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 o Large Fortune 100 Telco carrier Speech/ IVR system Case study #2 o State Medicare/Medicaid IVR, considering speech Questions and Answers

3. Why Have Analytics?

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

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

6. Fundamental Analytics Problem Key metrics KBIs Drop-offs Recognition Hang-ups Thresholds Alerts WHAT? Step 1 WHY? Step 2 What do I do? Step 3 Re-scripting Tuning Menu Restructuring Extend automation Build new automation ? 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. Events Patterns & Trends Structures e.g. What happened? Why did it happen? What was the cause? What’s been happening? Getting to Why React Change or improve Predict Behaviors Incentives Skills Technology Meaning Culture Expectations Scripts IVR Structure Experience

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

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

11. Management By “Events” MetricsCurrent PeriodLast period Call Volume450,000375,000 Overall drop-off to CSR35%29% Incomplete calls/ hang ups 20%22% Recognition rates for key modules 89%95% 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 Live Agent How Can We Help You? SS #Account # Google Yahoo MSN Home Page eNewsletter

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

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

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

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

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 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? Main Menu Recognition Event ValueRaw TextConf. ReservationsReservations962 schedulesschedules109 0:19.3 0:09.2 Prompt: _ UNKNOWN 0:20.8

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

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