Embed Size (px)
Citation preview
Email Analytics for Customer Support
Centres Gathering Insights about Support Activities, Bottlenecks and Remedies
2
Enterprise Emails are exchanged for transacting business
Emails are rich repositories with not only conversation but history
of conversation
Emails contain information about stakeholders and their
participation in organizational activities
Remains business users’ top-most preference for exchange of
information
Why Emails?
Can be anonymized to ensure privacy is not violated
3
Email-based Support Center Work-flow
Complaint
Response
Request
for
Resolution
Resolution
Customer Service and Support
4
Email Mining Objectives
Measure Performance
Gain Process insight
Know consumer sentiment
Ensure
compliance with
Service Line
Agreements
Optimize
Operation Cost
Improve
customer
satisfaction
5
Email Analytics
Complaint
Response
Request for
Resolution
ResolutionResponse Time
Resolution Time
Frequent Problems
Resource Distribution
Resolution Efficiency
Improve Efficiency
Predict Problems
Sentiments
Resolution Process
Bottlenecks
6
A Sample Case Resolution through Mails
From X To SC: Please do this
From SC To Y: Please do this
From SC To X: Acknowledged
From Y To Z: Please do this
From Z To Y: We need 7
from X
From Y To X, Z : We Need 7
From X To Y, Z : Information
From Z To Y : Done
From Y To X, SC : Done
From Z To A, Y : Need
permission to update
From A To Z, Y : Permission
Granted
7
Challenges
A single support Case spread all over the mail client
8
Possible Mail Client ViewFrom X To SC: Please do this
From SC To Y: Please attend to this
From SC To X: Acknowledged
From Y To Z: Please attend to this
immediately
From Z To Y: We need 7 from X
From Y To X, Z : We Need 7
information
From X To Y, Z : Information
From Z To A, Y : Need Permission
From X To SC: Please do this
From X To SC: Please do this
From X To SC: Please do this
From SC To Y: Please attend to this
From Y To Z: Please attend to this
immediately
From X To SC: Please do this
From SC To Y: Please attend to this
From X To SC: Please do this
From Y To X, Z : We Need 7
information
From X To SC: Please do this
From Y To Z: Please attend to this
immediately
From X To SC: Please do this
From SC To Y: Please attend to this
From Z To A, Y : Need Permission
From Y To Z: Please attend to this
immediately
From X To SC: Please do this
From SC To Y: Please attend to this
From A To Z, Y : Permission
Granted
From Y To Z: Please attend to this
immediately
From X To SC: Please do this
From SC To Y: Please attend to this
From Z To Y: Done
From X To SC: Please do this
From Y To SC, X: Done
9
Email Analytics Pipeline
9
Gather data from header and body
Integrated Analysis- Content + Meta-data
Performance Analysis
Generate Actionable Insight
Implement & Measure
Import Emails into Analytics Platform
Gather All Mails of a single Support Case
3
4
5
0
2
1
10
From:
To:
Subject
Date & Time:
Body:
From:
To:
Subject
Date & Time:
Body:
Reconstructing Support Cases from Emails
From:
To:
Subject
Date & Time:
Body:
Complaint
From:
To:
Subject
Date & Time:
Body:
ComplaintFrom:
To:
Subject
Date & Time:
Body:
Response
From:
To:
Subject
Date & Time:
Body:
ComplaintFrom:
To:
Subject
Date & Time:
Body:
ResponseFrom:
To:
Subject
Date & Time:
Body:
Assignment
From:
To:
Subject
Date & Time:
Body:
ComplaintFrom:
To:
Subject
Date & Time:
Body:
ResponseFrom:
To:
Subject
Date & Time:
Body:
Assignment
From:
To:
Subject
Date & Time:
Body:
Resolution
Locate Duplicate messages
deep inside body
(Locality-Sensitive-Hashing)
Single Group
11
Email Analytics Pipeline
11
Gather data from header and body
Integrated Analysis- Content + Meta-data
Performance Analysis
Generate Actionable Insight
Implement & Measure
Gather All Mails of a single Support Case
3
4
5
0
2
1
12
Support Case Resolution Data
Resolution Time
Last MessageTime to respond
Second MessageProblem Statement
First Message
13
A Single Support Case
From:
To:
Subject
Date & Time:
Body:
Complaint
From:
To:
Subject
Date & Time:
Body:
Response
From:
To:
Subject
Date & Time:
Body:
Assignment
From:
To:
Subject
Date & Time:
Body:
Resolution
Response Time
Resolution Time
14
Email Analytics Pipeline
14
Gather data from header and body
Integrated Analysis- Content + Meta-data
Performance Analysis
Generate Actionable Insight
Implement & Measure
Gather All Mails of a single Support Case
3
4
5
0
2
1
15
Integrated Analytics
Unstructured Content
• Clustering – unsupervised grouping
• Frequent Phrases
• Categorization – Supervised Labels – Lexicon Based
• Sentiment Extraction
• Priority
Numeric Data
•Volume
•Duration
•Arrival Times
•Number of messages exchanged in a case
•Number of People involved
Case Level
Group Level
16
Complexity of Resolution Process
Complexity Measure
Support Case
f(#People
involved)
f(#Messages
exchanged)
f(#Independent Mail Chains)
f(#Hours to
resolve)
17
Complexity from Message Interaction Pattern
Mail Chains - Message Interchange Pattern
A Difficult Case
18
Complexity from People Involvement
Action Initiators Only
19
Complexity from Message Interaction Pattern
An Easy Case
20
Complexity from People Involvement
An Easy Case
21
Case-level Complexity
22
Content Analytics
Resolution
Last MessageInformation Exchange
Second MessageProblem Statement
First Message
Sentiments Issues Priority Problems
23
Content Extraction for better understanding of
Individual Cases
Phrases in first message
Phrases in subsequent messages
Insights
� High Priority Case
� Problem Type
� Several Messages exchanged
� Needs additional Information
� Needs many approvals
� Resolution Time was Long
� Several status updates were requested
� Responses not received on Time
24
1. Words from Subject(s)
2. Words from First Message
3. Co-occurrence-based
constraint Clustering
(to be presented at ICDM,
Shenzhen, China – Dec. 2014)
Aggregate Analysis – Content-based Clustering
25
Email Analytics Pipeline
25
Gather data from header and body
Integrated Analysis- Content + Meta-data
Performance Analysis
Generate Actionable Insight
Implement & Measure
Gather All Mails of a single Support Case
3
4
5
0
2
1
26
Performance Indicators – Aggregate
Response Time Histograms
Resolution Time Histograms
27
Compliance Analysis
28
Cluster Analysis
29
Insight into Frequent Problems
Cluster 1
Cluster 2
Cluster 3
30
Problem Arrival Patterns
Regular – High Volume
Surge
31
Cluster-wise complexity analysis
100% of
low complexity
100% of
messages in
this cluster are
getting
resolved with
low complexity
Around 10% of
messages in
this cluster
have high
resolution
complexity
Around 10% of
messages in
this cluster
have high
resolution
complexity
25% of
messages have
AVERAGE
resolution
complexity
25% of
messages have
AVERAGE
resolution
complexity
32
Sentiment Analysis
Impact -
High
Impact -
Low
33
Email Analytics Pipeline
33
Gather data from header and body
Integrated Analysis- Content + Meta-data
Performance Analysis
Generate Actionable Insight
Implement & Measure
Gather All Mails of a single Support Case
3
4
5
0
2
1
34
Process Insights
Problems &
Resolution
Volume
Complexity
Frequency
Impact (Priority
+ Sentiment)
35
Characterizing Resolution Process
Complexity
High
Average
Low
Volume
High
Average
Low
Impact
Good
None
Bad
Frequency
Regular
Irregular
36
Cluster Level Insights
Medium Priority
High Priority
37
Actionable Intelligence
• Set Alerts on problem phrases
• Prevent Outages
Volume = High / Average
Frequency = regular
Process = Difficult
Priority = High
• Process automationVolume = High
Frequency = Regular
Process = Easy
• Detect BottlenecksVolume = Low
Process = Difficult
Priority = High / Medium
38
Email Analytics Pipeline
38
Gather data from header and body
Integrated Analysis- Content + Meta-data
Performance Analysis
Generate Actionable Insight
Implement & Measure
Gather All Mails of a single Support Case
3
4
5
0
2
1
39
� Visibility into SLA Compliance led to improvement of
Performance
� Single-day resolution emphasized
� Automated Response generation
� Redistribution of Work-force
� Redefinition of Solution Process
� Single Point Approvals
Actions & Outcomes
� Resolution Compliance went up from 69% to 85%
� Target 95%
� Average Outage reduction – by 15% over a month
40
Emails capture enterprise processes
Email mining can be effectors of Process Monitoring and Analysis
How are things
What needs to be changed
The effects of a change
There is a lot that emails can offer without getting into privacy
and confidentiality
Conclusions
41
