MDBW-Cappius-Speaker Presentation - Enterprise_Speech_Analytics_v5

Confidential Information www.cappius.com

Enterprise Speech Analytics Actionable insights from customer interactions in a Service Center

Presented by

Name: Surya Putchala

Title: Head, Big Data Analytics


1. Customer experience: overview

2. Need for the Solution

3. Enterprise Speech Analytics Solution

4. Architecture

5. Features

6. Benefits

2

Agenda

Confidential Information www.cappius.com 3

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

Confidential Information www.cappius.com 4

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.

1 2


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.

5

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.


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


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

7


Speech Analyzer in Action

8


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


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

10


Thank You

Contact us,

www.cappius.com

[email protected]

http://www.cappius.com/

mailto:[email protected]

Documents

MDBW-Cappius-Speaker Presentation - Enterprise_Speech_Analytics_v5