{"id":7392,"date":"2016-02-25T09:00:25","date_gmt":"2016-02-25T14:00:25","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=7392"},"modified":"2016-02-24T20:30:17","modified_gmt":"2016-02-25T01:30:17","slug":"what-are-you-predicting-in-customer-retention","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/what-are-you-predicting-in-customer-retention\/7392\/","title":{"rendered":"What are you Predicting in Customer Retention?"},"content":{"rendered":"Customer Retention models are arguably the most valuable models that organizations can develop in improving overall customer profitability. The ability to target high value customers who are most likely to defect or become inactive allows organizations to prioritize limited resources within an overall retention strategy. With customers now scored in terms of their value and defection risk, testing can be now employed across different groups or segments of customers as we attempt to find whether or not save rates differ across these groups. In most cases, we know that save rates will vary, hence the need for net <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/what-are-you-predicting-in-customer-retention\/7392\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Customer Retention models are arguably the most valuable models that organizations can develop in improving overall customer profitability. The ability to target high value customers who are most likely to defect or become inactive allows organizations to prioritize limited resources within an overall retention strategy. With customers now scored in terms of their value and [&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":[486,494,8],"class_list":["post-7392","post","type-post","status-publish","format-standard","hentry","category-leading-stories","tag-boire-filler-group","tag-customer-retention","tag-predictive-analytics"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7392","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=7392"}],"version-history":[{"count":4,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7392\/revisions"}],"predecessor-version":[{"id":7396,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7392\/revisions\/7396"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=7392"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=7392"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=7392"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}