{"id":"W2011055298","doi":"10.5539/ass.v10n13p169","title":"Churn Analytics on Indian Prepaid Mobile Services","year":2014,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Analytics; Conceptual model; Mobile telephony; Marketing; Computer science; Telecommunications; Data science","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":[0.0004450944,0.0000843918,0.00008570677,0.0001832916,0.0005895533,0.0004242436,0.0003295045,0.00003356147,0.0001085333],"category_scores_gemma":[0.00002121933,0.00007821678,0.00003849429,0.0009884948,0.0001601717,0.0009464541,0.00007793511,0.00006518363,0.0005031912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004697631,"about_ca_system_score_gemma":0.00001913767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001444791,"about_ca_topic_score_gemma":0.000105869,"domain_scores_codex":[0.9989839,0.000006083772,0.0001136452,0.000233714,0.0004100112,0.0002526547],"domain_scores_gemma":[0.9996792,0.00000848499,0.0001096685,0.0001140576,0.00007137263,0.00001720812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002942803,0.0002199638,0.03236347,0.0001877409,0.00001561483,0.000006118171,0.006177627,0.00002312741,0.003518658,0.1104162,0.002528176,0.8445138],"study_design_scores_gemma":[0.001079203,0.00008877958,0.7561927,0.00007737247,0.00006732388,0.000001516902,0.0145786,0.004844881,0.0008995882,0.01193856,0.2093557,0.0008757916],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3979018,0.00000196802,0.00004641523,0.0005282916,0.0002830489,0.0001313515,7.558472e-7,0.00008627726,0.60102],"genre_scores_gemma":[0.9961679,5.644041e-7,0.00002963696,0.002576796,0.0009920297,0.000008347544,0.000006649469,0.000006929386,0.0002111668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8436381,"threshold_uncertainty_score":0.6467673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009773615803502642,"score_gpt":0.2516488590767018,"score_spread":0.2418752432731992,"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."}}