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Customertouch Points Videshi

Apr 14, 2018

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    Understanding Customer Motivation toNavigate Toward Desired Outcomes

    March 28, 2012

    1

    Mike Ashe, Vice President, Mattersight

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    About Mattersight

    Behavioral Analytics is a Cloud Based Managed Service to

    extract insights and drive operational value using allcustomer and employee interaction data

    Worlds most advanced linguistic processing algorithms

    Weve decoded the genome for human language in a

    business context

    Analyzed Over 1 Billion Interactions for CustomerSatisfaction, Service, Sales, Collections and Fraud

    Strongly centered on behavior:

    Behavior = Personality (who you are)

    + Actions (what you do)

    2

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    Millions of customer and employee interactions occur across anorganization on a daily basis

    Highly variable customer expectations and behaviors

    Highly variable employee performance

    Sheer size and complexity of collecting, descrambling andreorganizing voice, desktop and multi-channel interaction data canbe overwhelming, making it very difficult to understand and act onthese insights

    3

    The Customer Experience Challenge

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    Customer Experience Spans Multiple Channels

    8:47 am 8:52 am 8:58 am 9:02 am 11:10 am 11:20 am

    4

    StatementReceived Via

    Email

    A Multi Channel Customer Interaction

    Checks onMobile App

    Checks onWebsite

    Talks toCSR

    ReceivesRevised

    Statement

    Talks toAnother

    CSR

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    Behavioral Analytics Solution Overview

    5

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    Predicting Customer Outcomes and Behaviors

    6

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    What is Predictive Analytics?

    7

    Desirable OutcomeUndesirable

    Outcome

    Customers with Measurable Experience

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    Predictive Customer Satisfaction vs. Surveys

    Issues with Customer Satisfaction Surveys

    Sample sizes often arent statistically valid Respondent population often doesnt match typical caller population

    Lack actionability to the specific process or employee level

    Expensive and can have hidden costs (time spent soliciting surveys)

    Using Analytics to Predict Customer Satisfaction

    Empathy

    Caller Distress

    Customer Engagement

    Selling Effort

    Policy Information

    Customer Personality

    Demographics

    Call Type

    Product Type

    EmployeeAttributes

    ExperienceAttributes

    BusinessProcess

    CustomerAttributes

    Tenure

    Personality

    Proficiency

    8

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    Driving Improvement in Employee Performance

    9

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    Significant Employee Variability Impacts Satisfaction

    10

    Distribution Identifies Lowest Performing Agents

    Agent

    Poor/Very Poor Customer Satisfaction per 1,000 Customer Interactions

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    Identifying Best Coaching Opportunities by Employee

    11

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    Routing Callers to the Best Available Agent

    12

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    Caller with 5Recent Calls allwith Distress

    Caller with

    EscalationLikelihood of

    82%

    Caller with 79%Likelihood of

    Attrition

    Customer whoCalls Daily toCheck RoutineOrder Status

    Caller

    13

    Predicting Optimal Pairing of Caller and Employee

    Best AvailableAgent Handling

    Callers inDistress

    Employee

    Agent withLowest Escalation

    Likelihood forthat Type of

    Caller

    Agent withLowest

    Predicted

    Attrition for thatType of Caller

    Low Performingor Low Tenure

    Agent

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    Case Study Predictive Analytics & PerformanceManagement

    14

    Business Objectives Improve customer experience

    during the sales and service process

    Increase sales effectiveness

    Deployment Behavioral Analytics Service and

    Sales Application with PredictiveAnalytics & PerformanceManagement

    700 service agents across 3 sites 800 sales agents across 3 sites

    Business Results 10% increase in customer

    satisfaction (CSAT) across salesand service within 6 months of

    deploying predictive CSATcombined with performancemanagement

    5.5% decrease in costs within 12months across all service agents

    45% improvement in sales closurerates within 12 months across all

    sales agents

    Our client is one of the United States largest publicly held propertyand casualty insurers

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    Case Study

    Business Objectives Improve call center efficiency and

    effectiveness by reducing AverageTalk Time (ATT) and Average ContactTime (ACT)

    Deployment Behavioral Analytics Predictive

    Routing Application

    200 agents

    Business Results Immediate 29% ATT reduction and

    13% ACT reduction for calls routedby Predictive Routing

    15

    Our client provides health care insurance for approximately 3 millionmilitary members, retirees and their families

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    Driving Value Across an Organization

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    White Papers Available

    17

    Mike AsheVice President, Mattersight CorporationMobile: [email protected]

    mailto:[email protected]:[email protected]