{"id":"W7148627237","doi":"10.71465/ajbd780","title":"Big Data for Enhancing Customer Experience in Digital Marketing","year":2023,"lang":"","type":"article","venue":"American Journal Of Big Data","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Big data; Digital marketing; Purchasing; Customer advocacy; Customer intelligence; Personalized marketing; Customer experience; Product (mathematics)","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":[0.005154755,0.0006417448,0.0005707309,0.001907234,0.001494116,0.00683309,0.001121058,0.001498655,0.00686955],"category_scores_gemma":[0.01571993,0.0002922514,0.0005490842,0.003719886,0.00148222,0.01156023,0.003769861,0.002048195,0.00143687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200168,"about_ca_system_score_gemma":0.001256759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008684898,"about_ca_topic_score_gemma":0.00156317,"domain_scores_codex":[0.996292,0.002067701,0.0001515981,0.0002970248,0.0009870545,0.0002047227],"domain_scores_gemma":[0.9912124,0.005113915,0.0007751405,0.0009847337,0.001318693,0.0005951089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009387752,0.0009484432,0.03147319,0.002287605,0.0002729661,0.000460137,0.005443964,0.006291913,0.004083119,0.2030285,0.08850706,0.6562643],"study_design_scores_gemma":[0.0001650049,0.0008602854,0.03019195,0.002404315,0.0002817354,0.0006088555,0.01668008,0.05770532,0.008828776,0.5808792,0.3011005,0.0002939272],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2381069,0.04687217,0.2146651,0.1565224,0.005274412,0.002056461,0.006049258,0.002069757,0.3283835],"genre_scores_gemma":[0.8909069,0.01088004,0.08067968,0.007374534,0.001685409,0.0005772247,0.001259219,0.0001523182,0.006484718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00686955,"threshold_uncertainty_score":0.02726132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4337847677042681,"score_gpt":0.4281121127584623,"score_spread":0.005672654945805766,"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."}}