Ontology-centric knowledge discovery in a Contact Centre for Technical Product Contact Centre for Technical Product Support Christopher JO Baker bakerc@unb.ca p @ Bradley Shoebottom bradley.shoebottom@innovatia.net Alex Kouznetzov alexk@unb.ca
Knowledge Discovery: Contact Centre • Business Challenges: – Lengthy diagnosis phase / insufficient time for Lengthy diagnosis phase / insufficient time for troubleshooting • Technical support teams spend 25 to 50% of time searching for answers hi f • Unlinked information in knowledge-base silos and heterogeneous formats • Case escalation due to poor information find-ability – Cases languish when Tier 2 (second level) not available available – OEMs outsourcing to low cost solution providers drives Contact Centres to be more productive. 2
Semantic Knowledge Discovery: Contact Centre • Custom Telecom Gazetteers • OWL-DL • Pellet Reasoner • GATE • TopBraid Composer • OWL API • TopBraid Live/ Ensemble 3
Semantic Solution: Visual Query over Telecom KB Visual Query: Network Routing Server has a Configuring and Enabling Procedure TopBraid Ensemble 4
Telecommunications Ontology • Describes phone routing software High Level Ontology • Based on OWL-DL (OWL-2) Based on OWL DL (OWL 2) – Classes: 506 – Instances: 12,000+ – Data Properties: 47 – Obj ect Properties: 167 – Class Equivalencies: 37 – Class Equivalencies: 37 – Class Disj unctions: 34 – S ubclass Axioms: 37 – Inverse Obj ects: 50 I Obj 50 – Description logic: ALCHI(D) – Depth: 8 classes 5
Technical Support Contact Center FAQs • What are the product software error codes? – E.g. ADM0234 E g ADM0234 • What are the problem symptoms? – E g Unable to call 911 – E.g. Unable to call 911 • What are the possible causes for a problem symptom? symptom? – E.g. Mis-configured system settings • What is the solution for a possible problem? p p – E.g. Reset Emergency S ervices settings • Where is the procedure for a solution? p 6
Case Resolution Process Transfer to T f t Tier 1 Monitor 1 Tier 1 Resolve 1 Annotate If No Tier 2 CRM Queue Using KB CRM Solution Product Case Specialist p Tier 2 Attempts to p Resolve Share Point CRM Annotate Annotate Create Create Case Solution KB=Knowledge Base DB=Data Base Document Federated CRM=Customer Relationship Management DB Access OEM=Original Equipment OEM=Original Equipment Product Bug, Document CR P d t B D t CR If No Point is OEM Responsibility Manufacturer Handoff to Solution OEM Technical CR DB Publications Wiki KB Manuals 7
Contact Center Environment • Tier 1: – Information gathering/ validation Information gathering/ validation – Initial problem solving – Requires highly precise information Requires highly precise information – Needs simple-to-use user interface • Tier 2: – Problem escalation or information not found – Requires high information recall – Requires advanced search capabilities 8
Pilot Study: Query Types and Source Content • S earches involve up to 4 terms, links to granular literature metadata and data in diverse (un)- structured formats. t t d f t Type of Query CRM DB Bulletins Technical Publications Existing Existing Form query only Form query only Knowledge Existing HTML PDF only* PDF only* Base Knowledge Base S S emantic e a t c Form query on all o que y o all S olution ontology entities S emantic HTML Word* 2 kinds of Pre-configured visual S olution XML, query (F AQ) FrameMaker* Ad hoc visual query *Unstructured 9
Pilot Test Design • Phase 1: Ontology and Interface Usability Test (completed) – Can users find answers to 5 common queries f d • Tier 1: Form search, pre-built general visual query, pre- built specific visual query • Tier 2: Form S earch, Create pre-built general visual query, pre-built specific visual query • Phase 2: S • Phase 2: S cenario Testing (ongoing) cenario Testing (ongoing) – Role-play of interaction with customer to test • Troubleshooting decision tree g • Did information retrieved address symptoms, provide procedures for solutions (recall and precision) • At what point did escalation occur and why At what point did escalation occur and why 10
Testing Challenges • S election of enough Tier 1 and 2 with a limited supply of people (especially Tier 2) limited supply of people (especially Tier 2) • Time need for training and testing • Complexity of testing to ensure all search • Complexity of testing to ensure all search paradigms covered • Extra time needed to gather baseline • Extra time needed to gather baseline metrics in old toolset since they did not exist • Adoption of new software toolset had Adoption of new software toolset had glitches 11
Pilot Study: Results d Search ared to Old ge Compa % Chang Toolset • Tier 1 found the right information with less need for escalation – Now able to find documents 90% of the time (old toolset 75% ) • Tier 2 has more tasks and toolset features to learn – longer Tier 2 has more tasks and toolset features to learn longer learning curve 12
Contact Center Performance Metrics Metric Impact of Semantic Solution Tier Impact Utilization (Productive versus Less time in training/ mentoring 2 non-productive time) and more time solving cases First Call Resolution Information found the first time, , 2 less time spent in research Case Closed Timeframe (Total Case Closed e a e ( otal Decreased case duration due to ec eased case du at o due to 1 and 2 a d elapsed time) less time spent in research Filtration Rate (Escalation) Less cases escalated to Tier 2 or to 2 [Linked to First Call Resolution] [Linked to First Call Resolution] Manufacturer Manufacturer Revenue Model Move from a per person 1 and 2 headcount/ per client to a per case handled and multi-client support handled and multi client support model 13
Business Outlook in Contact Centre Vertical • Contact Centres employ 18,000 in New Brunswick Canada and provides over CDN$1 Brunswick, Canada and provides over CDN$1 billion to provincial economy • S S emantic S emantic S olution: olution: – Proj ected saving for Tier 1 is 26% of overall case resolution cost – Re-usable methodology applicable across multiple telecommunications products – Business driver in cost reduction, platform Business driver in cost reduction platform customizations, professional services 14
Innovatia Research • Funded by a CDN $4 million grant from the Atlantic Innovation Fund of the Atlantic Atlantic Innovation Fund of the Atlantic Canada Opportunities Agency • Research Focus – S S ingle source content development and re-use ingle source content development and re use – S emantic knowledge management 15
Acknowledgements • Proj ect Team – Christopher JO Baker 1 : Primary Investigator Christopher JO Baker 1 : Primary Investigator – Bradley S hoebottom 2 : Knowledge Engineer – Alex Kouznetzov 1 : Text Mining Engineer Alex Kouznetzov : Text Mining Engineer – Michael Doyle 2 : Network Infrastructure • Testing Team g – Karen Lewis 2 : Information Architect – Innovatia Technical support team • Dearran Townes, Amanda Chase, Darrell Flynn, Gregg Knight, Corey Harris, Andrew Madsen 1 UNBSJ 1 UNBSJ, 2 Innovatia 2 Innovatia 16
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