{"id":"W2808600871","doi":"10.17722/ijrbt.v10i3.497","title":"Segmentation of business saving customer to improve average balance based on structural equation modeling (SEM) and recency, frequency, monetary (RFM): Case study Bank XYZ in Indonesia","year":2018,"lang":"en","type":"article","venue":"International Journal of Research in Business and Technology","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Balance (ability); Business; Segmentation; Computer science; Econometrics; Operations management; Economics; Artificial intelligence; Psychology","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":[0.00111621,0.0001412945,0.0002477353,0.003960221,0.00009113076,0.0001320508,0.0002550893,0.00009830935,0.00002178491],"category_scores_gemma":[0.0003115913,0.000127998,0.00001649008,0.002098985,0.0001319776,0.0008954296,0.0001590683,0.0003187079,0.000002605842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001455799,"about_ca_system_score_gemma":0.00007505107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002636892,"about_ca_topic_score_gemma":0.0009953699,"domain_scores_codex":[0.9982148,0.00004892362,0.0005982527,0.0002663005,0.0006452,0.0002264786],"domain_scores_gemma":[0.9972133,0.00009174298,0.0002826964,0.0001236232,0.002269542,0.00001909716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006679296,0.0003134559,0.9037401,0.0001576747,0.00003990575,0.001238396,0.0003872531,0.008260632,0.01093733,0.000792117,0.00001087954,0.07345434],"study_design_scores_gemma":[0.007546603,0.0003829612,0.4643922,0.001077569,0.00003267515,0.000428249,0.005617541,0.5125014,0.0006690238,0.006911348,0.00002244098,0.0004180146],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946709,0.00006488865,0.002031705,0.002352409,0.0003907851,0.0003871002,0.000003052034,0.00001196355,0.00008723106],"genre_scores_gemma":[0.9988523,0.00007467624,0.0006577237,0.00009186634,0.000279336,0.00001829606,0.000007948292,0.00001430071,0.000003511814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5042407,"threshold_uncertainty_score":0.5219606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04401101826759843,"score_gpt":0.3398461867554714,"score_spread":0.2958351684878729,"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."}}