call center chat analytics

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TM

Call Center Chat AnalyticsAchieving actionable insights from customer interactions

in a Service Center

Surya PutchalaLead, Big Data Analytics

Cappius Technologies

Tom OttMarketing Data Scientist

RapidMiner


TMAgenda• Introduction to RapidMiner• Business Drivers for Call Centers• Call Center Chat Analytics– Chat Process & Solution Architecture– Algorithms and Methodologies

• Solution Demo• Q&A


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ExpertsSelf-service machine

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dataexploration, blending

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Embed results in all types of

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Business Drivers for Call Centers


TMCappius Call Center Analytics Solution

The dynamics of the call center are changing because of the adoption of chats and other text channels. The drivers are :• Smartphone usage for chat• Multi-task chat windows• Reducing Service agent visits • Cheaper cost

Real-time Speech Post ChatPost Call Speech Live Chat Support Email Social Media


TMCurrent Chat Process

The chat is set up for each customer. It captures name of the user, product/service the customer wants and, specific issue about the product.

Pre-chat Info Chat Post chat Survey

The post chat survey are direct questions about the chat experience, if they would want the chat forwarded, reasons for dissatisfaction and overall satisfaction.

Information from Chat Meta-data Total lines (Operator/Customer ) Agent-Customer Response Times, Handle times Survey statistics Characteristics (Volumes, averages, abandoned ,

duration, word counts /lines by customer/agent) Utilization (engaged time, idle time, online time)


TM

• Measure Customer Satisfaction

Goals

• Identify Top Issues• Customer Preferences• Upsell/Cross-sell needs

• Improve quality of service

BenefitsFrom thousands of calls and chats, locate the troubled ones (negative sentiment) and address the issues

Explore the cross-sell and up-sell opportunities with effective customer handling

Measure the effectiveness of agents. Get actionable insights to train or reassign agents to various teams

Sentiment Analysis

Trending Topics

Agent Scoring

Solution

Cappius’ Chat Solution


TM

LivePerson

Salesforce ChatLive Agent

Zendesk Chat

Atlassian Service Desk

Data Access

Trends Ticker

Data Blending & Cleansing

Voice data/ Analysis Transcribed text/Summary

NLP

Sentiment Agent Scoring Topic Mining

Modeling

Machine Learning

Scoring

Persist real-time data as well as run predictive Analytics

Knowledgebase

Core Engine - RapidMiner

Interactive Dashboards & Reports

Call Center Agents

Others

Cappius Solution

Chat Analytics Solution Architecture


TM

Algorithms and Methodologies


TMChat Analyses

Text Categorizationo N-gram o Entity Extraction and taggingo Text Rank

Apply Evaluation rules (based on customer specific rules)

Text Analysis

Topic graphso Logical Chunking ( blocks of

conversation)o Topic Sentiment

Structure of the Chat

Chat Scorecards

Analyze and Quantify

Select Chats ( for Audit and Conformance)

Sentiment Analysis

Trending topics

Detect o Themes detectiono Insults, Sarcasm o Other emotionso Fatal occurrences

Qualitative Analysis o Mechanical Turk o Concept Codingo ML based on validation

Identify process adherence

Derive Generic KPIs

Outliers


TMSentiment Scoring

Customer Sentiment

Conversation

Sentiment

Agent Sentiment

Model Evolution

Rules

Statistics Analysis

Machine Learning

Artificial Intelligence

Model Components

Current State

We consider the last three sentences of the customer to be very important predictor of his satisfaction and has a great predictive power of deciphering customer satisfaction. We weigh this factor higher.

There can be multiple themes in a chat conversation. The mood, sentiment, tempo of the conversations could be modelled as a network. This is inferable factor but has a great impact on arriving at a sentiment score.

AveragesWeighted AveragesSmoothing

The ability of an agent to convince the customer and close an issue successfully with a customer can yield a great customer satisfaction. Hence the sentiment of a Chat is also a predictor for Customer Satisfaction.


TMAgent ScoringExtract raw scores of the Parameters from the Chats

Determine the frequencies of non conformance wrt the total volume

Rank the parameters by difficulty

Determine the weights of each parameter (Sum of Ranks)

Determine the weights

Normalize on the scale of 1-100

Score the parameters. Sum of all the scores

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Chat Solution Demo


TMRapidMiner Implementation1. Data Ingestion2. Data Cleansing and Feature Extraction3. Modelling and ML Algorithms

a. Customer Sentiment analysisb. Agent Scoringc. Topic Modeling and Mining

4. Persistence5. Visual Dashboards


TMDemo – Process Design


TMOverall Sentiments


TMSessions by City


TMSession Durations


TMAgent Chat volumes


TMAgent Grammar Performance


TMAgent Performance by Chat Sentiment


TMWhy RapidMiner?1. Performance and Scalability

a. In memory processingb. Large Scale Data processing (Clustered Environment)

2. Maintainability and Model Management3. Developer Productivity and Environment4. Integration and Data Persistence5. Capabilities

a. Connectors to variety of Data Sources (Input / Output formats)b. Ability to extensively Cleanse and Munge datac. An extensive array of Modeling Techniquesd. Built in Schedulers ( For data ingestion and report generation)e. Extensibility of functionality


TMKey Takeaways Chat Analytics solution addresses call center challenges

through Sentiment Analysis, Agent Scoring and Topic Detection

Improves productivity by rapidly surfacing customer and business issues and opportunities

Serves as an early warning system to identify issues before they escalate and impact a large number of customers

Helps you optimize customer engagement and service strategies by revealing trends, opportunities, potential issues, and the root cause of customer perceptions so that you can take action quickly


TMCappius TechnologiesCustomer Insights

Understand and engage your customers

Customer Lifetime ValueRenewal Analytics

Segmentation

Sales PipelineR

Intelligent Sales Pipeline forecasting

Sales Rep ScoringOpportunity Scoring

Smart Lead Allocation

Industries Automotive Life Sciences Retail & Consumer

goods Manufacturing Telecom Insurance Utilities

Customer Interaction Call Center InsightsRetention Incentives and Rewards Cross-sell and Upsell

Marketing Analytics Social Network Analytics Recommendations Next Best action AnalyticsSales Sales Pipeline Analytics

Acquire Understand Retain

Customer Experience Analytics

Call Center Analytics

Optimize Service Centre Operations

Real Time Mood AnalysisAgent Scoring

Voice Transcription

Sherlock

Anomaly IdentificationInvestigative Intelligence

Analysis

Solutions

RapidMiner Consulting

Customer Behavior Customer Segmentation Churn Analysis Customer Propensity AnalysisCustomer Loyalty Customer Lifetime Value

Use Cases Predictive

Maintenance Risk Modeling Product Propensity Sentiment Analysis Quality Assurance Next Best Action Churn Prevention


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TEST DRIVE CAPPIUSCALL CENTER ANALYTICS SERVICES

[email protected]

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Surya PutchalaLead, Big Data Analytics

Tom OttMarketing Data Scientist

call center chat analytics

Technology