{"id":"https://openalex.org/W7225808657","doi":"https://doi.org/10.3389/frai.2026.1956496","title":"Cross-channel temporal fusion for retail customer lifetime value prediction: architecture, ablation, and causal validation of a gated BiGRU fusion network","display_name":"Cross-channel temporal fusion for retail customer lifetime value prediction: architecture, ablation, and causal validation of a gated BiGRU fusion network","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225808657","doi":"https://doi.org/10.3389/frai.2026.1956496"},"language":"en","primary_location":{"id":"doi:10.3389/frai.2026.1956496","doi":"https://doi.org/10.3389/frai.2026.1956496","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1956496","pdf_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1956496/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1956496/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024051573","display_name":"Sheikh Md Faysal","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I166088655","display_name":"Montclair State University","ror":"https://ror.org/01nxc2t48","country_code":"US","type":"education","lineage":["https://openalex.org/I166088655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheikh Md Faysal","raw_affiliation_strings":["Feliciano School of Business, Montclair State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Feliciano School of Business, Montclair State University","institution_ids":["https://openalex.org/I166088655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133974482","display_name":"Sharmin Sultana","orcid":"https://orcid.org/0009-0005-7213-4504","observed_orcids":["https://orcid.org/0009-0005-7213-4504"]},"institutions":[{"id":"https://openalex.org/I4210105364","display_name":"International American University","ror":"https://ror.org/02ad2pb06","country_code":"LC","type":"education","lineage":["https://openalex.org/I4210105364"]},{"id":"https://openalex.org/I4405267770","display_name":"International American University","ror":"https://ror.org/0493eqn45","country_code":"US","type":"education","lineage":["https://openalex.org/I4405267770"]}],"countries":["LC","US"],"is_corresponding":false,"raw_author_name":"Sharmin Sultana","raw_affiliation_strings":["School of Business, International American University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Business, International American University","institution_ids":["https://openalex.org/I4210105364","https://openalex.org/I4405267770"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117323875","display_name":"Urmi Haldar","orcid":"https://orcid.org/0009-0000-4040-7583","observed_orcids":["https://orcid.org/0009-0000-4040-7583"]},"institutions":[{"id":"https://openalex.org/I195939026","display_name":"Glasgow Caledonian University","ror":"https://ror.org/03dvm1235","country_code":"GB","type":"education","lineage":["https://openalex.org/I195939026"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Urmi Haldar","raw_affiliation_strings":["Glasgow School for Business and Society, Glasgow Caledonian University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Glasgow School for Business and Society, Glasgow Caledonian University","institution_ids":["https://openalex.org/I195939026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113367846","display_name":"Md Rashel Miah","orcid":"https://orcid.org/0009-0007-3874-053X","observed_orcids":["https://orcid.org/0009-0007-3874-053X"]},"institutions":[{"id":"https://openalex.org/I4404532904","display_name":"Westcliff University","ror":"https://ror.org/05xkpwy13","country_code":"US","type":"education","lineage":["https://openalex.org/I4404532904"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Rashel Miah","raw_affiliation_strings":["College of Business, Westcliff University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Business, Westcliff University","institution_ids":["https://openalex.org/I4404532904"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161435134","display_name":"Amena Akther","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210105364","display_name":"International American University","ror":"https://ror.org/02ad2pb06","country_code":"LC","type":"education","lineage":["https://openalex.org/I4210105364"]},{"id":"https://openalex.org/I4405267770","display_name":"International American University","ror":"https://ror.org/0493eqn45","country_code":"US","type":"education","lineage":["https://openalex.org/I4405267770"]}],"countries":["LC","US"],"is_corresponding":false,"raw_author_name":"Amena Akther","raw_affiliation_strings":["School of Business, International American University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Business, International American University","institution_ids":["https://openalex.org/I4210105364","https://openalex.org/I4405267770"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161467752","display_name":"Md Rayhan Hossain","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I19648265","display_name":"Wright State University","ror":"https://ror.org/04qk6pt94","country_code":"US","type":"education","lineage":["https://openalex.org/I19648265"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Rayhan Hossain","raw_affiliation_strings":["Raj Soin College of Business, Wright State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Raj Soin College of Business, Wright State University","institution_ids":["https://openalex.org/I19648265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161467752","display_name":"Md Rayhan Hossain","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I19648265","display_name":"Wright State University","ror":"https://ror.org/04qk6pt94","country_code":"US","type":"education","lineage":["https://openalex.org/I19648265"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Md Mamun Hossain","raw_affiliation_strings":["Raj Soin College of Business, Wright State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Raj Soin College of Business, Wright State University","institution_ids":["https://openalex.org/I19648265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119483388","display_name":"Sudipta Acharjee","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I19648265","display_name":"Wright State University","ror":"https://ror.org/04qk6pt94","country_code":"US","type":"education","lineage":["https://openalex.org/I19648265"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sudipta Acharjee","raw_affiliation_strings":["Raj Soin College of Business, Wright State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Raj Soin College of Business, Wright State University","institution_ids":["https://openalex.org/I19648265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5160272595","display_name":"Denis Kovalenko","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I8765205","display_name":"V. N. Karazin Kharkiv National University","ror":"https://ror.org/03ftejk10","country_code":"UA","type":"education","lineage":["https://openalex.org/I8765205"]}],"countries":["UA"],"is_corresponding":true,"raw_author_name":"Denis Kovalenko","raw_affiliation_strings":["Department of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University","institution_ids":["https://openalex.org/I8765205"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5160272595"],"corresponding_institution_ids":["https://openalex.org/I8765205"],"apc_list":{"value":1950,"currency":"CHF","value_usd":2353},"apc_paid":{"value":1950,"currency":"CHF","value_usd":2353},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":null,"first_page":null,"last_page":null},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.00019999999494757503,"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/T11918","display_name":"Forecasting Techniques and Applications","score":9.999999747378752e-05,"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":[],"concepts":[{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.7401000261306763},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6218000054359436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5548999905586243},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5196999907493591},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.4884999990463257},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.46050000190734863},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.4578000009059906},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4041000008583069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38850000500679016},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37380000948905945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C187008535","wikidata":"https://www.wikidata.org/wiki/Q204255","display_name":"Viral marketing","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.34769999980926514},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C81388566","wikidata":"https://www.wikidata.org/wiki/Q526668","display_name":"Sigmoid function","level":3,"score":0.30149999260902405},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2971000075340271},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.28209999203681946},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.27639999985694885},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3389/frai.2026.1956496","doi":"https://doi.org/10.3389/frai.2026.1956496","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1956496","pdf_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1956496/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3389/frai.2026.1956496","doi":"https://doi.org/10.3389/frai.2026.1956496","is_oa":true,"landing_page_url":"https://doi.org/10.3389/frai.2026.1956496","pdf_url":"https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1956496/pdf","source":{"id":"https://openalex.org/S4210197006","display_name":"Frontiers in Artificial Intelligence","issn_l":"2624-8212","issn":["2624-8212"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225808657.pdf","grobid_xml":"https://content.openalex.org/works/W7225808657.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W2036193982","https://openalex.org/W2052065931","https://openalex.org/W2104212223","https://openalex.org/W2107581815","https://openalex.org/W2113879048","https://openalex.org/W2150291618","https://openalex.org/W2157331557","https://openalex.org/W2607045400","https://openalex.org/W2899663614","https://openalex.org/W2948140294","https://openalex.org/W2996331899","https://openalex.org/W2999615587","https://openalex.org/W3113269512","https://openalex.org/W4294739073"],"related_works":[],"abstract_inverted_index":{"Introduction":[0],"Predicting":[1],"customer":[2],"lifetime":[3],"value":[4,180],"(CLV)":[5],"in":[6,200],"non-contractual":[7],"retail":[8,201],"settings":[9],"is":[10],"complicated":[11],"by":[12,122,163],"purchase":[13],"sequence":[14],"sparsity":[15],"and":[16,91,186,194],"heterogeneous":[17],"engagement":[18],"across":[19],"marketing":[20],"channels.":[21],"Methods":[22],"We":[23],"propose":[24],"the":[25,107,192],"Cross-Channel":[26],"Temporal":[27],"Fusion":[28],"Network":[29],"(CCTFN),":[30],"a":[31,35,48,53,60,71,149,171,188],"dual-branch":[32],"architecture":[33],"combining":[34],"convolutional-recurrent":[36],"temporal":[37,176],"encoder":[38],"(Conv1d":[39],"\u2192":[40,43],"2-layer":[41],"BiGRU":[42],"4-head":[44],"attention":[45],"pooling)":[46],"with":[47,78],"residual":[49],"static":[50,96,183],"MLP":[51],"via":[52],"per-household":[54],"learned":[55],"sigmoid":[56],"gate,":[57],"evaluated":[58],"through":[59],"four-way":[61],"ablation":[62],"on":[63],"2,581,266":[64],"grocery":[65],"transactions":[66],"from":[67],"2,500":[68],"households":[69],"using":[70],"strictly":[72],"disjoint":[73],"547-day":[74],"calibration/164-day":[75],"holdout":[76],"design":[77],"GroupKFold":[79],"cross-validation":[80],"(K":[81],"=":[82,100,104,112,116,124,132],"5);":[83],"causal":[84,150,195],"validation":[85],"employed":[86],"propensity":[87],"score":[88],"matching":[89],"(PSM)":[90],"difference-in-differences":[92],"(DiD).":[93],"Results":[94],"The":[95],"branch":[97],"alone":[98],"(RMSE":[99],"485.60,":[101],"Spearman":[102],"\u03c1":[103],"0.8060)":[105],"outperforms":[106,120],"full":[108],"gated":[109,118],"model":[110],"(\u03c1":[111],"0.7998)":[113],"at":[114,165],"N":[115],"2,500;":[117],"fusion":[119],"concatenation":[121],"\u0394\u03c1":[123],"0.0042":[125],"(95%":[126],"CI":[127,159],"[0.0028,":[128],"0.0056];":[129],"paired":[130],"t":[131],"8.3,":[133],"p":[134],"<":[135],"0.01);":[136],"channel-exposure":[137,198],"variables":[138],"account":[139],"for":[140],"only":[141],"1.50%":[142],"of":[143,152,174,197],"permutation":[144],"importance":[145],"yet":[146],"PSM":[147],"yields":[148],"ATT":[151],"$193.55":[153],"per":[154],"household":[155],"(22.8%":[156],"uplift,":[157],"95%":[158],"[$112.51,":[160],"$276.70]),":[161],"corroborated":[162],"DiD":[164],"$100.30.":[166],"Discussion":[167],"These":[168],"results":[169],"provide":[170],"transparent":[172],"diagnostic":[173],"where":[175],"deep":[177],"learning":[178],"adds":[179],"relative":[181],"to":[182],"feature":[184],"engineering,":[185],"reveal":[187],"systematic":[189],"asymmetry":[190],"between":[191],"predictive":[193],"roles":[196],"features":[199],"CLV":[202],"modeling.":[203]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
