The Client is a large provider of group insurance especially in the US. Hundreds of queries regarding claims and other related service are made by customers at the company’s contact Centre every day. The objective of the project was to analyze the huge volume unstructured texts in contact centre agent comment to assess the key drivers of service call volume and other related aspects

1. Identification of key reasons for calling contact centre and
drivers of low first call resolution based on agent notes
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• Agent notes are textual, and
contains many spelling
issues. Publicly available
lexicons & dictionaries are
used to enable this
• The history of conversations
were broken into single
discussions to allow
separate analysis
• Semantics based text
mining methods are used to
segment discussions into
various topics which allowed
to identify various reasons
for calls.
• Analysis of service instances
needing multiple calls
provided insights on key
bottlenecks in the claim
management process.
• Analysis of time taken to
resolve various issues
helped to set right
expectations with customers
• Insights obtained
from the exercise
helped the insurance
company identify key
bottlenecks in the
process and
conceptualize
measures to
significantly decrease
the service instances
as well as improve
customer
satisfaction.
Data Sources Approach Outcome
Internal unstructured
query logs from call
centre Agents
The Client is a large provider of group insurance especially in the US. Hundreds of queries regarding claims and other
related service are made by customers at the company’s contact centre every day. The objective of the project was to
analyse the huge volume unstructured texts in contact centre agent comment to assess the key drivers of service call
volume and other related aspects .
Objective
Report:
Summary
Statistics
Data Structuring Identify Key topics Analyze drivers of delay
Report:
Identify
Problems
and trends