{"id":"https://openalex.org/W166194309","doi":null,"title":"A comparison of approaches for maximizing business payoff of prediction models","display_name":"A comparison of approaches for maximizing business payoff of prediction models","publication_year":1996,"publication_date":"1996-08-02","ids":{"openalex":"https://openalex.org/W166194309","mag":"166194309"},"language":"en","primary_location":{"id":"mag:166194309","doi":null,"is_oa":false,"landing_page_url":"https://www.aaai.org/Papers/KDD/1996/KDD96-032.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076123772","display_name":"Brij M. Masand","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4388891836","display_name":"GTE (United States)","ror":"https://ror.org/03ek3rf92","country_code":"US","type":"company","lineage":["https://openalex.org/I4388891836"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brij Masand","raw_affiliation_strings":["GTE Laboratories, Waltham, MA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GTE Laboratories, Waltham, MA#TAB#","institution_ids":["https://openalex.org/I4388891836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013158304","display_name":"Gregory Piatetsky-Shapiro","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4388891836","display_name":"GTE (United States)","ror":"https://ror.org/03ek3rf92","country_code":"US","type":"company","lineage":["https://openalex.org/I4388891836"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregory Piatetsky-Shapiro","raw_affiliation_strings":["GTE Laboratories, Waltham, MA#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GTE Laboratories, Waltham, MA#TAB#","institution_ids":["https://openalex.org/I4388891836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4388891836"],"apc_list":null,"apc_paid":null,"fwci":12.7059,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.97706422,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"195","last_page":"201"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/database-marketing","display_name":"database marketing","score":0.7879999876022339},{"id":"https://openalex.org/keywords/customer-behavior-prediction","display_name":"customer behavior prediction","score":0.8429999947547913},{"id":"https://openalex.org/keywords/customer-value","display_name":"customer value","score":0.921999990940094},{"id":"https://openalex.org/keywords/model-selection","display_name":"model selection","score":0.45899999141693115},{"id":"https://openalex.org/keywords/post-processing","display_name":"post-processing","score":0.5},{"id":"https://openalex.org/keywords/customer-segmentation","display_name":"customer segmentation","score":0.328000009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7478865385055542},{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.6326867341995239},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49720337986946106},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4768306314945221},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4618455767631531},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.4182053804397583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4165131747722626},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38674086332321167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10868602991104126},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:166194309","doi":null,"is_oa":false,"landing_page_url":"https://www.aaai.org/Papers/KDD/1996/KDD96-032.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/9","score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W23418094","https://openalex.org/W32535764","https://openalex.org/W1601529450","https://openalex.org/W1610836425","https://openalex.org/W2766736793"],"related_works":["https://openalex.org/W2623677285","https://openalex.org/W1533166359","https://openalex.org/W2373242643","https://openalex.org/W7097009037","https://openalex.org/W2917868367","https://openalex.org/W2183969227","https://openalex.org/W3139014432","https://openalex.org/W4402945951","https://openalex.org/W7166529132","https://openalex.org/W2995423468"],"abstract_inverted_index":{"In":[0],"many":[1],"database":[2],"marketing":[3],"applications":[4],"the":[5,10,29,40,64,91,98,113],"goal":[6],"is":[7,21,54],"to":[8,22,48,62,82],"predict":[9],"customer":[11,65],"behavior":[12],"based":[13,119],"on":[14,28,39,120],"their":[15],"previous":[16],"actions.":[17],"A":[18],"usual":[19],"approach":[20],"develop":[23],"models":[24,38,78],"which":[25],"maximize":[26,49],"accuracy":[27,52],"training":[30,114],"and":[31,34,58,79,116],"test":[32],"sets":[33],"then":[35],"apply":[36],"these":[37],"unseen":[41],"data.":[42],"We":[43,69],"show":[44],"that":[45,101,106],"in":[46],"order":[47],"business":[50],"payoffs,":[51],"optimization":[53],"insufficient":[55],"by":[56],"itself,":[57],"explore":[59],"different":[60,77,87],"strategies":[61],"take":[63],"value":[66,110,121],"into":[67],"account.":[68],"propose":[70],"a":[71,84],"framework":[72],"for":[73,89],"comparing":[74],"payoffs":[75],"of":[76,86,95,109],"use":[80,108],"it":[81],"compare":[83],"number":[85],"approaches":[88],"selecting":[90],"most":[92],"valuable":[93],"subset":[94],"customers.":[96],"For":[97],"two":[99],"datasets":[100],"we":[102,104],"consider,":[103],"find":[105],"explicit":[107],"information":[111],"during":[112],"process":[115],"stratified":[117],"modelling":[118],"both":[122],"perform":[123],"better":[124],"than":[125],"post":[126],"processing":[127],"strategies.":[128]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2016-06-24T00:00:00"}