{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005599588,0.0004233923,0.0003438726,0.003188651,0.0006438906,0.001126355,0.0006250526,0.0003950655,0.00175997],"category_scores_gemma":[0.002445108,0.0001248562,0.0004557154,0.00340636,0.0002888878,0.0007948254,0.0007434665,0.000850477,0.0007512616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177942,"about_ca_system_score_gemma":0.0005586828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05108016,"about_ca_topic_score_gemma":0.04387864,"domain_scores_codex":[0.9995469,0.00008159331,0.00003829205,0.00007176295,0.0001589007,0.0001025104],"domain_scores_gemma":[0.9981616,0.0007729121,0.0002618888,0.00014735,0.0005120268,0.0001442155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001343259,0.001234967,0.515465,0.0005043433,0.0002378181,0.002388745,0.004818868,0.1420384,0.005726358,0.006456216,0.03547053,0.2843156],"study_design_scores_gemma":[0.00001242429,0.0002131554,0.2421931,0.00006573138,0.00005145105,0.0003980668,0.002534156,0.7418943,0.002989607,0.002049883,0.007547648,0.00005049137],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9858636,0.0004540039,0.003045631,0.0005329087,0.00003414605,0.00004500747,0.003400391,0.0008161287,0.005808105],"genre_scores_gemma":[0.9911419,0.000254174,0.002357775,0.00005291234,0.00001967405,0.00002662808,0.00411409,0.00004468401,0.001987994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05108016,"threshold_uncertainty_score":0.1015657,"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."}}