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

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

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

5. Another way of looking at it: Word Spotting, Categorization, Root Cause
Root Cause Analytics
Technician did not show
Customer complaints
Received wrong information
Did not receive credit
Large sample of customer interactions
Interactions about new product offering
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

6. 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
Sales
Back Office
Fraud
Collections
Risk
Management
Compliance
R&D
Marketing
Contact
Center
Intelligence
from Customer
Interactions

7. Verint Analytics Addresses Key Business Issues
Why are Customers Calling?
Identify contact drivers
Uncover trends and customer needs
Increase usage and effectiveness of self service channels
Customer Complaints
Reduce customer defections
Reduce costly escalations
First Contact Resolution
Improve first contact resolution
Sales Effectiveness
Pinpoint best (and worst) selling circumstances and behaviors
Improve up-selling/cross-selling capabilities
Increase closing rates
Customer Retention
Increase customer loyalty
Reduce churn
Vendor Management
Evaluate performance of business partners

8. First Contact Resolution
Customer Case Study
First Contact Resolution
Improve First Contact Resolution
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 8

9. First Contact Resolution
Customer Case Study
First Contact Resolution
How it works
Classifies calls via automated speech recognition and categorization technology
Identifies key reasons why customer issues were not resolved
Customer Calls
Resolved
Calls
(60%)
Unresolved
Calls
(40%)
WHY?
Success
(65%)
The first step in using speech analytics to improve sales effectiveness was to first separate calls with sales opportunities from calls that were about other issues. We were able to do this with a combination of speech analytics and metadata. In this case it was about a 50/50 split.
Once we were able to classify calls with sales opps from other calls we then used speech analytics to determine which calls closed with a successful sale. Keywords from agent scripts were used and were automatically detected via speech analytics. For example, there was a disclosure statement that was required to be read at every sale. This made it easy to find sales calls.
We also found calls that were not successful (once again in the sales opps bucket). Agents also had scripts they were required to read in the event of an offer rejection.
It worked out to be about a 65/35 split, along the lines with their historical data. 9

10. First Contact Resolution
Customer Case Study
First Contact Resolution
How it works
Surfaces root cause of first call resolution issues
Terms automatically surfaced indicating root cause !
Resolved
Calls
(60%)
Unresolved
Calls
(40%)
Processing Issues
“calling back about my claim”
Agent Knowledge Gaps
“I don’t know”
Lack of Agent Empowerment
Missing Paperwork
“waiting for a claim form”
“Check with my supervisor”
For the unsuccessful calls a couple of key phrases were identified via automated root cause analysis. Terms like “I am not sure we offer that..”, I’m confused”, and “are you interested in the choices I presented” were automatically found by the system. The business users did not know to search for those terms but the system found that they were more frequently said in failure calls than all other calls in the contact center.
Similarly, on successful calls there automated root causes. The terms ““May I ask you a few questions?”, “the best deal for you is..”, “this is a better offer because” all were found by the system to be more frequently said in success calls than all other calls. 10

11. First Contact Resolution
Customer Case Study
First Contact Resolution
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
These findings were eye-opening to the business. They had assumed that successful agents just followed script. Instead what they found was that deviating from the script and engaging in conversation was what led to sales.
So, they redesigned their sales process. Agents were retrained to engage in conversation and present offers appropriately
Marketing began providing competitive data to agents prior to campaign launch and revised their offers based on these findings to make them less complicated. 11

12. First Contact Resolution
Customer Case Study
First Contact Resolution
Results
Unresolved Calls
25% increase
in First Call Resolution!
The results were astounding. After implementing these changes sales conversions jumped from 65% to 77%, which was an all-time high and an improvement of 19%.
Without speech analytics there would have been no way to uncover the root cause of sales effectiveness. But with it, the reasons were quite clear. And the solutions were actionable. 12

13. First Contact Resolution
Customer Case Study
First Contact Resolution
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
These findings were eye-opening to the business. They had assumed that successful agents just followed script. Instead what they found was that deviating from the script and engaging in conversation was what led to sales.
So, they redesigned their sales process. Agents were retrained to engage in conversation and present offers appropriately
Marketing began providing competitive data to agents prior to campaign launch and revised their offers based on these findings to make them less complicated. 13

14. Speech Analytics Delivers the Power of Why
What am I analyzing?
First contact resolution
Why
How
Execute
What
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
To summarize, this customer used speech analytics to analyze the factors behind why sales are successful
Automated root cause analysis surfaced the reasons why
The findings pinpointed specifically how to improve
And the customer executed a plan to enact changes 14

15. Customer Case Study Customer Details Sales Effectiveness
Pinpoint best (and worst) selling circumstances and behaviors
Improve up-selling/cross-selling capabilities
Increase closing rates
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 15

16. Customer Case Study How it works Sales Effectiveness
Classifies calls via automated speech recognition and categorization technology
Identifies the most effective approaches for agents when selling to customers
Sales Opportunities
(50%)
Other calls
Inbound Calls
Success
(65%)
The first step in using speech analytics to improve sales effectiveness was to first separate calls with sales opportunities from calls that were about other issues. We were able to do this with a combination of speech analytics and metadata. In this case it was about a 50/50 split.
Once we were able to classify calls with sales opps from other calls we then used speech analytics to determine which calls closed with a successful sale. Keywords from agent scripts were used and were automatically detected via speech analytics. For example, there was a disclosure statement that was required to be read at every sale. This made it easy to find sales calls.
We also found calls that were not successful (once again in the sales opps bucket). Agents also had scripts they were required to read in the event of an offer rejection.
It worked out to be about a 65/35 split, along the lines with their historical data. 16

17. WHY? Customer Case Study How it works Sales Effectiveness Success
Automatically detects sales success and failures based on key phrases and metadata
Other calls
(50%)
Sales Opportunities
(50%)
Success
(65%)
Failure
(35%)
WHY?
Now that we know what, the important question is Why? Why do sales calls sometimes close and sometimes don’t? What do successful agents do differently than unsuccessful agents? Is there something in our process that could be tweaked? Do customers respond to the offers? Do we have the right offer? Too many variations? To few? This is what Speech Analytics can help us uncover. 17

18. ! WHY? Customer Case Study How it works Sales Effectiveness Success
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?”
Success
(65%)
Failure
(35%)
“I’m confused”
Customer Confusion
WHY?
For the unsuccessful calls a couple of key phrases were identified via automated root cause analysis. Terms like “I am not sure we offer that..”, I’m confused”, and “are you interested in the choices I presented” were automatically found by the system. The business users did not know to search for those terms but the system found that they were more frequently said in failure calls than all other calls in the contact center.
Similarly, on successful calls there automated root causes. The terms ““May I ask you a few questions?”, “the best deal for you is..”, “this is a better offer because” all were found by the system to be more frequently said in success calls than all other calls. !
“I am not sure that we offer that…”
Agents Acted Simply as Order Takers 18

19. ! Customer Case Study How it works Sales Effectiveness Success (65%)
Surfaces root cause of positive sales performance
Terms automatically surfaced indicating root cause
Positive behaviors are reinforced, negative behaviors are corrected
Agent Presented
All Options
“May I ask you a few questions?”
Presented Qualifying Questions
Success
(65%)
“the best deal
for you is…”
Customer Confusion
Offered Most Relevant Option
The business examined the reasons why these terms were being said and found that in the failure calls terms like “I am not sure we offer that” signified that agents were just taking orders instead of being consultative.
On calls where customers said “I’m confused” and “are you interested in the choices I presented” , they were being overwhelmed by offers.
On successful calls there automated root causes. The terms ““May I ask you a few questions?” signified that the agents were engaging in conversation instead of strictly reading a script
The term “the best deal for you is..”, surfaced agents that actually didn’t read all offers but picked the one most relevant to the customer.
And finally “this is a better offer because” signified that successful agents were actually conducting their own research !
“this is a better
offer because…”
Agents Acted Simply as Order Takers
Conducted Research on Competitive Offers 19

20. Customer Case Study Solutions
Sales Effectiveness
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
These findings were eye-opening to the business. They had assumed that successful agents just followed script. Instead what they found was that deviating from the script and engaging in conversation was what led to sales.
So, they redesigned their sales process. Agents were retrained to engage in conversation and present offers appropriately
Marketing began providing competitive data to agents prior to campaign launch and revised their offers based on these findings to make them less complicated. 20

21. Sales Conversion Rates
Customer Case Study
Sales Effectiveness
Results
Sales Conversion Rates
19% increase
in conversions!
The results were astounding. After implementing these changes sales conversions jumped from 65% to 77%, which was an all-time high and an improvement of 19%.
Without speech analytics there would have been no way to uncover the root cause of sales effectiveness. But with it, the reasons were quite clear. And the solutions were actionable. 21

22. Speech Analytics Delivers the Power of Why
What am I analyzing?
The factors that drive success or
failure in sales calls
Why
How
Execute
What
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
To summarize, this customer used speech analytics to analyze the factors behind why sales are successful
Automated root cause analysis surfaced the reasons why
The findings pinpointed specifically how to improve
And the customer executed a plan to enact changes 22

23. Speech Analytics Delivers Quantifiable ROI
Communications Provider
KPI
Past Performance
Performance three months after Verint deployment
Impact
Quality Scores
70%
81.20%
+16%
Improvement
Revenue Per Call
$0.33
$0.67
+103%
First Call Resolution
76.8%
79.1%
+3% Improvement
Customer Satisfaction /Executive Complaints
$82
Countless occasions to be proactive
Potential saving of $713K
Manager Productivity
1-2 call evaluations/month
5-6 call evaluations/month
+300%
Customer Churn
Analysis underway
25% reduction
Speech analytics has been vital to many organizations and has a quantifiable ROI. The communications provider in this slide has achieved the following results:

24. 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

25. 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