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Actionable Intelligence via Speech Analytics

Actionable Intelligence via Speech Analytics. Dr. Ofer Shochet SVP Verint Systems July 2008 IBM Speech Technologies Seminar. Speech analytics transforms recorded customer interactions from idle data to actionable intelligence. ROOT CAUSE ANALYTICS.

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Actionable Intelligence via Speech Analytics

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  1. Actionable Intelligence via Speech Analytics Dr. Ofer Shochet SVP Verint Systems July 2008 IBM Speech Technologies Seminar

  2. Speech analytics transforms recorded customer interactions from idle data to actionable intelligence

  3. ROOT CAUSE ANALYTICS Find out what you do not know to look for Analyze impact on known issues (lower false alarms) CONTENT CATEGORIZATION Transcribe and index entire call and extract concepts Mine categorized calls and suggest root cause Three Levels of Speech Analytics BUSINESS VALUE Find isolated calls of interest (high false alarms) KEYWORD SPOTTING INTELLIGENCE Spot 20-200 defined words

  4. Another way of looking at it: Word Spotting, Categorization, Root Cause Word Spotting Categorization Root Cause Analytics Technician did not show Customer complaints Received wrong information Did not receive credit Interactions about new product offering Large sample of customer interactions Offer not clear to customers Product does not work well Product is too expensive Perceived as better value Interactions involving competition Product quality driving churn Price attracting customers

  5. Contact Center Sales Marketing R&D Back Office Intelligence from Customer Interactions Compliance Fraud Risk Management Collections The Value of Speech Analytics • Delivers value from the “voice of the customer” • “Focus groups on demand” with a more complete view of the customer experience • Enhances Quality Monitoring • Evaluate calls that represent “what matters most” to you • Connects the contact center and the enterprise

  6. Verint Analytics Addresses Key Business Issues

  7. Customer Case Study • Customer Details • Fortune 500 Insurance provider with over 4 million customers • First call resolution at 60% • Abandonment rate of 28% • Customer service rating of “Poor” • No clear insight into why customer issues not resolved First Contact Resolution • Improve First Contact Resolution

  8. Customer Case Study How it works Customer Calls Classifies calls via automated speech recognition and categorization technology Identifies key reasons why customer issues were not resolved First Contact Resolution WHY? Success (65%) Resolved Calls (60%) Unresolved Calls (40%)

  9. Customer Case Study ! How it works Surfaces root cause of first call resolution issues Terms automatically surfaced indicating root cause “calling back about my claim” Processing Issues Agent Knowledge Gaps “I don’t know” First Contact Resolution Resolved Calls (60%) Unresolved Calls (40%) Lack of Agent Empowerment Missing Paperwork “waiting for a claim form” “Check with my supervisor”

  10. Customer Case Study • Solutions • Outdated policies reviewed and changed and agents were trained to fully understand them • Agents empowered to solve customer issue on first call • Integration of frontline transaction processing • Clarification of timelines on claim forms First Contact Resolution

  11. Customer Case Study Results Unresolved Calls First Contact Resolution 25% increase in First Call Resolution!

  12. Customer Case Study • Additional Results • 83% improvement in average speed of answer • 68% improvement in their service level (% of calls answered in 30 seconds) • 25% improvement in abandonment • 20% reduction in average handle time • 15% reduction in seasonal call volumes • eliminated the need to hire 22 additional agents • greatly improved staff morale First Contact Resolution

  13. Speech Analytics Delivers the Power of Why • What am I analyzing? • First contact resolution • Root cause of • why my results are • poor/excellent? • Agent knowledge • Agent empowerment • Outdated policies • Confusing claim forms • Execute a Plan • Increase first call resolution by 25% • How can I improve • performance? • Review outdated policies • Empower agents • Revise claim forms • Improve frontline processing

  14. Customer Case Study • Customer Details • Credit card provider • Historical record of converting 65% of inbound customer inquires • Sales conversion rate stagnating in previous three years • Marketing currently testing new offers Sales Effectiveness • Pinpoint best (and worst) selling circumstances and behaviors • Improve up-selling/cross-selling capabilities • Increase closing rates

  15. Customer Case Study How it works Inbound Calls Identifies the most effective approaches for agents when selling to customers Classifies calls via automated speech recognition and categorization technology Sales Effectiveness Success (65%) Sales Opportunities (50%) Other calls (50%)

  16. Customer Case Study How it works Automatically detects sales success and failures based on key phrases and metadata Sales Effectiveness Success (65%) WHY? Sales Opportunities (50%) Other calls (50%) Failure (35%)

  17. Customer Case Study How it works Surfaces root cause of negative sales performance Terms automatically surfaced indicating root cause Agent Presented All Options “Are you interested in the choices I presented?” Sales Effectiveness Success (65%) “I’m confused” Customer Confusion WHY? ! “I am not sure that we offer that…” Agents Acted Simply as Order Takers Failure (35%)

  18. Customer Case Study How it works Surfaces root cause of positive sales performance Terms automatically surfaced indicating root cause Positive behaviors are reinforced, negative behaviors are corrected Presented Qualifying Questions Agent Presented All Options “May I ask you a few questions?” Sales Effectiveness Success (65%) “the best deal for you is…” Customer Confusion Offered Most Relevant Option “this is a better offer because…” ! Agents Acted Simply as Order Takers Conducted Research on Competitive Offers

  19. Customer Case Study • Solutions • Agents trained to engage in conversation to uncover what customer values • Agents trained in presenting offers appropriately • Marketing began providing competitive data to agents prior to campaign launch • Marketing revised offers based on findings Sales Effectiveness

  20. Customer Case Study Results Sales Conversion Rates Sales Effectiveness 19% increase in conversions!

  21. Speech Analytics Delivers the Power of Why • What am I analyzing? • The factors that drive success or • failure in sales calls • Root cause of • why my results are • poor/excellent? • Agent knowledge • Probing questions • Simplicity of offers • Execute a Plan • Increase closing rates by 19% • How can I improve • performance? • Train agents to qualify • Create simple marketing offers

  22. Speech Analytics Delivers Quantifiable ROI Communications Provider

  23. Analyst Praise for Verint Analytics “Saddletree Research views the Verint approach to speech analytics managed services as the most comprehensive and efficient offering on the market today…Verint has set the competitive bar” Paul Stockford - Saddletree Research

  24. Why Verint Speech Analytics? • Automated root-cause • Delivers the Power of WHY • Integrated recording and QM platforms • Lower TCO and future proof • #1 Market Leader in Speech Analytics • Market proven ROI • Expert turnkey service offering

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