{"id":"https://openalex.org/W4206789917","doi":"https://doi.org/10.1109/bigdata52589.2021.9671710","title":"Predicting Loss Risks for B2B Tendering Processes","display_name":"Predicting Loss Risks for B2B Tendering Processes","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W4206789917","doi":"https://doi.org/10.1109/bigdata52589.2021.9671710"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671710","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5009239751","display_name":"Eelaaf Zahid","orcid":null},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Eelaaf Zahid","raw_affiliation_strings":["IBM Research - Almaden, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research - Almaden, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015741315","display_name":"Yuya Jeremy Ong","orcid":"https://orcid.org/0000-0002-8591-4455"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuya Jeremy Ong","raw_affiliation_strings":["IBM Research - Almaden, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research - Almaden, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074701480","display_name":"Aly Megahed","orcid":"https://orcid.org/0000-0002-1819-2624"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aly Megahed","raw_affiliation_strings":["IBM Research - Almaden, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research - Almaden, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074644024","display_name":"Taiga Nakamura","orcid":null},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Taiga Nakamura","raw_affiliation_strings":["IBM Research - Almaden, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research - Almaden, San Jose, CA, USA","institution_ids":["https://openalex.org/I4210085935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5009239751"],"corresponding_institution_ids":["https://openalex.org/I4210085935"],"apc_list":null,"apc_paid":null,"fwci":0.2513,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52925781,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2076","last_page":"2083"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11182","display_name":"Auction Theory and Applications","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9962999820709229,"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/bidding","display_name":"Bidding","score":0.7339863777160645},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6610035300254822},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.625064492225647},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5771055221557617},{"id":"https://openalex.org/keywords/procurement","display_name":"Procurement","score":0.547707200050354},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.536532998085022},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5326297879219055},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.513496458530426},{"id":"https://openalex.org/keywords/competition","display_name":"Competition (biology)","score":0.4752861261367798},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4640604257583618},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.45634517073631287},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3771445155143738},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.15912437438964844},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.14437144994735718},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13616019487380981},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10911157727241516}],"concepts":[{"id":"https://openalex.org/C9233905","wikidata":"https://www.wikidata.org/wiki/Q3276328","display_name":"Bidding","level":2,"score":0.7339863777160645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610035300254822},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.625064492225647},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5771055221557617},{"id":"https://openalex.org/C201650216","wikidata":"https://www.wikidata.org/wiki/Q829492","display_name":"Procurement","level":2,"score":0.547707200050354},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.536532998085022},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5326297879219055},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.513496458530426},{"id":"https://openalex.org/C91306197","wikidata":"https://www.wikidata.org/wiki/Q45767","display_name":"Competition (biology)","level":2,"score":0.4752861261367798},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4640604257583618},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.45634517073631287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3771445155143738},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.15912437438964844},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.14437144994735718},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13616019487380981},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10911157727241516},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671710","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1494846538","https://openalex.org/W1533877311","https://openalex.org/W1916889544","https://openalex.org/W1964492736","https://openalex.org/W2017990205","https://openalex.org/W2097998348","https://openalex.org/W2148143831","https://openalex.org/W2295897189","https://openalex.org/W2747650751","https://openalex.org/W2768348081","https://openalex.org/W2900373954","https://openalex.org/W2932185161","https://openalex.org/W2964022491","https://openalex.org/W2970961064","https://openalex.org/W3002671651","https://openalex.org/W3005097661","https://openalex.org/W3006728089","https://openalex.org/W3010116109","https://openalex.org/W3035274228","https://openalex.org/W3100151823","https://openalex.org/W4286386845","https://openalex.org/W6674385629","https://openalex.org/W6745609711","https://openalex.org/W6750729320","https://openalex.org/W6756036446","https://openalex.org/W6793259180"],"related_works":["https://openalex.org/W2355561715","https://openalex.org/W2355326491","https://openalex.org/W2389286292","https://openalex.org/W2360751371","https://openalex.org/W2387920521","https://openalex.org/W2353137333","https://openalex.org/W2369720308","https://openalex.org/W2886188438","https://openalex.org/W2368635184","https://openalex.org/W3007108012"],"abstract_inverted_index":{"Sellers":[0],"and":[1,39,58,125,176],"executives":[2],"who":[3],"maintain":[4],"a":[5,61,67,83,90,95,114,171],"bidding":[6],"pipeline":[7],"of":[8,25,27,55,66,134,144,174,181],"sales":[9],"engagements":[10],"with":[11,103,167],"multiple":[12],"clients":[13],"for":[14,43,63],"many":[15,32],"opportunities":[16],"significantly":[17],"benefit":[18],"from":[19],"data-driven":[20],"insight":[21,79],"into":[22],"the":[23,53,64,104,142,145,152,161,168],"health":[24],"each":[26],"their":[28],"bids.":[29],"There":[30],"are":[31,51],"predictive":[33],"models":[34,50],"that":[35,136],"offer":[36,77,131],"likelihood":[37,65],"insights":[38],"win":[40,48,68,101],"prediction":[41,49,62],"modeling":[42],"these":[44,47],"opportunities.":[45],"Currently,":[46],"in":[52],"form":[54],"binary":[56,72],"classification":[57,97],"only":[59],"make":[60],"or":[69],"loss.":[70,91],"The":[71],"formulation":[73],"is":[74,116],"unable":[75],"to":[76,81,99,112,127],"any":[78],"as":[80,89,111],"why":[82,113],"particular":[84],"deal":[85],"might":[86,138],"be":[87,139],"predicted":[88],"This":[92],"paper":[93,156],"offers":[94,158],"multi-class":[96,153],"model":[98,162],"predict":[100],"probability,":[102],"three":[105],"loss":[106,115],"classes":[107,130],"offering":[108,148],"specific":[109],"reasons":[110],"predicted,":[117],"including":[118],"no":[119],"bid,":[120],"customer":[121],"did":[122],"not":[123],"pursue,":[124],"lost":[126],"competition.":[128],"These":[129],"an":[132,177],"indicator":[133],"how":[135],"opportunity":[137],"handled":[140],"given":[141],"nature":[143],"prediction.":[146],"Besides":[147],"baseline":[149],"results":[150,159,169],"on":[151,160],"classification,":[154],"this":[155],"also":[157],"after":[163],"class":[164],"imbalance":[165],"handling,":[166],"achieving":[170],"high":[172],"accuracy":[173],"85%":[175],"average":[178],"AUC":[179],"score":[180],"0.94.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}