{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001905014,0.0004339971,0.0006242883,0.0007513048,0.0007422601,0.0005434451,0.0003224943,0.0002638388,0.002954133],"category_scores_gemma":[0.0006410163,0.0004690322,0.0004462551,0.002539215,0.0001528864,0.002952883,0.0003583159,0.0004013409,0.0003777428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002398021,"about_ca_system_score_gemma":0.0001751746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007594618,"about_ca_topic_score_gemma":0.0003346531,"domain_scores_codex":[0.9965342,0.0001227679,0.001348128,0.0006389634,0.0005602012,0.0007957636],"domain_scores_gemma":[0.9976323,0.0002272968,0.0008210728,0.0005473988,0.0007450707,0.00002686126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003015131,0.003043766,0.05204995,0.02224358,0.004616988,0.00008599068,0.003460129,0.004527834,0.5622097,0.08945272,0.1723494,0.08294484],"study_design_scores_gemma":[0.01358817,0.0001305516,0.1742072,0.01436568,0.01213257,0.00001283476,0.02182664,0.09560297,0.02658788,0.02026849,0.6166673,0.004609741],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2954686,0.008133817,0.08304787,0.0118663,0.009104329,0.003335671,0.00006746546,0.0004955929,0.5884804],"genre_scores_gemma":[0.9873512,0.0005263768,0.000383131,0.003232181,0.0006557333,0.00002591954,0.0000857752,0.00004642687,0.007693264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6918826,"threshold_uncertainty_score":0.9997761,"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."}}