{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002794985,0.0001206933,0.00009450785,0.000133122,0.0002064601,0.0005362075,0.0000660518,0.00005922801,0.0004292762],"category_scores_gemma":[0.00005363137,0.0001083831,0.00009827298,0.000337814,0.00002059916,0.001130681,0.00004052343,0.0001100821,0.0009146721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000668822,"about_ca_system_score_gemma":0.00001840387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007668093,"about_ca_topic_score_gemma":0.000003696309,"domain_scores_codex":[0.9991726,0.000005334328,0.0001945247,0.0002510599,0.0001729708,0.0002035545],"domain_scores_gemma":[0.9997607,0.00003821211,0.00004322647,0.00006943522,0.00007751511,0.00001088219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002668727,0.00021111,0.008171993,0.001547122,0.0002323003,0.00001582161,0.000285804,0.02247747,0.007680233,0.5305296,0.3254204,0.1031612],"study_design_scores_gemma":[0.0003064135,0.00001411335,0.002240926,0.00004119123,0.00009228278,0.000002034813,0.0002239475,0.4203879,0.0002056311,0.0002572174,0.5760804,0.0001478516],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.235262,0.0004289272,0.6554989,0.003749247,0.004403352,0.001526416,0.00009354534,0.003098202,0.09593944],"genre_scores_gemma":[0.9883319,0.000009523464,0.0003503578,0.0002834805,0.001939761,0.00005760124,0.001678141,0.00002901222,0.007320241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7530699,"threshold_uncertainty_score":0.9998632,"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."}}