• Like
Interaction Mining: the new frontier of Call Center Analytics
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Interaction Mining: the new frontier of Call Center Analytics

  • 648 views
Published

Paper presented at the DART 2011 workshop in Palermo. The paper introduces a new type of call center analytics based on interaction mining. It shows how advanced metrics and KPIs for call center …

Paper presented at the DART 2011 workshop in Palermo. The paper introduces a new type of call center analytics based on interaction mining. It shows how advanced metrics and KPIs for call center quality management can be implemented through interAnalytics NLP technology.

Published in Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
648
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
21
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Standard metrics with nice visualizations…

Transcript

  • 1. Interaction Mining: the new frontier of Call Center Analytics
    Vincenzo Pallotta
    Rodolfo Delmonte
    LammertVrieling
    David Walker
    © 2011 interAnalytics
    1
  • 2. Outline
    Call Center Analytics
    Automatic Argumentative Analysis for Interaction Mining
    Experiments with Call Center Data
    Conclusions
    © 2011 interAnalytics
    2
  • 3. Call Center Analytics
    © 2011 interAnalytics
    3
  • 4. Call Center Analytics
    Call centers data represent a valuable asset for companies, but it is often underexploited for business purposesbecause:
    it is highly dependent on quality of speech recognition technology
    it is mostly based on text-based content analysis.
    Interaction Mining as a viable alternative:
    more robust
    tailored for the conversational domain
    slanted towards pragmatic and discourse analysis
    © 2011 interAnalytics
    4
  • 5. Mainstream Call Center Analytics
    © 2011 interAnalytics
    5
    Does not unveil real insights about customer satisfaction
  • 6. Call Center Analytics: metrics and KPIs
    Agent Performance Statistics:
    Average Speed of Answer, Average Hold Time, Call Abandonment Rate, Attained Service Level, and Average Talk Time.
    Quantitative measurements that can be obtained directly through ACD (Automatic Call Distribution), Switch Output and Network Usage Data.
    Peripheral Performance Data:
    Cost Per Call, First-Call Resolution Rate, Customer Satisfaction, Account Retention, Staff Turnover, Actual vs. Budgeted Costs, and Employee Loyalty.
    Quantitative, with the exception of Customer Satisfaction that is usually obtained through Customer Surveys.
    Performance Observation:
    Call Quality, Accuracyand Efficiency, Adherence to Script, Communication Etiquette, and Corporate Image Exemplification.
    Qualitativemetrics based on analysis of recorded calls and session monitoring by a supervisor.
    © 2011 interAnalytics
    6
  • 7. Four objectives
    Identify Customer Oriented Behaviors,
    which are highly correlated to positive customer ratings (Rafaeli et al. 2007);
    Identify Root Cause of Problems
    by looking at controversial topics and how agents are able to deal with them;
    Identify customers who need particular attention
    based on history of problematic interactions;
    Learn best practices in dealing with customers
    by identifying agents able to carry cooperative conversations.
    © 2011 interAnalytics
    7
  • 8. Argumentative Analysis for Interaction Mining
    © 2011 interAnalytics
    8
  • 9. Argumentative Structure of Conversations
    REJECT(alternative)
    1708.89 John: not before having one come here and have some people try it out . {s^arp^co} 61b.62a
    PROVIDE(justification)
    1714.09 B: because there's no point in doing that if it's not going to be any better . {s} 61b+
    PROVIDE(justification)
    1714.09 John: because there's no point in doing that if it's not going to be any better . {s} 61b+
    ACCEPT(justification)
    1712.69 David: okay . {s^bk} 62b
    PROPOSE(alternative)
    1716.85 John: so why don't we get one of these with the crown with a different headset ? {qw^cs} 63a
    PROVIDE(justification)
    1722.4 John: and - and see if that works . {s^cs} 63a+.64a
    1723.53 Mark: and see if it's preferable and if it is then we'll get more . {s^cs^2} 64b
    1725.47 Mark: comfort . {s}
    ACCEPT(alternative)
    1721.56 David: yeah . {s^bk} 63b
    1726.05 Lucy: yeah . {b}
    1727.34 John: yeah . {b}
    DISCUSS(issue) <- PROPOSE(alternative)
    1702.95 David: so - so my question is should we go ahead and get na- - nine identical head mounted crown mikes ? {qy} 61a
    Why was David’s proposal on microphones rejected?
    © 2011 interAnalytics
    9
  • 10. Automatic Argumentative Analysis
    Based on the GETARUNS system1.
    Clauses in Turns are labelled with Primitive Discourse Relations:
    statement, narration, adverse, result, cause, motivation, explanation, question, hypothesis, elaboration, permission, inception, circumstance, obligation, evaluation, agreement, contrast, evidence, hypoth, setting, prohibition.
    And then Turns are labelled with Argumentative labels:
    ACCEPT, REJECT/DISAGREE, PROPOSE/SUGGEST, EXPLAIN/JUSTIFY, REQUEST EXPLANATION/JUSTIFICATION.
    1 DelmonteR., Bistrot A., Pallotta V.,Deep Linguistic Processing with GETARUNS for spoken dialogue
    Understanding. Proceedings LREC 2010 (P31 Dialogue Corpora).
    © 2011 interAnalytics
    10
  • 11. Evaluation
    ICSI corpus of meetings (Janin et al., 2003)
    Precision: 81.26% Recall: 97.53%
    Delmonte R., Bistrot A., Pallotta V.,Deep Linguistic Processing with GETARUNS for spoken dialogue
    Understanding. Proceedings LREC 2010 (P31 Dialogue Corpora).
    © 2011 interAnalytics
    11
  • 12. Experiments with Call Center data
    © 2011 interAnalytics
    12
  • 13. Rationale: implement the four objectives
    Identify Customer Oriented Behaviors,
    Identify Root Cause of Problems
    Identify customers who need particular attention
    Learn best practices in dealing with customers
    © 2011 interAnalytics
    13
  • 14. The Data
    Corpus of 213 manually transcribed conversations of a help desk call center in the banking domain.
    Average of 66turns per conversation.
    Average of 1.6 calls per agent.
    Collected for a study aimed at identifying customer oriented behaviors that could favor satisfactory interaction with customers (Rafaeli et al. 2007).
    © 2011 interAnalytics
    14
  • 15. Identify Customer Oriented Behaviors
    Based on the work of Rafaeli et al. 2006.
    Customer Oriented Behaviors
    anticipating customers requests 22,45%
    educating the customer 16,91%
    offering emotional support 21,57%
    offering explanations / justifications 28,57%
    personalization of information 10,50%
    © 2011 interAnalytics
    15
  • 16. Significant correlation with argumentative labels
    © 2011 interAnalytics
    16
  • 17. Identify Root Cause of Problems
    Cooperativeness score
    a measure obtained by averaging the score obtained by mapping argumentative labels of each turn in the conversation into a [-5 +5] scale.
    Sentiment Analysis module.
    © 2011 interAnalytics
    17
  • 18. Top 20 Controversial Topics with average cooperativeness scores and sentiment
    © 2011 interAnalytics
    18
  • 19. Cooperativeness of speakers on top discussed topics
    © 2011 interAnalytics
    19
  • 20. Identify problematic customers
    © 2011 interAnalytics
    20
  • 21. Select a specific customer
    © 2011 interAnalytics
    21
  • 22. Visualize a selected call
    © 2011 interAnalytics
    22
  • 23. Conclusions
    © 2011 interAnalytics
    23
  • 24. Conclusions
    New Generation Call Center Analytics requires Interaction Mining
    Call Center Qualitative metrics and KPIs can be only implemented with a full understanding of the customer interaction dynamics
    Argumentation is pervasive in conversations.
    In order to recognize argumentative acts, advanced Natural Language Understanding is necessary.
    Future work:
    Scalability: need to process millions of call per day!
    Multi-language: call centers all over the world.
    © 2011 interAnalytics
    24
  • 25. The Team
    © 2011 interAnalytics
    25
  • 26. …find us at
    www.interanalytics.ch