{"id":"W4383533106","doi":"10.1002/mar.21866","title":"Does (customer data) size matter? Generating valuable customer insights with less customer relationship risk","year":2023,"lang":"en","type":"article","venue":"Psychology and Marketing","topic":"Consumer Behavior in Brand Consumption and Identification","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"British Academy","keywords":"Customer intelligence; Customer to customer; Customer retention; Customer advocacy; Loyalty business model; Voice of the customer; Customer equity; Business; Computer science; Marketing; Customer relationship management; Service quality; Service (business)","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":[0.001757107,0.0002290181,0.0002191819,0.0003703081,0.0008953345,0.0003345084,0.0003106661,0.0001404088,0.0007816682],"category_scores_gemma":[0.0003470014,0.0001620033,0.00003212737,0.000749554,0.0001562869,0.001050047,0.0002387935,0.0003372819,0.002936759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009929368,"about_ca_system_score_gemma":0.00001345148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001330493,"about_ca_topic_score_gemma":0.0001960399,"domain_scores_codex":[0.9981528,0.0001585478,0.0003857727,0.0007248503,0.0002214414,0.000356587],"domain_scores_gemma":[0.9984029,0.0004662341,0.0003277091,0.0006422197,0.000132268,0.00002865356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001691625,0.00005462141,0.8950133,0.0001230303,0.0000505735,0.0000112058,0.00009080306,0.00002030298,0.001115428,0.0002064183,0.03190352,0.07124158],"study_design_scores_gemma":[0.0007426403,0.000001384862,0.8859589,0.00005754017,0.0001826721,0.000006864658,0.0003090653,0.001108208,0.000004485325,0.0001592926,0.1111687,0.0003002666],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840781,0.0001487423,0.00007658585,0.001071167,0.0009742134,0.0002774804,0.00001166409,0.0002857184,0.01307634],"genre_scores_gemma":[0.9935769,0.0002152795,0.0003331665,0.001034838,0.0004240144,0.00007369026,0.0001682482,0.00004329115,0.004130607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07926521,"threshold_uncertainty_score":0.9978396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05466477901955536,"score_gpt":0.3033289501992356,"score_spread":0.2486641711796803,"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."}}