{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.01025889,0.0003513971,0.0009618491,0.001206162,0.0003563643,0.001136319,0.01860086,0.0001128317,0.00004327443],"category_scores_gemma":[0.03179457,0.0002887547,0.0001246447,0.007101675,0.001351823,0.003334483,0.01093985,0.0006666607,0.0002228309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008667214,"about_ca_system_score_gemma":0.0007460633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001142888,"about_ca_topic_score_gemma":0.0002860084,"domain_scores_codex":[0.9933305,0.000151719,0.002606041,0.001477988,0.001504135,0.0009295685],"domain_scores_gemma":[0.9831221,0.005781878,0.002639039,0.007720412,0.0004398158,0.0002967422],"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":[0.0001347823,0.00009514861,0.002283884,0.00001110797,0.00003652186,0.00003991681,0.0002714884,0.00001305697,0.0004979211,0.00002307434,0.05138719,0.9452059],"study_design_scores_gemma":[0.0007978426,0.0003416777,0.00480384,0.0004988824,0.00006251207,0.0001717642,0.06689538,0.008949597,0.0001838504,0.0007121773,0.9159698,0.0006126339],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7672966,0.002040258,0.1581368,0.01638391,0.006278077,0.001259585,0.04767009,0.0001807542,0.0007539805],"genre_scores_gemma":[0.9906608,0.002584328,0.00473005,0.0001884707,0.0009073682,0.0000150632,0.0007205932,0.00003810948,0.0001552449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9445933,"threshold_uncertainty_score":0.9999565,"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."}}