{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003412814,0.0003704342,0.0004585271,0.002110224,0.004472824,0.004341166,0.002473889,0.000109585,0.009286808],"category_scores_gemma":[0.002403866,0.0004153427,0.0001734694,0.005352051,0.0002026271,0.001396982,0.003164106,0.0003715157,0.006285666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002497414,"about_ca_system_score_gemma":0.00001248329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003495228,"about_ca_topic_score_gemma":0.00002220465,"domain_scores_codex":[0.9963079,0.0002430341,0.0006888804,0.001435151,0.0006350513,0.000689977],"domain_scores_gemma":[0.9959638,0.00009545416,0.0003961316,0.001755146,0.001723897,0.00006561556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007033622,0.001313349,0.001707842,0.0006496835,0.001363825,0.00001092647,0.0003187148,0.000388047,0.001131803,0.004395978,0.5940441,0.3939724],"study_design_scores_gemma":[0.001218477,0.00006066744,0.05890152,0.00009187276,0.001505831,0.000001739648,0.0003155374,0.007809013,0.00009552574,0.00005160671,0.9295,0.000448232],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2620108,0.003032004,0.15334,0.01681977,0.008370411,0.02536649,0.02106966,0.006064675,0.5039262],"genre_scores_gemma":[0.9359789,0.0001086327,0.000145901,0.0007883491,0.0006564541,3.68792e-7,0.05539168,0.001284383,0.005645251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6739682,"threshold_uncertainty_score":0.9998298,"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."}}