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Optimizing IVR/Speech Using Customer Behavior Intelligence Michael Chavez

Optimizing IVR/Speech Using Customer Behavior Intelligence Michael Chavez Vice President Client Services ClickFox, Inc. Agenda. Welcome and Introductions The Optimization Problem Case study #1 Large Fortune 100 Telco carrier Speech/ IVR system Case study #2

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Optimizing IVR/Speech Using Customer Behavior Intelligence Michael Chavez

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  1. Optimizing IVR/Speech Using Customer Behavior Intelligence Michael Chavez Vice President Client Services ClickFox, Inc.

  2. Agenda • Welcome and Introductions • The Optimization Problem • Case study #1 • Large Fortune 100 Telco carrier Speech/ IVR system • Case study #2 • State Medicare/Medicaid IVR, considering speech • Questions and Answers

  3. Why Have Analytics?

  4. Customer Service Challenge Increase Satisfaction, Deeper Relationships, Increased Revenue Cut Costs, Do More With Less Efficiency Effectiveness Creating and managing high-qualityself-service channel experiences that meet both goals is difficult and hard to measure.

  5. Customers Customer Satisfaction by Channel Self-Service IVR Web Phone Face-to-Face

  6. Step 1 Step 2 Step 3 WHY? WHAT? What do I do? • Re-scripting • Tuning • Menu Restructuring • Extend automation • Build new automation • Key metrics • KBIs • Drop-offs • Recognition • Hang-ups • Thresholds • Alerts ? Fundamental Analytics Problem Result: Optimization is based upon qualitative assumptions, guesswork and can be extremely costly and time consuming.

  7. “Naming something,” said Alice to the Red Queen, “isn’t the same as explaining it.” Lewis Carroll, Alice’s Adventures in Wonderland

  8. IVR Structure Scripts Expectations Skills Experience Meaning Incentives Culture Technology Behaviors Getting to Why What happened? React Events What’s been happening? Predict Patterns & Trends Structures e.g. Why did it happen? What was the cause? Change or improve

  9. The “Black Box” User Experience in the IVR

  10. Some Assumptions and Guesswork Call Logs / Reports IVR optimization takes place through a cumbersome, qualitative process Extensive analyst hours Design Documents CSR Interviews (Qualitative) Optimization based on qualitative factors and extensive time investment

  11. Management By “Events” Proposition: MBE has limitations because it associates location with causality.

  12. MBE: “What”, not “why” Problem: We don’t know why success is measurably lower for one module. Proposition: Not a “data” problem, but a problem of perspective.

  13. The Need for New Thinking “The significant problems we face cannot be solved with the same level of thinking we were at when we created them.” --Albert Einstein

  14. Fundamental Problem of Organic Systems • Highly complex relationships • Non-linear • Cause and effect are distant in space and time • Leverage is generally not where the problem appears

  15. Getting to “Why” IVR/Speech WEB How Can We Help You? Yahoo Google eNewsletter MSN SS # Account # Home Page Live Agent

  16. Say “Agent” Say “Agent” Experiences, not events IVR/Speech WEB How Can We Help You? Yahoo Google eNewsletter MSN SS # Account # Home Page

  17. PRESS “0” ABANDON ABANDON Experiences, not events IVR/Speech WEB How Can We Help You? Yahoo Google eNewsletter MSN SS # Account # Home Page

  18. Press or Say “Zero” ABANDON Experiences incorporate “usage memory” IVR/Speech WEB How Can We Help You? Yahoo Google eNewsletter MSN SS # Account # Home Page

  19. What Happened & Why? PRESS “0” PRESS “0” PRESS “0” ABANDON ABANDON Experiences incorporate “usage memory” IVR/Speech WEB How Can We Help You? Yahoo Google eNewsletter MSN SS # Account # Home Page

  20. MBE: “What”, not “Why” Transfer analysis tells you how people transferred and even where they transferred from. Did they transfer because of problems at that dialogue or because of an earlier experience?

  21. Speech Confidence measures 0:09.2 Main Menu Recognition Event Value Raw Text Conf. Reservations Reservations 962 schedules schedules 109 0:19.3 0:20.8 Prompt: _ UNKNOWN Low-confidence measures direct you to fix recognition or grammar. But what if the problem is related to an overall experience and not this one event?

  22. Often, the cause is the experience, not the dialogue state. Offer The Why: much of the drop-off is caused by “error spiraling”.

  23. Case Study I Speech Optimization for Fortune 100 Telecom Company

  24. Case Study II State Medicare/Medicaid Member and Provider Helpline

  25. You need to show connectivity…

  26. To solve the puzzle.

  27. Continuous Optimization

  28. “If something is worth doing, it’s worth doing poorly until you can do it well.”  Robert Fritz

  29. About ClickFox • Founded 2000 in Atlanta • Pioneer in customer behavior intelligence • Continuous optimization services • Top-tier Fortune 500 customers:

  30. Questions & Answers Michael Chavez – VP Client Services michael.chavez@clickfox.com Mike Kent – Director National Accounts mike.kent@clickfox.com

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