arXiv cs.AI· Nan Wang, Mitchell Turiel·· 12 小时前AI 评分23
面向企业客户到销售分配的权衡感知区域设计：多目标决策系统
Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation
AI 导读
一套面向企业 GTM 规划的生产级决策系统，用于权衡感知的客户到销售分配，整合账户价值预测评分、基于合成控制式反事实框架的连续性估值，以及兼顾连续性、机会均衡与运营约束的多目标优化三层。历史回测显示优化器生成方案显著提升分配合规性，同时保持相近的销售机会分布；生产规划中采纳方案较原基线同时改善合规性与机会均衡。
正文 · 原文
Abstract:Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts receive seller coverage, which seller-account relationships are preserved, and how limited field capacity is distributed. Yet the process often remains manual, making it difficult to compare opportunities, value continuity, and evaluate tradeoffs among growth, balance, capacity, and feasibility.
We present a production decision system for tradeoff-aware customer-to-seller allocation in enterprise GTM planning. It integrates three components: a multidimensional account-value layer that combines predictive signals to score opportunity; an observational continuity-valuation layer that estimates the cost of breaking seller-account relationships using a synthetic-control-style counterfactual framework; and a multi-objective optimization layer that allocates coverage to high-value accounts while considering continuity, opportunity balance, and operational constraints.
Deployed as a scenario-based decision-support system, the framework enables planners to inspect and compare allocation tradeoffs. Historical backtesting shows that optimizer-generated scenarios substantially improve assignment compliance while maintaining comparable seller-level opportunity distribution. In production planning, adopted scenarios improved both assignment compliance and opportunity balance relative to the previous baseline. Directing scarce seller capacity toward higher-value accounts also offers incremental revenue potential at enterprise scale. The key contribution is an integrated, inspectable framework that turns territory carving into a governable decision process.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.31701 [cs.AI] |
| (or arXiv:2610.31701v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31701 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nan Wang [view email]
[v1]
Wed, 21 Oct 2026 18:03:34 UTC (2,488 KB)
来源：arXiv cs.AI · arxiv.org