{"id":"W2162619115","doi":"10.5267/j.msl.2015.2.004","title":"Market basket analysis in insurance industry","year":2015,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Insurance industry; Market analysis; Actuarial science; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001432484,0.0001362478,0.0001522386,0.001976647,0.0001165909,0.0004754477,0.0005384316,0.00003592826,0.0001905523],"category_scores_gemma":[0.00003596988,0.0001333743,0.00005280837,0.007100341,0.0001792417,0.00208423,0.0002577071,0.0001581881,0.000190564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001467169,"about_ca_system_score_gemma":0.000005883425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003487492,"about_ca_topic_score_gemma":0.0001046956,"domain_scores_codex":[0.9982002,0.00001130571,0.0002336305,0.0004321395,0.0007235563,0.000399163],"domain_scores_gemma":[0.9995168,0.000008377557,0.0001162996,0.0003011605,0.00002879038,0.00002851318],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001781059,0.00004678117,0.9705667,0.00002733659,0.00003324167,0.00004403012,0.00007544189,0.002552645,0.0001975534,0.001355865,0.02173921,0.003343324],"study_design_scores_gemma":[0.0005783543,0.00000212925,0.977535,0.00001400407,0.00007090486,2.676852e-7,0.0007697347,0.008966222,0.00001622828,0.0002065851,0.01162883,0.0002117265],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.900502,0.00000623021,0.0007423955,0.005652271,0.0003505748,0.0002083565,7.140635e-7,0.00006961244,0.09246781],"genre_scores_gemma":[0.9829035,0.000001529856,0.000306889,0.01601098,0.0001444643,0.00002127004,0.000007465653,0.000007650277,0.000596287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09187152,"threshold_uncertainty_score":0.5438847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0221726137536907,"score_gpt":0.2432677884839359,"score_spread":0.2210951747302452,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}