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

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

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 20-200 defined words

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

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

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

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

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

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

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

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

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

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

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

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

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

! 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

! 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

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

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

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

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 8700 @ $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:

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

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