{"id":3636,"date":"2014-06-23T18:57:14","date_gmt":"2014-06-23T18:57:14","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=3636"},"modified":"2014-07-01T13:02:53","modified_gmt":"2014-07-01T13:02:53","slug":"webinar-direct-steps-stop-expensive-churn-employee-churn","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/webinar-direct-steps-stop-expensive-churn-employee-churn\/3636\/","title":{"rendered":"Webinar: Towards Solving Employee Attrition: Cost Modeling"},"content":{"rendered":"Presented by: Pasha Roberts, Chief Scientist, Talent Analytics, Corp. Watch Webinar. Pasha Roberts, Chief Scientist at Talent Analytics, Corp., discusses Talent Analytics&#8217; first step when using a predictive analytics approach for solving employee attrition challenges. Severe employee attrition can be a thing of the past. It need not be the &#8220;cost of doing business\u201d. Talent Analytics predictive analytics approach and technology platform shows this to be a solvable challenge. Predictive models are powerful, and can be tuned in many directions. To tune them accurately (for solving the most expensive problems) it is important for employers to quantitatively understand <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/webinar-direct-steps-stop-expensive-churn-employee-churn\/3636\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Presented by: Pasha Roberts, Chief Scientist, Talent Analytics, Corp. Watch Webinar. Pasha Roberts, Chief Scientist at Talent Analytics, Corp., discusses Talent Analytics&#8217; first step when using a predictive analytics approach for solving employee attrition challenges. Severe employee attrition can be a thing of the past. It need not be the &#8220;cost of doing business\u201d. Talent [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3],"tags":[],"class_list":["post-3636","post","type-post","status-publish","format-standard","hentry","category-leading-stories"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3636","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=3636"}],"version-history":[{"count":9,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3636\/revisions"}],"predecessor-version":[{"id":3652,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/3636\/revisions\/3652"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=3636"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=3636"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=3636"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}