{"id":"W4321636122","doi":"10.1109/icast55766.2022.10039530","title":"Feedback Based Telecom Churn Prediction Using Machine Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Competition (biology); Preference; Quarter (Canadian coin); Customer retention; Value (mathematics); Customer lifetime value; Telecommunications; Artificial intelligence; Machine learning; Marketing; Business; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001314602,0.0008112662,0.0009534018,0.002231389,0.0004242747,0.0009238427,0.0007925943,0.001143227,0.0009573067],"category_scores_gemma":[0.006706432,0.0003483596,0.0003900569,0.001541828,0.0003295182,0.001021995,0.0004247619,0.001117054,0.0004281127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216577,"about_ca_system_score_gemma":0.0005976366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0141141,"about_ca_topic_score_gemma":0.01087391,"domain_scores_codex":[0.9992884,0.0001805287,0.0000542031,0.0001513655,0.0002022246,0.0001231459],"domain_scores_gemma":[0.9952579,0.002980089,0.0005732835,0.0001600456,0.0008733896,0.0001553522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004214614,0.0003578857,0.0329702,0.00009822845,0.00007443636,0.0002119957,0.0001146249,0.830032,0.001588251,0.0007301386,0.002928212,0.1304726],"study_design_scores_gemma":[0.000002561573,0.00001212489,0.001080305,0.000003502797,0.000002440075,0.000008300762,0.000004212081,0.9983689,0.0002405762,0.0002184926,0.00005517729,0.000003544094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7236145,0.001528807,0.263599,0.001102166,0.0002179363,0.0001456417,0.0009731952,0.00404377,0.004775008],"genre_scores_gemma":[0.9833257,0.0001645217,0.01504482,0.00007656555,0.00005344347,0.00003380259,0.000460239,0.00002656796,0.0008143737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0141141,"threshold_uncertainty_score":0.02806389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352073227171434,"score_gpt":0.2163500492570249,"score_spread":0.1928293169853106,"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."}}