{"id":"W7135233160","doi":"10.65713/ijtlsv1i301","title":"ENHANCING CUSTOMER RETENTION THROUGH AI-ENABLED CRM SOLUTIONS A MARKETING ANALYTICS PAPER OF GENPACT","year":2025,"lang":"","type":"article","venue":"IJTLS","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SAIT Polytechnic","funders":"","keywords":"Customer retention; Customer intelligence; Customer relationship management; Customer advocacy; Analytics; Customer satisfaction; Predictive analytics; Market segmentation; Customer to customer; Service quality","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":[0.004829994,0.000619683,0.0004592615,0.001468194,0.001765165,0.006004839,0.0007002008,0.001404078,0.005535724],"category_scores_gemma":[0.007233736,0.0002551959,0.0003763981,0.002004452,0.002043079,0.004999622,0.00159206,0.004192818,0.001077357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003227185,"about_ca_system_score_gemma":0.002824225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004374475,"about_ca_topic_score_gemma":0.004413955,"domain_scores_codex":[0.9980716,0.0006212703,0.00004176215,0.0002515712,0.0008320099,0.0001818368],"domain_scores_gemma":[0.9941981,0.003639461,0.0002135172,0.0002949548,0.001203069,0.0004508203],"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":[0.0002456836,0.0002368883,0.00450036,0.000347355,0.00007470221,0.0003915229,0.003904401,0.003902318,0.003022042,0.1291106,0.5126181,0.341646],"study_design_scores_gemma":[0.00008590863,0.0003973947,0.007309769,0.0005509782,0.00007358723,0.0003984098,0.003385852,0.02740864,0.009076633,0.06466552,0.8864997,0.0001475734],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.095,0.02300801,0.0388367,0.670084,0.01676027,0.0003755404,0.0007019502,0.001308855,0.1539247],"genre_scores_gemma":[0.6206536,0.03366651,0.03715346,0.07489597,0.01466104,0.0003838399,0.0007109261,0.001098225,0.2167765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006004839,"threshold_uncertainty_score":0.02554375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0257551432873806,"score_gpt":0.2720737399554815,"score_spread":0.2463185966681009,"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."}}