Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/14024
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dc.contributor.authorSrivastava, Rajiv
dc.contributor.authorPalshikar, Girish K.
dc.contributor.authorPawar, Sachin
dc.date.accessioned2015-07-08T05:00:40Z
dc.date.available2015-07-08T05:00:40Z
dc.date.issued2015
dc.identifier.citationSrivastava, R., Palshikar, G. K. & Pawar, S..(2015). Analytics for Improving Talent Acquisition Processes. 4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Indian Institute of Management, Ahmedabaden_US
dc.identifier.urihttp://hdl.handle.net/11718/14024
dc.description.abstractTalent Acquisition (TA) is an important function within HR, responsible for recruiting high quality people for given job positions through various sources under stringent deadlines and cost constraints. Given the importance of TA in the overall successful operations and growth of any organization, in this paper we identify specific “business questions” focused on analyzing various aspects of the TA processes, analyze past TA data using statistical analysis techniques and to discover novel patterns/insights and actionable knowledge which can help in improving the cost, efficiency and quality of recruitment. Our predictive analytic is mainly related to various duration and delays in TA, candidate selection or rejection, offer acceptance by selected candidates, root cause analysis for offer decline. We also use the data-mining technique of subgroup discovery to identify interesting patterns (e.g., candidate subgroups having unusually high decline ratios). We illustrate the approaches through a real-life data-set.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management, Ahmedabaden_US
dc.relation.ispartofseriesIC 15;016
dc.subjectTalent Acquisitionen
dc.subjectWorkforce Analyticsen
dc.subjectHuman Resources Managementen
dc.subjectMachine Learningen
dc.subjectText Miningen
dc.subjectData Miningen
dc.titleAnalytics for Improving Talent Acquisition Processesen_US
dc.typeArticleen_US
Appears in Collections:4th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence

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