4. CONTENT
CATEGORIZATION
Transcribe and index
entire call and extract
concepts
Analyze impact on known
issues
(lower false alarms)
Three Levels of Speech Analytics
KEYWORD
SPOTTING
Spot 20-200
defined words
Find isolated
calls of interest
(high false alarms)
BUSINESS VALUE
INTELLIGENCE
ROOT CAUSE
ANALYTICS
Mine categorized calls and
suggest root cause
Find out what you do not
know to look for
5. Word Spotting Categorization Root Cause Analytics
Technician did not show
Received wrong information
Did not receive credit
Offer not clear to customers
Product does not work well
Product is too expensive
Perceived as better value
Product quality driving churn
Price attracting customers
Large
sample of
customer
interactions
Customer
complaints
Interactions
about new
product
offering
Interactions
involving
competition
Another way of looking at it:
Word Spotting, Categorization, Root Cause
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
SalesSales
Back OfficeBack Office
FraudFraud
CollectionsCollections Risk
Management
Risk
Management
ComplianceCompliance
R&DR&D
MarketingMarketing
Contact
Center
Contact
Center
Intelligence
from Customer
Interactions
Intelligence
from Customer
Interactions
8. 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
Customer Case Study
• Improve First Contact Resolution
First Contact
Resolution
9. Classifies calls via automated speech recognition and categorization technologyIdentifies key reasons why customer issues were not resolved
Customer Case Study
How it works
First Contact
Resolution
Success
(65%)
10. Terms automatically surfaced indicating root causeSurfaces root cause of first call resolution issues
Customer Case Study
How it works
“calling back
about my claim”
“calling back
about my claim”
“Check with
my
supervisor”
“Check with
my
supervisor”
“I don’t know”“I don’t know”
First Contact
Resolution
“waiting for a
claim form”
“waiting for a
claim form”
11. 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• Outdated policies reviewed and changed and agents were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
Customer Case Study
First Contact
Resolution
13. 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
Customer Case Study
First Contact
Resolution
14. Speech Analytics Delivers the Power of Why
How can I improve
performance?
• Review outdated policies
• Empower agents
• Revise claim forms
• Improve frontline processing
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%
15. 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
Customer Case Study
• Pinpoint best (and worst) selling circumstances and behaviors
• Improve up-selling/cross-selling capabilities
• Increase closing rates
Sales Effectiveness
16. Identifies the most effective approaches for agents when selling to customersClassifies calls via automated speech recognition and categorization technology
Customer Case Study
How it works
Sales
Effectiveness
Success
(65%)
17. Automatically detects sales success and failures based on key phrases and metadata
Customer Case Study
How it works
Sales
Effectiveness
18. Surfaces root cause of negative sales performanceTerms automatically surfaced indicating root cause
Customer Case Study
How it works
“I am not sure that
we offer that…”
“I am not sure that
we offer that…”
“I’m
confused”
“I’m
confused”
“Are you interested
in the choices I
presented?”
“Are you interested
in the choices I
presented?”
Sales
Effectiveness
19. Terms automatically surfaced indicating root causePositive behaviors are reinforced, negative behaviors are correctedSurfaces root cause of positive sales performance
Customer Case Study
How it works
“May I ask you
a few
questions?”
“May I ask you
a few
questions?”
“the best deal
for you is…”
“the best deal
for you is…”
“this is a better
offer
because…”
“this is a better
offer
because…”
Sales
Effectiveness
20. 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
Customer Case Study
Sales
Effectiveness
22. Speech Analytics Delivers the Power of Why
How can I improve
performance?
•Train agents to qualify
•Create simple marketing offers
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%
23. 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%
Improvement
First Call Resolution 76.8% 79.1%
+3%
Improvement
Customer Satisfaction
/Executive Complaints
8700 @ $82
Countless occasions to be
proactive
Potential saving
of $713K
Manager Productivity 1-2 call evaluations/month 5-6 call evaluations/month
+300%
Improvement
Customer Churn Analysis underway Analysis underway 25% reduction
Speech Analytics Delivers Quantifiable ROI
Communications Provider
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
Editor's Notes
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.
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.
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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
Speech analytics has been vital to many organizations and has a quantifiable ROI. The communications provider in this slide has achieved the following results:
