{"id":"W7104250620","doi":"10.5281/zenodo.17542512","title":"Survey on Customer Behavior Data Analysis for Product Purchasing","year":2025,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sciencetech (Canada)","funders":"","keywords":"Purchasing; Product (mathematics); Context (archaeology); Consumer behaviour; Data collection; Resource (disambiguation); New product development; Voice of the customer; Customer intelligence","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":[0.00204102,0.000453539,0.0006253521,0.003043042,0.0003923783,0.000667304,0.0007167718,0.0005404354,0.005227847],"category_scores_gemma":[0.006421811,0.0001621452,0.0007430593,0.00513265,0.0001184393,0.000697417,0.0006176599,0.0006880593,0.005481884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007866327,"about_ca_system_score_gemma":0.0009693293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009018801,"about_ca_topic_score_gemma":0.01493278,"domain_scores_codex":[0.9965562,0.0007385065,0.0004030098,0.0003862667,0.001660126,0.0002559669],"domain_scores_gemma":[0.9931035,0.001588755,0.0005796545,0.0009593147,0.003388697,0.0003801631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0007033071,0.001379205,0.2859839,0.001579795,0.000369221,0.0002215274,0.0003908762,0.00469123,0.004739755,0.002485179,0.416719,0.280737],"study_design_scores_gemma":[0.00007001279,0.0005263037,0.7374227,0.0002203306,0.0001048195,0.000578486,0.0005306962,0.02287362,0.005503927,0.0009461855,0.231139,0.0000839323],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2694457,0.001300617,0.01205712,0.0008269805,0.0001279204,0.0009938251,0.6953695,0.002460363,0.01741799],"genre_scores_gemma":[0.1895168,0.0006172338,0.01547687,0.0003030492,0.00004827431,0.001025656,0.7868854,0.000160992,0.005965654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009018801,"threshold_uncertainty_score":0.01793265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1072856464700137,"score_gpt":0.3095908742552928,"score_spread":0.2023052277852791,"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."}}