{"id":"https://openalex.org/W2755829231","doi":"https://doi.org/10.1109/cit.2017.58","title":"Towards Accurate Predictions of Customer Purchasing Patterns","display_name":"Towards Accurate Predictions of Customer Purchasing Patterns","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2755829231","doi":"https://doi.org/10.1109/cit.2017.58","mag":"2755829231"},"language":"en","primary_location":{"id":"doi:10.1109/cit.2017.58","doi":"https://doi.org/10.1109/cit.2017.58","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cit.2017.58","pdf_url":null,"source":{"id":"https://openalex.org/S7407085889","display_name":"IEEE International Conference on Computer and Information Technology (CIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2017 IEEE International Conference on Computer and Information Technology (CIT)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5044550251","display_name":"Rafael Rodr\u00edguez Valero","orcid":"https://orcid.org/0000-0002-2569-7786","observed_orcids":["https://orcid.org/0000-0002-2569-7786"]},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Rafael Valero-Fernandez","raw_affiliation_strings":["Keele University, Keele, Staffordshire, GB"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keele University, Keele, Staffordshire, GB","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103102892","display_name":"David Collins","orcid":"https://orcid.org/0000-0002-8018-0245","observed_orcids":["https://orcid.org/0000-0002-8018-0245","https://orcid.org/0000-0002-5517-6949"]},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"David J. Collins","raw_affiliation_strings":["School of Computing and Mathematics, Keele University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematics, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"middle","author":{"id":null,"display_name":"K.P. Lam","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"K.P. Lam","raw_affiliation_strings":["School of Computing and Mathematics, Keele University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Mathematics, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045138078","display_name":"Colin Rigby","orcid":"https://orcid.org/0000-0002-8740-9663","observed_orcids":["https://orcid.org/0000-0002-8740-9663"]},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Colin Rigby","raw_affiliation_strings":["Keele Management School, Keele University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keele Management School, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"last","author":{"id":null,"display_name":"James Bailey","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"James Bailey","raw_affiliation_strings":["Keele Management School, Keele University, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keele Management School, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I56007636"],"apc_list":null,"apc_paid":null,"fwci":5.5894,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.9532114,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"157","last_page":"161"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9599999785423279,"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":0.9599999785423279,"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":0.027000000700354576,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.002300000051036477,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/customer-purchasing-patterns","display_name":"customer purchasing patterns","score":0.9139999747276306},{"id":"https://openalex.org/keywords/online-retail","display_name":"online retail","score":0.7160000205039978},{"id":"https://openalex.org/keywords/customer-classification","display_name":"customer classification","score":0.8199999928474426},{"id":"https://openalex.org/keywords/transaction-data","display_name":"transaction data","score":0.7250000238418579},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.5830000042915344},{"id":"https://openalex.org/keywords/quadratic-discriminant-analysis","display_name":"quadratic discriminant analysis","score":0.9150000214576721},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.968999981880188},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.8529999852180481},{"id":"https://openalex.org/keywords/decision-tree","display_name":"decision tree","score":0.6520000100135803},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.7599999904632568},{"id":"https://openalex.org/keywords/gaussian-processes","display_name":"Gaussian processes","score":0.6060000061988831},{"id":"https://openalex.org/keywords/lasso","display_name":"LASSO","score":0.5879999995231628},{"id":"https://openalex.org/keywords/regression-trees","display_name":"regression trees","score":0.7509999871253967}],"concepts":[{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.6756036877632141},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6635363101959229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.641277551651001},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5758398175239563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5665799379348755},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5447127223014832},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5370482802391052},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.5157235264778137},{"id":"https://openalex.org/C2778813691","wikidata":"https://www.wikidata.org/wiki/Q1369832","display_name":"Purchasing","level":2,"score":0.507297933101654},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5014827251434326},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4968126118183136},{"id":"https://openalex.org/C16023879","wikidata":"https://www.wikidata.org/wiki/Q1023599","display_name":"CHAID","level":3,"score":0.46942761540412903},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.46522897481918335},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.46212148666381836},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4401087462902069},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.43869057297706604},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.3267518877983093},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.17047294974327087},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.09125107526779175},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cit.2017.58","doi":"https://doi.org/10.1109/cit.2017.58","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cit.2017.58","pdf_url":null,"source":{"id":"https://openalex.org/S7407085889","display_name":"IEEE International Conference on Computer and Information Technology (CIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2017 IEEE International Conference on Computer and Information Technology (CIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5299999713897705}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1519051771","https://openalex.org/W2018977290","https://openalex.org/W2018979850","https://openalex.org/W2029387089","https://openalex.org/W2030714694","https://openalex.org/W2065080511","https://openalex.org/W2075150581","https://openalex.org/W2084484787","https://openalex.org/W2085988980","https://openalex.org/W2101234009","https://openalex.org/W2119231221","https://openalex.org/W2147953360","https://openalex.org/W2341282804"],"related_works":["https://openalex.org/W6941486662","https://openalex.org/W2752804894","https://openalex.org/W4245486366","https://openalex.org/W4285304539","https://openalex.org/W4230500374","https://openalex.org/W4400994076","https://openalex.org/W26221895","https://openalex.org/W4224220805","https://openalex.org/W4250861347","https://openalex.org/W2623356950"],"abstract_inverted_index":{"A":[0],"range":[1],"of":[2,11,22],"algorithms":[3],"was":[4,53,88],"used":[5,90],"to":[6,91],"classify":[7,92],"online":[8],"retail":[9],"customers":[10,52],"a":[12],"UK":[13,99],"company":[14],"using":[15,27],"historical":[16],"transaction":[17],"data.":[18],"The":[19,58],"predictive":[20],"capabilities":[21],"the":[23,98],"classifiers":[24],"were":[25,39],"assessed":[26],"linear":[28],"regression,":[29],"Lasso":[30],"and":[31,43,81,102],"regression":[32],"trees.":[33],"Unlike":[34],"most":[35],"related":[36],"studies,":[37],"classifications":[38],"based":[40],"upon":[41],"specific":[42],"marketing":[44],"focused":[45],"customer":[46],"behaviours.":[47],"Prediction":[48,107],"accuracy":[49,108],"on":[50,94],"untrained":[51],"generally":[54],"better":[55,110],"than":[56,111],"80%.":[57],"models":[59],"implemented":[60],"(and":[61],"compared)":[62],"for":[63],"classification":[64],"were:":[65],"Logistic":[66],"Regression,":[67],"Quadratic":[68],"Discriminant":[69],"Analysis,":[70],"Linear":[71],"SVM,":[72,74],"RBF":[73],"Gaussian":[75],"Process,":[76],"Decision":[77],"Tree,":[78],"Random":[79],"Forest":[80],"Multi-layer":[82],"Perceptron":[83],"(Neural":[84],"Network).":[85],"Postcode":[86],"data":[87,105],"then":[89],"solely":[93],"demographics":[95],"derived":[96],"from":[97],"Land":[100],"Registry":[101],"similar":[103],"public":[104],"sources.":[106],"remained":[109],"60%.":[112]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2026-10-13T10:08:12.033832","created_date":"2017-09-25T00:00:00"}