权衡感知的销售区域设计：企业客户到销售分配的多目标决策系统
Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation
AI 导读
研究者提出一套用于企业 GTM 规划中客户到销售分配的生产级决策系统，包含三维账户价值评分层、基于合成控制反事实框架的关系连续性估值层，以及多目标优化层。历史回测显示优化器生成的方案显著提升分配合规率，同时保持可比的销售级机会分布；生产规划中被采用的方案相对基线同时改善了合规率与机会平衡。该框架将区域划分变为可治理的决策流程，将稀缺销售能力导向高价值账户还带来增量收入潜力。
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