[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85305-en":3,"doc-seo-85305-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85305,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Trustworthy Synthetic Data for Campaign Decision Support Strategy Simulation Fidelity and the PolicySynth Framework","Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations because privacy constraints limit real-data access. Trustworthiness requires synthetic data to drive the same go/no-go campaign decisions as real data; however, prevailing metrics test distributional similarity rather than decision alignment, enabling misleading campaign steering. This work closes the decision-alignment gap with three contributions: SSF, PolicySynth, and a three-axis deployment quality standard spanning alignment, inference resistance, and novel record rate. Experiments show strong SSF stability and more reliable recommendations than baseline generators.","Trustworthy synthetic data for campaign decision support: strategy simulation fidelity and the PolicySynth framework􀀡  \nTung Danga,ω , The Hung Phungb , Son Lam Nguyenc and Tu Nguyend,ω  \na Graduate School of Science, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan b Faculty of Human Resource Management, Trade Union University, 169 Tay Son, Kim Lien, Hanoi, Vietnam  \nc Institute for Business Management Development, Academy Of Finance, 58 Le Van Hien Street, Dong Ngac Ward, Hanoi, Vietnam d Ministry of Industry and Trade, 23 Ngo Quyen, Hoan Kiem, Hanoi, Vietnam  \n\n| ARTICLE INFO |  | ABSTRACT |  |\n| --- | --- | --- | --- |\n| Keywords:\u003Cbr>synthetic-data decision support decision-alignment evaluation retention strategy simulation privacy-preserving DSS\u003Cbr>di!usion-based generators |  | Decision support systems (DSS) increasingly run retention what-if analysis on synthetic customer populations, because privacy constraints preclude unrestricted use of real data. Such a system is trustworthy only if the synthetic data lead managers to the same decisions as the real data would; yet prevailing criteria certify distributional similarity, not decision alignment, so a synthetic population can match every marginal distribution while still steering a marketing team toward the wrong campaigns. We close this decision-alignment gap with three contributions: strategy simulation fidelity (SSF), a criterion measuring how often the synthetic population yields the same go/no-go campaign decision as the real population; PolicySynth, a DSS framework whose generator is conditioned on the production churn scorer to align decision-relevant structure; and a three-axis reporting standard of decision alignment, membership-inference resistance, and novel-record rate as the minimum deployment quality gate. On a telecommunications churn corpus and a banking acquisition corpus, PolicySynth attains a mean SSF of 0.923 and 0.960, with seed-to-seed variance roughly ten times tighter than CTGAN on telecommunications and 2.5 times on banking. This stability is the deployable property: go/no-go recommendations shift by at most ±1 .2 percentage points between monthly retraining cycles, against ±11 .5 for CTGAN, a reversed recommendation on one campaign in nine. A bootstrap baseline matches PolicySynth on SSF yet copies real records verbatim and fails membership inference, evidence that no single axis su\"ces. PolicySynth reliably supports directional go/nogo screening; its ROI estimates diverge from real outcomes by 70 to 78% and require the volume correction we document. |  |\n| 1. Introduction\u003Cbr>Customer churn costs the telecommunications and subscription industries an estimated USD35billion a year[1, 2], and customer acquisition in retail banking is a comparably large, recurring marketing outlay. Every targeted campaign commits scarce budget against an uncertain response: a poorly targeted o!er is spent on customers who would have acted anyway, and a poorly designed one fails to move those who are genuinely persuadable. Marketing decisionmakers therefore want a decision support system (DSS) that can test a campaign design in simulation before any real budget is committed [3, 4, 5] . The natural data layer for such a system is a synthetic copy of the customer base: it supports unlimited what-if experiments while keeping personally identifiable records out of the analytics environment, as modern data-protection regimes increasingly require. Industry is already deploying such systems, but the DSS literature on synthetic-data-driven architectures is thin and its evaluation methodology has not kept pace [6, 7] .\u003Cbr>􀀡\u003Cbr>ωCorresponding author\u003Cbr> [dangthanhtung91@g.ecc.u-tokyo.ac.jp](dangthanhtung91@g.ecc.u-tokyo.ac.jp) (T. Dang); [tunn@moit.gov.vn](tunn@moit.gov.vn) (T. Nguyen)\u003Cbr>ORCID(s): 0000-0002-4974-9632 (T. Dang); 0009-0001-8682-5235 (T.H. Phung); 0009-0001-5302-5047 (S.L. Nguyen); 0009-0001-1781-2617 (T. Nguyen)\u003Cbr>1 |  |  | A synthetic-data","cbCaiqD9G2m9qHos","https://ap.wps.com/l/cbCaiqD9G2m9qHos","pdf",2565935,4,1,15,"English","en",105,"# Introduction\n## Decision-alignment gap\n## Contributions: SSF and PolicySynth\n## Deployment quality reporting standard\n# Experiments","[{\"question\":\"What problem does the document identify with existing synthetic-data decision support evaluations?\",\"answer\":\"Existing evaluations emphasize distributional similarity, which can still produce decision misalignment—leading managers to fund wrong campaigns even when marginal statistics match.\"},{\"question\":\"What is strategy simulation fidelity (SSF) in this work?\",\"answer\":\"SSF is a criterion that measures how often the synthetic population yields the same go/no-go campaign decision as the real population within a parameterized strategy family.\"},{\"question\":\"How does PolicySynth aim to improve decision alignment?\",\"answer\":\"PolicySynth uses a generator trained with a decision-conditioning loss derived from the production churn scorer, targeting decision-relevant structure to improve stability of 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