{"id":"W2887528003","doi":"10.1155/2018/3635107","title":"Application of Customer Segmentation for Electronic Toll Collection: A Case Study","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Basic Research Program of Shaanxi Province; Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China","keywords":"Cluster analysis; Computer science; Segmentation; Market segmentation; Data mining; Sample (material); Customer relationship management; Payment; Decision tree; Big data; Scale (ratio); Artificial intelligence; Marketing; Business; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001393107,0.0004573392,0.0003205964,0.001673706,0.001792972,0.001314827,0.001327072,0.001921509,0.002617487],"category_scores_gemma":[0.003127812,0.0002622268,0.0007755284,0.003089878,0.0006943078,0.001099107,0.0009730676,0.0007868718,0.0003662863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001847021,"about_ca_system_score_gemma":0.001125622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0101538,"about_ca_topic_score_gemma":0.01451403,"domain_scores_codex":[0.9982292,0.0007872275,0.0000967109,0.0001627369,0.0004513645,0.0002727826],"domain_scores_gemma":[0.9969532,0.001707148,0.0002973773,0.0003377684,0.0004225709,0.0002818921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001600248,0.006484243,0.3913303,0.001172845,0.0004102839,0.05058005,0.01676115,0.1578999,0.01532141,0.02547762,0.01634917,0.3166128],"study_design_scores_gemma":[0.0002468953,0.002119912,0.2349685,0.0002304341,0.0003506397,0.01842329,0.06875464,0.5862422,0.03513031,0.009001007,0.04417581,0.0003563918],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745086,0.0001437998,0.01747632,0.0004871785,0.00003011255,0.000268401,0.000447326,0.0001045658,0.006533748],"genre_scores_gemma":[0.9858138,0.0001504874,0.0120403,0.00007021119,0.00002091882,0.00007213769,0.0002123337,0.0000162427,0.001603519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0101538,"threshold_uncertainty_score":0.0201894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158567676713836,"score_gpt":0.2777961086099889,"score_spread":0.2662104318428506,"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."}}