call center chat analytics
TRANSCRIPT
©2016 RapidMiner, Inc. All rights reserved. - 1 -
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
©2016 RapidMiner, Inc. All rights reserved. - 2 -
TMAgenda• Introduction to RapidMiner• Business Drivers for Call Centers• Call Center Chat Analytics– Chat Process & Solution Architecture– Algorithms and Methodologies
• Solution Demo• Q&A
©2016 RapidMiner, Inc. All rights reserved. - 3 -
TM
#1 Open Source Data Science Platform
#1 Marketplace for Data Science Experts
Data Science Behind Every Decision
The key to innovation and competitive advantage lies in the power of data science. Invest in the right tools and the right skills to uncover new opportunities – and change the
world
Our Mission
©2016 RapidMiner, Inc. All rights reserved. - 4 -
TMData Science Behind Every Decision
#1 Open Source Data Science
Platform
#1 Marketplace for Data Science
ExpertsSelf-service machine
learning
Lightning-fast creation of models
Frictionless operationalization
On-demand consultants (“Uber”)
Domain expertise in every industry
Global presence
©2016 RapidMiner, Inc. All rights reserved. - 5 -
TMUnified Data Science Platform
Data PrepSpeed & optimize ALL
dataexploration, blending
& cleansing tasks
Operationalize
Easily deploy & maintain models and
embed analytic results
Model & Validate
Apply machine learning to rapidly prototype & confidently validate predictive models
Embed results in all types of
business apps & data
visualization tools
Incorporate all types of
data
ACCELERATES TIME TO VALUE
©2016 RapidMiner, Inc. All rights reserved. - 7 -
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
©2016 RapidMiner, Inc. All rights reserved. - 8 -
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)
©2016 RapidMiner, Inc. All rights reserved. - 9 -
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
©2016 RapidMiner, Inc. All rights reserved. - 10 -
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
©2016 RapidMiner, Inc. All rights reserved. - 12 -
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
©2016 RapidMiner, Inc. All rights reserved. - 13 -
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.
©2016 RapidMiner, Inc. All rights reserved. - 14 -
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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©2016 RapidMiner, Inc. All rights reserved. - 16 -
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
©2016 RapidMiner, Inc. All rights reserved. - 24 -
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
©2016 RapidMiner, Inc. All rights reserved. - 25 -
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
©2016 RapidMiner, Inc. All rights reserved. - 26 -
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
©2016 RapidMiner, Inc. All rights reserved. - 27 -
TM
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Surya PutchalaLead, Big Data Analytics
Tom OttMarketing Data Scientist