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