{"id":"W4413339942","doi":"10.1007/978-3-031-99477-7_39","title":"Customer Segmentation via Clustering on Demographics and Purchases","year":2025,"lang":"en","type":"book-chapter","venue":"Learning and analytics in intelligent systems","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Passat (Canada)","funders":"","keywords":"Demographics; Cluster analysis; Segmentation; Business; Market segmentation; Computer science; Artificial intelligence; Marketing; Demography; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003799618,0.0003459966,0.0004196707,0.00126971,0.00020175,0.0004173154,0.00009049486,0.0002305951,0.0000642436],"category_scores_gemma":[0.00004284861,0.0003463314,0.00008068357,0.0001593527,0.00007380194,0.0001702416,0.0001194767,0.0005418431,0.00008052721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007891864,"about_ca_system_score_gemma":0.000009904717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002599079,"about_ca_topic_score_gemma":0.0001136203,"domain_scores_codex":[0.9984925,0.00001655773,0.0005305774,0.0004470157,0.0002977566,0.0002155688],"domain_scores_gemma":[0.9992704,0.0001222761,0.0003694359,0.0001289957,0.00008689277,0.00002196544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004039612,0.0002324161,0.07086457,0.01266317,0.001190302,0.000220417,0.001115514,0.0454746,0.0002485728,0.479245,0.005196664,0.3831448],"study_design_scores_gemma":[0.001080057,0.0001111825,0.0003558331,0.006912186,0.0006905168,0.00002325919,0.002642137,0.4022923,0.00002201238,0.003512366,0.5807137,0.001644402],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.007513555,0.005541125,0.02701727,0.0003782292,0.002671549,0.001636617,0.00001584934,0.0003785701,0.9548472],"genre_scores_gemma":[0.6804865,0.00414678,0.00006862841,0.000577151,0.001060329,0.0000281972,0.0003194075,0.0001166541,0.3131963],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.672973,"threshold_uncertainty_score":0.9998989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02504820645497956,"score_gpt":0.2597136968952481,"score_spread":0.2346654904402685,"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."}}