Optimizing IVR/Speech Using Customer Behavior Intelligence

Michael Chavez

Vice President Client Services

ClickFox, Inc.

Agenda• Welcome and Introductions

• The Optimization Problem

• Case study #1o Large Fortune 100 Telco carrier Speech/ IVR system

• Case study #2o State Medicare/Medicaid IVR, considering speech

• Questions and Answers

Why Have Analytics?

Customer Service Challenge

Cut Costs,Do More

With Less

Cut Costs,Do More

With Less

Increase Satisfaction,

Deeper Relationships,

IncreasedRevenue

Increase Satisfaction,

Deeper Relationships,

IncreasedRevenue

Creating and managing high-quality self-service channel experiences that meet both goals is difficult

and hard to measure.

EfficiencyEfficiency EffectivenessEffectiveness

Customer Satisfaction by Channel

PhoneCustomers

Face-to-Face

Self-Service

Web

IVR

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.

“Naming something,” said Alice to the Red Queen, “isn’t the same as

explaining it.”

Lewis Carroll, Alice’s Adventures in Wonderland

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

IncentivesSkills

TechnologyMeaning

Culture

ExpectationsScriptsIVR Structure

Experience

The “Black Box”

User Experience

in the IVR

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)

Management By “Events”

Metrics Current Period

Last period

Call Volume 450,000 375,000

Overall drop-off to CSR

35% 29%

Incomplete calls/ hang ups

20% 22%

Recognition rates for key modules

89% 95%

Proposition: MBE has limitations because it associates location with causality.

% of executions ending in:

Name

# Executions

Success

Max retry

Max timeout

Hangup

Other

TOTAL 10768 94.6 0.4 0.9 4.0 0.1

HEAR EMERGENCY MESSAGE 1177 (10.9%) 89.5 0.0 6.2 3.9 0.4

MAIN MENU 1156 (10.7%) 95.8 0.3 0.6 3.3 0.0

GET ARRIVAL CITY 722 (6.7%) 97.5 1.1 0.1 1.2 0.0

GET DEPARTURE CITY 721 (6.7%) 95.7 1.4 0.3 2.6 0.0

GET NUM PASSENGERS 676 (6.3%) 98.7 0.4 0.0 0.9 0.0

LIST FARES 551 (5.1%) 83.1 0.2 0.0 16.7 0.0

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.

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

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

Getting to “Why”IVR/Speech WEB

Live Agent

How Can WeHelp You?

SS # Account #

Google Yahoo

MSN

Home Page

eNewsletter

Experiences, not events

IVR/Speech WEB

How Can WeHelp You?

SS # Account #

Google Yahoo

MSN

Home Page

eNewsletter

Say “Agent”Say “Agent”

Experiences, not events

IVR/Speech WEB

How Can WeHelp You?

SS # Account #

Google Yahoo

MSN

Home Page

eNewsletter

ABANDON

ABANDON

PRESS “0”

Experiences incorporate “usage memory”IVR/Speech WEB

How Can WeHelp You?

SS # Account #

Google Yahoo

MSN

Home Page

eNewsletter

Press or Say “Zero”

ABANDON

Experiences incorporate “usage memory”IVR/Speech WEB

How Can WeHelp You?

SS # Account #

Google Yahoo

MSN

Home Page

eNewsletter

PRESS “0”

ABANDON

PRESS “0”

What What HappeneHappene

dd& Why?& Why?

PRESS “0”

ABANDON

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?

Reservations – Transfer Analysis

Total Calls Transferred 386

Requested agent 119 (30.8%)

DTMF 0 61 (15.8%)

Said “agent” 58 (15.0%)

Error condition 267 (69.2%)

Technical 95 (24.6%)

Other 87 (22.5%)

Out-of-app request 85 (22.0%)

Total Calls 1277

Total Calls Transferred 386 (30.2%)

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

Value Raw Text Conf.Reservations Reservations 962schedules schedules 109

0:19.3

0:09.2

Prompt: _ UNKNOWN 0:20.8

22

Often, the cause is the experience, not the dialogue

state.

The Why: much of the drop-off is caused by “error spiraling”.

Offer

Case Study I

Speech Optimization for Fortune 100 Telecom Company

Case Study II

State Medicare/Medicaid Member and Provider Helpline

You need to show connectivity…

To solve the puzzle.

Continuous Optimization

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

Robert Fritz

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

Questions & Answers

Michael Chavez – VP Client Servicesmichael.chavez@clickfox.com

Mike Kent – Director National Accountsmike.kent@clickfox.com