2. Nemesysco Ltd. Privately held security-oriented company, founded in 2000 by Amir Liberman, its CEO and the LVA technology inventor Headquartered in Israel, network of distributors and centers around the world Developing advanced emotion detection technologies for the past 14 years Used for non-invasive investigation tools, fraud and loss prevention systems, CRM applications, consumer products and psychological assessments Nemesysco’s products and services are based on Layered Voice Analysis (LVA), a unique and patented voice analysis technology

3. About Nemesysco Security Police use HLS Intelligence Commercial Human resources Call centers Fraud Prevention Entertainment TV & ITV Cellular services PC apps. Medical Stress meter Phobia free Mental state analysis One technology, numerous uses:

4. The traditional quality monitoring approach Current quality monitoring methods Random and subjective monitoring of calls (both in real-time and from recordings) Customers’ feedback at the end of a call or through periodical surveys The relatively new “Keyword” spotting and BI tools Inspected conversations ~5% Uninspected conversations ~95%

5. The traditional quality monitoring approach Disadvantages of the current tools Loss of information as only a small percentage of calls can be monitored Non-instantaneous results Lack of objective criteria for quality assurance and assessing customer satisfaction Customers do not always explicitly state their preferences or feelings about their interactions with your company’s CSR agents High costs of monitoring and evaluating the Quality of Service New systems require intensive training and are often context dependent

6. Nemesysco’s QA5 SDK is… An Emotion-Detection engine designed for manufacturers and system integrators that uses Nemesysco’s proprietary voice analysis technology to detect and measure wealth of emotions, such as: Anger Stress Concentration Confusion Happiness Sadness Energy and other relevant emotions that may arise in a contact center conversations…

7. Nemesysco’s QA5 SDK is… Targeted at Call Center vendors and large software integrators Provided as an SDK for WINDOWS based platforms Can process any number of calls simultaneously in real-time Can process pre-recorded files from the database Analyze separate voice channels for the agent and the customer Produces pre-defined scores for “Call Priority” and “Agent Priority” Allows development of any other type of Business logic that may be relevant. Presents the customer’s “Emotional Profile” to meet marketing needs. Includes a learning engine that enables the system to learn additional emotions, cognitive states or Recognize situations which are of special interest as they develop

8. QA5 Operation Modes Possible uses of the QA5 SDK: Online/Real time analysis of all concurrent calls Offline/post processing of previously logged calls Automated reports generation and workforce optimization Marketing assistance and customer “Emotional profiling” Training and feedback systems

9. QA5 Operation Modes Online Mode Voice data from multiple simultaneous conversations is streamed to a designated QA5 server in the local network and analyzed in real-time Identifies elevated levels of negative emotions (i.e.: anger, rising stress, growing discomfort) during the agent - caller interaction Alerts can be displayed on the supervisor’s screen in real time, to enable immediate affirmative action

10. QA5 Operation Modes Offline Mode Performs analysis of pre-recorded calls to identify and measure the emotional build of the agent-caller interaction and flags “priority” calls based on trainable criteria Identify patterns of “problematic” calls using data mining. Evaluate specific agents, as well as overall call center performance

11. QA5 Operation Modes Automated reports Generate general site performance reports and set objective goals. Monitor agent’s performance on a monthly, weekly, daily and even hourly basis Reward the best performers, keep a closer look on selected others. Optimize shifts selection automatically, monitor agents’ churn and drop out states. Identify topics relevant for additional training

12. Implementation example Supervisor screen  Displays all active operators  Color “Red” for high priority calls  Overall display of customers emotional level  Overall display of agents emotional level

13. Implementation example Supervisor screen  Zoom into priority calls  See emotional behavior of both agent and caller  Use additional tools and history records in conjunction with the emotional analysis

14. Implementation example History review  List all high priority calls for a selected period  Listen to the calls in order to decide on proper follow-up

15. Implementation example History review  List calls for a selected period  See agent’s performance for the period (identify fatigue and low energy)

16. Mobile TeleSystems OJSC, Russia MTS is the biggest cellular operator in Europe with over 90 million customers. ( http://www.mtsgsm.com) http://www.mtsgsm.com Recently finished a 7-month test with QA5 Results: Method Ran analysis on 81,000 calls. Thoroughly researched 3,000 calls Compared QA5 analysis to MTS contact center supervisors analysis of the same calls. Findings 77% compliance between QA5 analysis and MTS supervisors on all calls (marked “Good” / “Problematic” / “Bad”) 95% compliance on marking of “Good / Normal” calls

17. Mobile TeleSystems OJSC, Russia Conclusions: For offline quality check: Using QA5’s “Call Priority” score, listening to the top 1.8% of the QA5 marked calls was enough to capture 40% of the “negative customer experience” calls. Using QA5’s “Agent Priority” score, review of the top 3% of the QA5 marked calls was enough to capture 30% of “poor call handling” by operator. Using QA5 can reduce to 25% the time supervisor spends listening to calls and allows him to listen to up to 9 times more calls. Improving operators performance Managers have a better control over operator performance. Operator can receive feedback from the system for self-improvement. Overall, QA5 exposes a wealth of new information for a true call quality measurement which did not exist before.

18. Mobile TeleSystems OJSC, Russia Further conclusions: It was found that analyzing a call as “Bad” is very subjective to each human supervisor. Every supervisor has his own criteria of what is a “Bad” call QA5 can be trained to identify “Bad Calls” Once done, QA5’s ability to produce a stable analysis over a period of time is higher than the contact center’s staff

19. Thank you