{"id":"W7125811025","doi":"10.1109/icit64950.2025.11049096","title":"Customer Segmentation for Targeted Campaigns Using RFM Analysis and K-Means Clustering","year":2025,"lang":"","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Market segmentation; Cluster analysis; Component (thermodynamics); Segmentation; Loyalty; Customer lifetime value; Loyalty business model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001624183,0.001155744,0.001073732,0.004276744,0.001041907,0.00199791,0.001351848,0.001493376,0.003267926],"category_scores_gemma":[0.004171815,0.0003638576,0.002402513,0.002536603,0.0004111242,0.0009678478,0.000894308,0.00132282,0.001925375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935687,"about_ca_system_score_gemma":0.001793112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03338525,"about_ca_topic_score_gemma":0.03305035,"domain_scores_codex":[0.9989828,0.0002525593,0.00006490704,0.0003021777,0.0001916583,0.0002058352],"domain_scores_gemma":[0.9988681,0.000470862,0.0001524811,0.0001133757,0.0003377619,0.00005739113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009056258,0.001332214,0.1213579,0.0003402556,0.0005131037,0.0003124943,0.001652677,0.3502778,0.005168845,0.01116426,0.01936279,0.487612],"study_design_scores_gemma":[0.0000189019,0.00007121201,0.01835792,0.00004176134,0.00004171548,0.00005040598,0.0003763414,0.9740949,0.001108944,0.003384426,0.002413536,0.00003993181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5996993,0.0007509573,0.3759113,0.001229402,0.0001798285,0.001224205,0.002945834,0.003611312,0.01444799],"genre_scores_gemma":[0.839099,0.0001681055,0.1523182,0.0001742689,0.00007476506,0.0005025896,0.003548197,0.000172208,0.00394265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03338525,"threshold_uncertainty_score":0.06638187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187438299690996,"score_gpt":0.2834426081295363,"score_spread":0.2615682251326263,"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."}}