Empowering Customers to Self Solve - A Findability Journey - Manikandan Sivanesan & Rutvij Vyas, Red Hat
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Empowering Customers to Self Solve - A Findability Journey - Manikandan Sivanesan & Rutvij Vyas, Red Hat
- 1. Empowering Customers to Self Solve - A Findability Journey Manikandan Sivanesan Senior Software Engineer, Red Hat @manisnesan #Activate18 #ActivateSearch
- 2. Agenda What is Self-Solve? Why self-solve matters? How we are doing?
- 4. Self Solve • Bullet text • Secondary bullet • Tertiary bullet • Second secondary bullet • More bulleted content I want my server fixed now
- 5. Self Solve - Ideal Scenario I want my server fixed now • Search • Relevant Content • SELF-SOLVE!!!
- 8. Why self-solve matters for Customer?
- 9. Why self-solve matters for Business? “Self-service offers companies a tantalizing opportunity to reduce spending, often drastically. The cost of a do-it-yourself transaction is measured in pennies, while the average cost of a live service interaction (phone, e-mail, or webchat) is more than $7 for a B2C company and more than $13 for a B2B company.” - HBR Business Review 2017
- 10. Why self-solve matters for Business?
- 11. How we are doing?
- 12. About Red Hat • Open Source Company • Communities • Product Offering • Subscription Business Model
- 14. Red Hat Customer Portal • Value of Red Hat Subscription • FAQ, Docs, Knowledge Base • Certifications • Product Security • Award Winning Support 24x7 • Onsite Search and Apps
- 15. Top Tasks
- 16. Delivering Self-Solve • Functional & Relevant Search • Portal Search • TroubleShooting • Tool Automation - Faster Resolution • User Experience • Support (Knowledge Centered) • Content • Lucidworks Fusion & Apache Solr • JBoss EAP for client application
- 18. Categories of queries • Low intent High Frequency • rhel, ansible, openshift - Product based • sosreport, openjdk - technology/component specific • High intent Low Frequency • open files hard limit • multipath configuration • Error messages • org.jboss.ejb3.annotation.TransactionTimeout • NFS: nfs4_discover_server_trunking unhandled error -22. Exiting with error EIO
- 19. Product Entity Recognition • Customer Product Vocabulary vs Official • rhel 7, red hat linux , linux, red hat enterprise linux • SynonymGraphFilterFactory • Build our own synonyms list • Manually Maintained • Acronyms • Hard to identify components or technology specific words
- 20. Word2Vec for Product Inference • Query Preprocessing • Word2Vec - Learning representation • “relatedness” between words • q = kubernetes • "bq": “boostProduct:openshift^4" • q = karaf • “bq”: "boostProduct:fuse^4" • Source the Product Detector Synonyms • neutron, packstack, director => OpenStack
- 21. Tier Model Approach • Fields from Content mapped to Tiers • Search across multiple tiers • Different boosts for different tiers • Common parlance with Content Writers • qf = tierA^10.0 tierB^5.5 tierc^4.5 tierD^3.5 Content/Tier Tier A Tier B Tier C Tier D Knowledge Base title issue resolution resolution_stripPunc Documentation chapterTitle sectionTitles content content_stripPunc Vulnerability title content content_stripPunc
- 24. Problem Query ● Product, Version ● Summary ● Description Challenges • Vague • Important tokens • Diagnostics Info Don’t Force the customer
- 25. Only the important tokens • edismax query parser ○ stopword removal in phrase query •Missing bigram/trigram with leading/following stopword • Stopwords handling before sending to Solr • Improved phrase matching ((capsule does not have installation media)^5.0) => (title:"instal media 5.0"~2 | body:"instal media 5.0"~2)~0.1 (title:"media 5.0 instal"~2 | body:"media 5.0 instal"~2)~0.1
- 26. Relevance Signals • Customer Feedback • User Clicks • Fusion - Signals and Aggregation • Associate Feedback • Case resolution • Popularity based on linked knowledge content • Linked Solutions from similar cases "boost": "if(exists(caseCount_365),sum(1,div(log(map(caseCount_365,0,0,1)),log(8.0))),1)"
- 27. Evaluation framework • Leverages past closed cases & linked knowledge content • Exact answer to a particular problem • Search effectiveness • % of linked content in Top 1, 3, 6 ranks of results • Tuning • Best parameters value chosen based on maximized score • Hard to tune in practice
- 28. Product Specific Tuning • Boost factors determined by Major Products • Link Popularity not relevant for emerging products • Individual Tuning for Top products
- 29. Journey Ahead - Product Specific LTR • RankNet • Training Dataset • Neural Network Model • Available in Solr 7.3
- 30. Results • Self-Solve based Home Page • Increased traffic • Customers are motivated to self-solve • A/B testing • 7% decrease in case creation rate for experiment group
- 31. Faster Case Resolution • Speciality Case Routing • Rule Engine -> Mahout Classifier -> Deep Learning • Accuracy ~ 85% • Language Detection • Case Routing based on Customer Locale • Case text in multiple languages • FastText based Language Classifier • Accuracy ~ 99%
- 32. Credits • Charles Sanders • Tom Butt • Michael Alcorn • Scot Floess • Diego Fernandez • JP Sherman • Search Team • AIR Team
- 33. Q&A
- 34. Thank you! Manikandan Sivanesan Senior Software Engineer, Red Hat @manisnesan #Activate18 #ActivateSearch