guide with Natural Language Technology

  10. RightNow CX Intent Guide Matches Your Customer’s Intent to Your Business Goals. 1) Engage 2) Match 3) Fulfill Moment of Truth Question Intent Customer Convert Consumer Goal Brand Goal Natural Language Technology Conversion of extra luggage, push into online check in process and cross sell to travel insurance “large suitcase” = What are the size and weight allowances

  11. Strategic Drivers

  12. Customer Life Cycle Engagement “New York?” “upgrade seat” “book flight” “flight status” “print eticket”

  13. Top 5 Drivers for Intent Guide • Increase Online Revenue • Decrease in Online Processes abandonment • Increase Cross and Upsell Opportunities • Increase Insight into Customer Behavior • Right Channeling

  14. Solution Example - KLM

  15. Lunch & Learn Intent Guide 10.30-11.30 Research & Product Management by Leonoor van der Beek, Manager Research 11.30-11.45 Break (Atrium)

  16. Intent Guidetechnology, integration, and roadmap Leonoor van der Beek Manager Research & Language Technology

  17. The technology: NLP

  18. Common search scenarios User queries Intent

  19. Common search scenarios Intent User query (typo)

  20. Intent Guide Basics User Query Linguistic Analysis Meaning Matching Meaning Linguistic Analysis Model Question Linguistic analysis on bothqueryandcontent givesus themeaningbehind the encodinginwords

  21. Context sensitive spelling correction morgtage => mortgage oen => open? OEM? pen? teh <=> the Multi Word Units Checking account Red eye flight Lemmatization / stemming card, cards => card pay, pays, paid => to pay Synonyms ATM = Cash machine baggage = luggage Linguistic Analysis User Query Linguistic Analysis Dictionaries Meaning Matching Meaning Linguistic Analysis Model Question

  22. Intent GuideAn example User Query Linguistic Analysis Meaning Matching Meaning Linguistic Analysis Model Question

  23. Context sensitive spelling correction Intent GuideAn example

  24. Linguistic Analysis Grammar • Compound/cliticanalysis • huisdierenticket => ticket(pet) • damelo => give it to me • Generalize over syntactic structure • Can I… • Is it possible for me to… • I’d like to… • Resolve ambiguity • “check my cash” vs. ”cash my check” User Query Linguistic Analysis Dictionaries Meaning Matching Meaning Linguistic Analysis Linguistic Analysis Model Question

  25. Linguistic Analysis “How can I order a vegetarian meal?”

  26. Linguistic Analysis “How can I order a vegetarian meal?” Word Analysis ‘can’ (aux) ‘can’ (n) ‘I’ (pn) ‘order’(v) ‘order’ (n) ‘a’ (art) ‘How’ (qword) ‘vegetarian’ (adj) meal (n) Lexica

  27. Linguistic Analysis “How can I order a vegetarian meal?” Word Analysis ‘can’ (aux) ‘can’ (n) ‘I’ (pn) ‘order’(v) ‘order’ (n) ‘a’ (art) ‘How’ (qword) ‘vegetarian’ (adj) meal (n) Lexica Sentence Analysis Subject= ‘person’ Object= ‘meal(prop(vegetarian))’ Qtype= ‘manner’ Action= ‘order’ Grammar (manner, order, person, meal(prop(vegetarian)), _, _, _)

  28. Semantic Matching User Query Linguistic Analysis Meaning Semantic Matching Meaning Linguistic Analysis Model Question • Semantic matching furtherincreasesrelevanceofanswersthroughinformation retrievalonrelated concepts

  29. Matching per position Either direct or via semantic relation Semantic Matching (how,order,person,meal(prop(vegetarian)), _, _, _) Relation Hier-archies Relations: book-order vegetarian-special Matching relation relation (how,book,person,meal(prop(special)),_, _, _)

  30. From jargon to user language Near-synonyms/related terms: account balance – bank statement to transfer - transaction More/less specific items (hypernyms/hyponyms): Porsche - car transfer costs – fee Semantic Matching User Query Linguistic Analysis Relation Hierarchies Meaning Semantic Matching Meaning Linguistic Analysis Model Question

  31. “Normal” vs. NLP Search • Index vs. Model Question “How can I order a vegetarian meal?” ? Index Model Question Word Analysis ‘can’ (aux) ‘can’ (n) ‘I’ (pn) ‘order’(v) ‘order’ (n) ‘a’ (art) ‘How’ (qword) ‘vegetarian’ (adj) meal (n) Lexica Sentence Analysis Subject= ‘person’ Object= ‘meal(prop(vegetarian))’ Qtype= ‘manner’ Action= ‘order’ Grammar (manner, order, person, meal(prop(vegetarian)), _, _, _)

  32. NLP is a Continuum! Multi-word phrases Virtual Assistant Dialog Understanding Spelling Correction Basic Named Entity Recognition Fuzzy Semantic Matching Stemming Lemmatization Synonyms Ontologies Increasing Difficulty Compound Analysis (Germanic) Verticalized Ontologies Linguistic Analysis Bag of Words Part of Speech Identification Syntactic Structure Context Sensitive Spelling Correction “Intent” Ambiguity Resolution Emotion Identification Advanced Named Entity Recognition Automatically Identified Ontologies Keyword/ Boolean Human Skill Question Answering NLP Complexity Scale

  33. Integration

  34. Integration Vision Front End Intent Guide The combination of IG and RN CX provides the best possible customer experience across the entire customer life cycle & RightNow CX

  35. Integration Vision Front End • Delivers top-notch answers to customer questions across the entire website • Improves customer experience through relevant answers and intuitive interaction (suggest, Virtual Assistant, context dependent FAQs) • Boosts conversion and sales through intent-driven banner ads • Drives marketingby providing actionable insight in customers’ thoughts and wishes • Reduces costs by increasing self-service levels Intent Guide

  36. Integration Vision Front End • Answers all support questionson the CP site • Combines informationfrom Search Priority Words, Intent Guide, CX Answers and CX Social • Decreases costsby increasing self-service levels for support issues RightNow CX

  37. Integration Vision Front End Intent Guide • … can both be used stand alone or in combination • … & are entirely complementary & RightNow CX

  38. Integration Roadmap Front End Search from RNT Support pages Intent Guide Results Intent Guide results blended with other content sources Community Results RightNow search results

  39. Implementing Intent Guide for RN customers • Existing RN KB can be complemented by Intent Guide on the support page (through combined search widget) • Intent Guide provides highly relevant answers and offers on the home page

  40. “Joint RightNow and Q-go Prospects and Customers: Prospects for this combined offering, as well as customers using the combined offering, should inquire about the go-forward strategy and technology road map, because it might require the migration of knowledgebase information to the final, standardized knowledge product.” Gartner First Take, Feb 2011

  41. Integration Vision Back End • One single knowledge base that is • Applied across all channels • Easily accessible • Not affected by hardware constraints • Separated from other tasks in the customers experience work space

  42. Integration Vision Back End For the current Intent Guide integration, this means: • No content duplication • No duplication of functionality in the tooling • No need for more than one tool for any single role/work process

  43. Integration Roadmap Back End • Batch linking from RN KB to Intent Guide • Integrated maintenance tool • Intent Guide Administration separated from content creation workflow

  44. Integration Roadmap Batch Linking • Select Answers for linking to Intent Guide Model Questions • Model Questions may be created automatically based on answer summary • Answer will be stored in RN knowledge base

  45. Integration Roadmap Integrated Maintenance • Creating or linking to MQ integrated in Answer writing/publishing process • Configuration and validation of MQs separate workflow from within Intent Guide

  46. Implementing Intent Guide for RN customers • Existing RN KB Answers may be marked as Intent Guide content by batch process • Initial Intent Guide automatically populated by (and linked to) marked RN KB answers • Integrated maintenance after GoLive

  47. Integrated maintenance • While writing a RightNow Answer, a corresponding Model Question can be created by a simple mouse click • Newly created or linked Model Questions will automatically link to RightNow answers • No reduplication of content • No double work • Separation of workflow for content creation & Intent Guide configuration/administration

  48. Roadmap

  49. May 2011 Key Highlights May 2011 Key Highlights Month May • Intent Guide • Federated Search Widget • Virtual Assistant Packaging

  50. Virtual Assistant Packaging Web Customer Portal Intent Guide Virtual Assistant Packaging Virtual Assistant Packaging provides an out of the box Virtual Assistant template for an the VA overlay that differentiates from the Question Matching overlay, provides the ability for customers to provision a virtual assistant as a separate support channel, and provides a dialog model for interacting with content which moves the consumer self-service experience beyond ‘Search’ • Key Capabilities: • Ability for threaded interaction control to reinforce the conversational dialog model • Ability to embed an out of the box ‘static’ visual representation / brand character • Ability to overlay a smaller profile on the page in order to support site tours and form fill • Social Chatter interaction types added to the out of the box question dictionaries per supported language

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