{"id":"W4223417015","doi":"10.1142/s2196888822500208","title":"The Quest for Customer Intelligence to Support Marketing Decisions: A Knowledge-Based Framework","year":2022,"lang":"en","type":"article","venue":"Vietnam Journal of Computer Science","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Customer intelligence; Marketing and artificial intelligence; Customer advocacy; Voice of the customer; Customer knowledge; Computer science; Market intelligence; Customer to customer; Knowledge management; Customer retention; Marketing; Competitive intelligence; Marketing research; Customer lifetime value; Marketing management; Business; Artificial intelligence; Service quality; Intelligent decision support system","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":[0.007672156,0.0009118514,0.000725603,0.007852701,0.002638328,0.01858948,0.003471834,0.004343256,0.002493693],"category_scores_gemma":[0.007339848,0.0005966031,0.0009889393,0.005918501,0.01358612,0.01720639,0.005261923,0.003422133,0.0006896717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005157514,"about_ca_system_score_gemma":0.006928365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007917053,"about_ca_topic_score_gemma":0.006115896,"domain_scores_codex":[0.9958583,0.002195074,0.0002493377,0.0004386186,0.0009357902,0.0003229566],"domain_scores_gemma":[0.9922745,0.004850971,0.000696063,0.0005893033,0.0009551271,0.0006340911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001029098,0.00006852267,0.000545265,0.0001200951,0.00002388666,0.0002318066,0.0009444427,0.003343107,0.0001561338,0.9765601,0.001297497,0.01669882],"study_design_scores_gemma":[0.0000155845,0.00002889836,0.0004665769,0.000471117,0.00002870126,0.0001391742,0.001577698,0.02419963,0.0003028168,0.9390326,0.03369642,0.00004090531],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02432218,0.01238596,0.6713765,0.08810774,0.000438287,0.0004952635,0.0005185909,0.0003276428,0.2020278],"genre_scores_gemma":[0.5869437,0.009002356,0.3933278,0.003205903,0.0007223687,0.0004548051,0.0003774644,0.00004742506,0.005918146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01858948,"threshold_uncertainty_score":0.04057473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06333396029481826,"score_gpt":0.3342316419568033,"score_spread":0.2708976816619851,"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."}}