{"id":"W2351547823","doi":"","title":"Application of Clustering Analysis in Family Customer Segmentation in Telecom Industry","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Market segmentation; Computer science; Cluster analysis; Segmentation; Telecommunications; Government (linguistics); Service (business); Enhanced Telecom Operations Map; Customer intelligence; Data mining; Customer advocacy; Marketing; Business; Service quality; Service provider; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001945546,0.0006214005,0.0006726406,0.005938733,0.001510874,0.001281113,0.0007536024,0.001233199,0.001383405],"category_scores_gemma":[0.006270089,0.0003029024,0.0008818818,0.005742787,0.0005218521,0.001047094,0.0007176303,0.0005850474,0.0003938588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001890284,"about_ca_system_score_gemma":0.00158152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02838246,"about_ca_topic_score_gemma":0.0197413,"domain_scores_codex":[0.9981874,0.000813188,0.000104922,0.0003250071,0.0003916717,0.0001777365],"domain_scores_gemma":[0.996953,0.001539913,0.0003185029,0.000207641,0.0008423586,0.0001385402],"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":[0.0008235663,0.000676172,0.1192843,0.0003734568,0.0006079595,0.0008220323,0.004602658,0.2550262,0.00847522,0.02086972,0.01013793,0.5783009],"study_design_scores_gemma":[0.00002501894,0.0001133613,0.04305161,0.0000481522,0.00009316397,0.0002790079,0.002022396,0.9368091,0.00395894,0.0107471,0.002765287,0.00008681352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6099119,0.001048112,0.3748679,0.001109685,0.0001004296,0.0003661391,0.000623554,0.001218569,0.01075367],"genre_scores_gemma":[0.9098052,0.0002400993,0.08853684,0.00005251596,0.00002602564,0.00005604452,0.0003303227,0.00005539633,0.0008975742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02838246,"threshold_uncertainty_score":0.05643451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01545242694526022,"score_gpt":0.242888798867585,"score_spread":0.2274363719223248,"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."}}