This document discusses an enterprise speech analytics solution presented by Surya Putchala. The solution analyzes customer service call audio data to provide actionable insights. It can interpret voice to text, analyze sentiment and mood in real-time, and score service representatives. Analytics dashboards provide insights into topics, response times, top customers and representatives, call volumes, lengths, and more. The benefits of the solution include improving customer experience, service quality, reducing costs, and increasing revenue through up-selling and cross-selling while reducing attrition.

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Enterprise Speech Analytics
Actionable insights from customer interactions in a Service Center
Presented by
Name: Surya Putchala
Title: Head, Big Data Analytics

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1. Customer experience: overview
2. Need for the Solution
3. Enterprise Speech Analytics Solution
4. Architecture
5. Features
6. Benefits
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Agenda

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Customer experience: overview
Business
Intelligence
CRM
Intelligence
Social Media
Intelligence
Market
Intelligence
Enhanced
Consumer
Experience
Transactional
Intelligence
Customer
Demographics
Market/Product
Trends, Recalls,
Feedback
Consumer
Sentiment
Customer Insights
With big data and analytics you
can combine all information to
extract insight in real-time and
create an actionable view of
each customer to craft an
exceptional
experience
 Need to integrate multi-Intelligence data
 Increasingly, more intelligence data is
unstructured in text formats, video and
audio.
Traditional
Approach
Enhanced
Approach
Service call
In store, in person
touch points
Moments
of truth

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Why Speech Analysis?
In a Service Call center, the audio call data is archived for reference and not mined for customer
interactions and extract value from them. It is exhausting to hear the tapes, hence this source of rich
data is routinely ignored.
Often this data is never accessed, unless there is a special situation such as an escalation or a dispute.
Most information is hidden and should be mustered from the customer call data. Since, it is Audio,
many Enterprises give it the least preference.
The efficiencies will further improve
by knowing the trending call center
enquiries and deploying the right
person for answering the right
issue.
Customer service will enhance
tremendously by knowing the
moods of the customer and intensity
of conversation which will allows
taking necessary actions to provide
better service for the customer.
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Archival Analysis
This Solution is applicable in cases such as :
1. identification of the trending inquiries
2. identifying common pain points and improve
understanding of a customer behavior.
These levers could pre-empt actions that will result
in preventing customer dissatisfaction and increase
customer delight; achieving a superior contact
center performance.
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Enterprise Speech Analytics Solution
Synchronous Analysis
The process :
1. Capture the voice stream by applying various
text and audio processing techniques
2. Understand the sentiments as well as mood of
the conversation for a customer service call.
This is accomplished real-time and continuously
monitored and tracked; which allows the service
center the ability to provide superior customer
engagement, handle situations at the right time to
mitigate attrition and escalations.

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Solution Architecture
Google
Web
Speech API
Transcript
Polarity
Trends Ticker
Mood
 Voice data/ Analysis
 Transcripted text/Summary
Beyond
Verbal API
Streaming
 Data Collection
 Data Processing
NLP  Sentiment
 Mood
Real time
Call Signal
Archived
Audio
Apache Storm
Machine
Learning
Real- time
Mood
Transcripted Text
Analytics
Scoring
 Service Rep Scoring
 Outlier detection
 Session benchmarking
 Trending Topics
 Floor Analysis
 Problem Resolution Analysis
 NPS – Net Promoter Score
 Customer Traffic Analysis
Persist real-time data as well as
run predictive Analytics
Customer Sentiment tracking at pre-configured intervals (default 15 sec)
Knowledgebase
6
Core Engine
Visualization
Call Conversation
Voice
Expression
Audio Extract
from Video

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Features
Interprets voice to text (on the fly or from the archives)
 Accent aware speech to text conversion
 Summary and Conclusions of Call sessions
Polarity Deciphering the Feelings and Meaning of a conversation
Mood Analysis Analyze mood of the customer in real-time
Knowledgebase
 Two-way voice data (Customer and Support Executive)
 Results of voice analysis (quantitative and qualitative)
 Transcripted Text
 Polarity, Sentiment, Word clout
 Summary and conclusions data needed for Analytics
Service Rep Scoring
Evaluation Score how well executive handled with customer
 Call Forwarding to most appropriate Staff
 Help to Identified Most Efficient Employee to take up the calls
Effective Handling
 Real-time suggestions for customer executive
 Optimization of the responses in Real-time
 Trending issues, best relevant answer
 Audio sniffer with configurable “key“ words (used for escalations)
Transcription
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Speech Analyzer in Action
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Analytic Dashboards
Service Rep Scoring
Customer Call Analysis
Topic mining and trending
Average Response Time
Top 10 Customer Service Representatives
Top 100 Customers by # Inbound Calls
Average Number of Outbound Calls to Sell a Product
Change in Customer Satisfaction Rating
Variance in Call Volume by Customer Segment
Forecasted Call Volume
Average Call Length
Call Type (Sales, Service) as % of Total Inquiries
Session Benchmarking
Floor Analysis
Customer Experience
Customer Segmentation
Customer Mood, Sentiment, Satisfaction
Net Promoter Score
Identification of Common Concerns
Trending Topics
Queuing Analysis
Statistical Analysis
Ranking and Scoring
Document Similarity
Anomaly Detection
Customer Segmentation
Anomaly Detection
Analytical Themes

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 Improve the customer experience
 Improve service quality
 Reduce operating expenses and save money
 Revenue enhancement with up-sell and cross-sell
 Reduce Customer Attrition
Benefits
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Thank You
Contact us,
www.cappius.com
sales@cappius.com