{"id":"W2093015399","doi":"10.1002/dir.20027","title":"Can we predict customer lifetime value?","year":2005,"lang":"en","type":"article","venue":"Journal of Interactive Marketing","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":246,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Profitability index; Customer lifetime value; Profit (economics); Customer profitability; Customer value; Marketing; Sample (material); Value (mathematics); Business; Econometrics; Computer science; Microeconomics; Customer retention; Economics; Finance","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":[0.004517358,0.0007878469,0.0007964984,0.002305868,0.0004636406,0.003018882,0.001323615,0.002246195,0.003169585],"category_scores_gemma":[0.05018074,0.0003217287,0.0004804298,0.002699961,0.001090363,0.008630835,0.0007147708,0.002618238,0.001529674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001211234,"about_ca_system_score_gemma":0.0006345166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01081545,"about_ca_topic_score_gemma":0.009071791,"domain_scores_codex":[0.9987862,0.0005716484,0.00005832638,0.0001850849,0.0002280699,0.0001706107],"domain_scores_gemma":[0.9771332,0.01604168,0.002744131,0.001385106,0.001977831,0.0007180976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002941563,0.0002315408,0.7583166,0.0001081893,0.0002544582,0.0001306787,0.0005211851,0.06127756,0.0001169485,0.01074399,0.01038324,0.1576214],"study_design_scores_gemma":[0.0000602316,0.0004101833,0.2839758,0.0002814094,0.0001787535,0.0005090754,0.001471063,0.5629274,0.0006689177,0.1406156,0.008756433,0.0001451314],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073489,0.006152094,0.04266683,0.02497282,0.0003614792,0.00008524166,0.003648219,0.0003612302,0.01440324],"genre_scores_gemma":[0.9937212,0.0008362295,0.003025841,0.0003627702,0.0001581315,0.00002313856,0.00108619,0.00001851458,0.0007679844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01081545,"threshold_uncertainty_score":0.02389038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01001644444592861,"score_gpt":0.2453457079815457,"score_spread":0.2353292635356171,"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."}}