{"id":"W4403577487","doi":"10.1145/3627673.3679712","title":"OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Value (mathematics); Computer science; Customer value; Distribution (mathematics); Machine learning; Mathematics; Economics","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":[0.001814822,0.001510306,0.00147677,0.001782426,0.000595964,0.001001683,0.002638988,0.001501116,0.002036156],"category_scores_gemma":[0.005577814,0.0006225327,0.0009045106,0.0017489,0.0007256212,0.002614514,0.001410122,0.002308659,0.001066466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001578413,"about_ca_system_score_gemma":0.001710313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118623,"about_ca_topic_score_gemma":0.01228615,"domain_scores_codex":[0.9989919,0.0002561405,0.00005195835,0.0003798999,0.0001913485,0.0001287862],"domain_scores_gemma":[0.9980714,0.001087898,0.0001537337,0.0002125855,0.0003520005,0.0001225502],"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":[0.000731864,0.0004586987,0.02911001,0.0001809494,0.0001404343,0.0002113481,0.0001495503,0.6371345,0.002488101,0.006041675,0.0292126,0.2941402],"study_design_scores_gemma":[0.00001422709,0.00001749429,0.0004723514,0.000004368975,0.000003942116,0.00002199839,0.0000109456,0.995796,0.0004374735,0.00261417,0.0006011878,0.000005804181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1311189,0.00199964,0.8496521,0.001501518,0.0001742915,0.0002205059,0.004525597,0.008724596,0.0020829],"genre_scores_gemma":[0.7695597,0.0007422677,0.2105522,0.0008268205,0.0002088485,0.0003557203,0.01268789,0.0004565588,0.004610005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01118623,"threshold_uncertainty_score":0.02224225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118806842799383,"score_gpt":0.2414078766402588,"score_spread":0.2295271923603205,"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."}}