Customer Contact Center Analytics - Dealing with influx of structured and unstructured data

1. Customer Contact Centre
Analytics for Banks

2. Introduction
Customer service team for Retail Banking handles customer queries or
concerns raised through multiple channels – emails, calls, walk-ins, letters,
and faxes.
Barring high priority cases flagged by persistent or irate customers, most
cases aggregated on a periodic basis ranging from daily, weekly, monthly
or annually
The customer service team attempts to identify any patterns such as spikes
or drops in calls based on historical data from the reports
Inferences drawn highly dependent on the team’s experience and
knowledge of the prevailing conditions such as festive season, tax filing
period and so on

3. Challenge
Contact centers receive large streams of data - combination of audio calls and text communication.
Making sense of such largely unstructured data and taking real time action is a major challenge
Traditional analytics reveal trends about data such as calls received, average hold time, average call
duration, resolution rate, inquiry type etc.
Reports are mostly reactive - essentially giving a view of what has already occurred

4. Need
A mechanism that will enable the customer service team to
 adopt a proactive approach - alerting it to incidents that might occur in foreseeable future
 take pre-emptive measures to tackle any such situations
Resulting in both rapid turnaround time and better decision making capabilities

5. Use Case Scenarios
Industries are using Emerging Customer Service Analytics to
 Isolate revenue-related calls or other forms of communication
 Identify agent best practices
 Identify areas of gaps in knowledge of contact center personnel
 Identify cases for personalized agent coaching and training
 Predict root cause of customer dissatisfaction
 Identify what characteristics of a contact lead to costly repeat communications
 Identify other causes of customer churn e.g. better products and services of competitors
 Improved operational efficiency - Optimize call handling and first contact resolution
 Personalized cross selling and up selling
Source: http://www.cio.com/article/2396132/customer-relationship-management/big-data-analytics-gold-for-the-call-center.html

6. Why Sentiment Analytics?

7. Starting Point on the Analytics Journey
Analyze customer interactions
 Email requests and
conversations
 Audio calls - search
by keywords
 CRM Data
Analyze customer transactions
 Relationship data
 Portfolio data
 Transaction data

8. Analytics will reveal Trends

9. Big Data powered Analytics Platform
Audio
Calls
IVR
SMS
IVR
Email
Original Customer Data
Audio to Text Mining
Data Mining, Storage and Analysis
Big Data
Audio Mining Platform
• Linguistic Analysis
• Intention Analysis
• Dependency Analysis
• Trend Analysis
• Text Mining
Departmental
Regulatory
Management
Reports
Stakeholders
Audio mining platform to convert audio to text.
Big Data Analytics solution
 Uses transactions and interactions data to derive correlations and dependencies
 Reveals trends and patterns to alert team and direct focus on potential situation(s).

10. Thank You
https://www.linkedin.com/in/deepthirajanhttp://www.slideshare.net/RajanDeepthi