Customer Contact Centre Analytics for Banks

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

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

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

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

Why Sentiment Analytics?

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

Analytics will reveal Trends

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).

Thank You

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