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Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
Interaction Mining: the new frontier of Call Center Analytics
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Interaction Mining: the new frontier of Call Center Analytics

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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.

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

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