{"id":3994,"date":"2017-10-31T10:51:45","date_gmt":"2017-10-31T10:51:45","guid":{"rendered":"http:\/\/cio.edu.umh.es\/?p=3994"},"modified":"2017-10-31T10:51:45","modified_gmt":"2017-10-31T10:51:45","slug":"seminario-del-profesor-fernando-bernstein","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2017\/10\/31\/seminario-del-profesor-fernando-bernstein\/","title":{"rendered":"Seminario del Profesor Fernando Bernstein"},"content":{"rendered":"<p>[:es]<span style=\"color: #000000\"><strong>Title:<\/strong>\u00a0A Dynamic Clustering Approach to Data-Driven Assortment Personalization<\/span><br \/>\n<span style=\"color: #000000\"><strong>Speaker:<\/strong>\u00a0Fernando Bernestein, The Fuqua School of Business, Duke University, USA<\/span><br \/>\n<span style=\"color: #000000\"><strong>Date:\u00a0<\/strong>17\/11\/2017, 12:00<\/span><br \/>\n<span style=\"color: #000000\"><strong>Abstract:<\/strong><\/span><br \/>\n<span style=\"color: #000000\">We consider an online retailer facing heterogeneous customers with initially unknown product preferences.\u00a0\u00a0Customers are characterized by a diverse set of demographic and transactional attributes.\u00a0 The retailer can personalize the customers&#8217; assortment offerings based on available profile information to maximize cumulative revenue.\u00a0 To that end,\u00a0 the retailer must estimate customer\u00a0 preferences\u00a0 by\u00a0 observing\u00a0 transaction\u00a0 data.\u00a0\u00a0 This,\u00a0 however,\u00a0 may\u00a0 require\u00a0 a\u00a0 considerable amount of data and time given the broad range of customer profiles and large number of products available.\u00a0 At the same time, the retailer can aggregate (pool) purchasing information among customers with similar product preferences to expedite the learning process.<\/span><br \/>\n<span style=\"color: #000000\">We propose a dynamic\u00a0 clustering policy that estimates customer preferences by adaptively adjusting customer segments (clusters of customers with similar preferences) as more transaction information becomes available.\u00a0 We test the proposed approach with a case study based on a dataset from a large Chilean retailer.\u00a0 The case study suggests that the benefits of the dynamic clustering policy can be substantial and result (on average) in more than 37%\u00a0 additional\u00a0 transactions compared to a data-intensive policy that treats customers independently and in more than 27% additional transactions compared to a linear-utility policy that assumes that product mean utilities are linear functions of available customer attributes.\u00a0 We support the insights derived from the numerical experiments by analytically characterizing settings in which pooling transaction information is beneficial for the retailer, in a simplified version of the problem.\u00a0 We also show that there are diminishing marginal returns to pooling information from an increasing number of customers.<\/span>[:]<\/p>","protected":false},"excerpt":{"rendered":"<p>[:es]Title:\u00a0A Dynamic Clustering Approach to Data-Driven Assortment Personalization<br \/>\nSpeaker:\u00a0Fernando Bernestein, The Fuqua School of Business, Duke University, USA<br \/>\nDate:\u00a017\/11\/2017, 12:00<br \/>\nAbstract:<br \/>\nWe consider an online retailer facing heterogeneous customers with initially unknown product preferences.\u00a0\u00a0Customers are characterized by a diverse set of demographic and transactional attributes.\u00a0 The retailer can personalize the customers&#8217; assortment offerings based on available profile information to [&#8230;]<\/p>","protected":false},"author":3477,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[4],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/3994"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/3477"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=3994"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/3994\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=3994"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=3994"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=3994"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}