{"id":5372,"date":"2015-05-12T21:54:21","date_gmt":"2015-05-12T21:54:21","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=5372"},"modified":"2020-09-07T09:56:23","modified_gmt":"2020-09-07T13:56:23","slug":"uplift-modeling-predictive-analytics-cant-optimize-marketing-decisions-without-it","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/uplift-modeling-predictive-analytics-cant-optimize-marketing-decisions-without-it\/5372\/","title":{"rendered":"White Paper &#8211; Immediate Access"},"content":{"rendered":"Thank you for your interest in the white paper, &#8220;Uplift Modeling: Predictive Analytics Can\u2019t Optimize Marketing Decisions Without It,&#8221; written by PAW Founder Eric Siegel, Ph.D., and sponsored by Pitney Bowes Business Insight. &nbsp; Access the white paper below: Download this PDF PDF Download is restricted to site members.Log in on the right OR subscribe for free WHITE PAPER DESCRIPTION: To drive business decisions for maximal impact, analytical models must predict the marketing influence of each decision on customer buying behavior. Uplift modeling provides the means to do this, improving upon conventional response and churn models that introduce <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/uplift-modeling-predictive-analytics-cant-optimize-marketing-decisions-without-it\/5372\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Thank you for your interest in the white paper, &#8220;Uplift Modeling: Predictive Analytics Can\u2019t Optimize Marketing Decisions Without It,&#8221; written by PAW Conference Chair Eric Siegel, Ph.D., and sponsored by Pitney Bowes Business Insight.<\/p>\n<p><em>To drive business decisions for maximal impact, analytical models must predict the marketing influence of each decision on customer buying behavior. Uplift modeling provides the means to do this, improving upon conventional response and churn models that introduce significant risk by optimizing for the wrong thing. This shift is fundamental to empirically driven decision making. This convention-altering white paper reveals the why and how, and delivers case study results that multiply the ROI of predictive analytics by factors up to 11.<\/em><\/p>\n<p>For upcoming presentations on uplift modeling at Predictive Analytics World, and other additional related resources, <a href=\"http:\/\/www.predictiveanalyticsworld.com\/patimes\/personalization-is-back-how-to-drive-influence-by-crunching-numbers\/\" target=\"_blank\" rel=\"noopener noreferrer\">see this article<\/a>.<\/p>\n<p>For a broader introduction to uplift modeling, <a href=\"http:\/\/www.predictiveanalyticsworld.com\/book\/\" target=\"_blank\" rel=\"noopener noreferrer\">see also the final chapter of Eric Siegel&#8217;s book, Predictive Analytics<\/a><\/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":[3,426],"tags":[],"class_list":["post-5372","post","type-post","status-publish","format-standard","hentry","category-leading-stories","category-whitepaper"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/5372","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=5372"}],"version-history":[{"count":28,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/5372\/revisions"}],"predecessor-version":[{"id":11678,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/5372\/revisions\/11678"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=5372"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=5372"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=5372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}