business development

This study addresses the trade-off between cost and service quality in multichannel customer service by modeling the entire service process as a gated system. It jointly optimizes decisions across three levels: strategic (channel deployment), tactical (staffing and AI allocation), and operational (real-time scheduling). Leveraging operations research, dynamic modeling, and numerical simulation, the work derives a structured optimal request-handling policy and uncovers a counterintuitive insight: judicious deployment of AI chatbots not only enhances service efficiency but also significantly improves service quality, thereby achieving simultaneous optimization of cost and customer experience.

Current large language models (LLMs) exhibit dual limitations in enterprise competitive analysis: insufficient access to real-time commercial knowledge and inadequate multidimensional competitive cognition, leading to strategic decision bias. To address this, we propose a multidimensional business-element-guided framework specifically designed for competitive analysis. Our approach innovatively integrates interpretable business dimensions—such as market positioning, product competitiveness, and technological trends—explicitly into LLM reasoning. It combines prompt-engineering-driven multi-faceted cue injection, structured domain-knowledge alignment, and a dual-track evaluation mechanism integrating quantitative metrics and qualitative assessment. Empirical evaluation on real-world tasks demonstrates that our method improves key judgment accuracy by 23.6% and analytical consistency by 31.2% over baseline models, significantly enhancing the credibility and operational feasibility of strategic recommendations.

This study investigates the efficacy of integrating CAVE (Cave Automatic Virtual Environment) systems with adaptive virtual agents to support immersive sales training, specifically targeting interpersonal communication and negotiation skills. A within-subjects experimental design was employed, in which 20 participants engaged with virtual agents exhibiting distinct communication styles across four randomized dealership scenarios, facilitated by a semi-immersive VR platform enabling collaborative learning and peer feedback. Results indicate stable levels of presence and immersion across all conditions; neither environmental variables nor agent communication style significantly affected experiential consistency (p > 0.05), confirming system feasibility and robustness. The key contribution is the development of the first controllable, scalable sales training paradigm leveraging CAVE infrastructure and parameterizable virtual agents—providing empirical validation and a technical framework for high-fidelity, simulation-based soft-skills training in professional contexts.

This paper investigates cooperative pricing between a platform and independent sellers in a revenue-sharing Bertrand game. Addressing the limitation of conventional models that neglect distributional collaboration, we develop an extended model wherein the revenue-sharing ratio is endogenously determined and sellers possess outside options. Using game-theoretic analysis and Nash equilibrium characterization, we establish— for the first time—that under specific cost structures and revenue-sharing parameters, introducing independent sellers can simultaneously increase both the incumbent manufacturer’s profit and consumer surplus, thereby overturning the conventional efficiency–profit trade-off. We further derive necessary and sufficient conditions for the existence and uniqueness of multiple equilibrium types, and demonstrate that the revenue-sharing mechanism exerts a non-monotonic effect on market efficiency, firm profits, and social welfare. Our findings provide theoretical foundations and actionable insights for cooperative pricing and revenue-allocation mechanism design in platform economies.

Existing CRM evaluation benchmarks fail to capture real-world business complexity, hindering the integration and validation of AI agents in professional settings. Method: We introduce CRMArena—the first industrial-grade CRM workflow benchmark—featuring nine realistic tasks across three roles (service agent, analyst, manager), grounded in 16 highly interdependent object types and latent-variable modeling to encode intricate business logic and regulatory constraints. It uniquely integrates domain expert knowledge with latent-variable formalization and evaluates agents via dual paradigms: ReAct prompting and structured function calling, under high-fidelity object-relational modeling and dynamic data distribution simulation. Contribution/Results: CRMArena systematically exposes critical LLM agent limitations in rule adherence and structured function invocation. Experiments show state-of-the-art LLM agents achieve only 40% task completion under ReAct and 55% under function calling—highlighting the stringent demands of real-world CRM on robustness, compliance, and structured operational fidelity.

This paper addresses the practical deployment challenge of heterogeneous treatment effects (HTE) in marketing. We propose a causal-driven constrained optimization framework: first estimating conditional average treatment effects (CATE) via uplift modeling, then jointly optimizing target audience selection and incentive policy design under hard constraints—including budget limits and sales decay thresholds—to maximize revenue and retention rate. We introduce the novel “causal uplift + constrained allocation” two-stage paradigm, ensuring both KPI alignment and customer experience preservation, while maintaining interpretability and engineering deployability. Offline evaluation employs uplift AUC, inverse propensity scoring (IPS), and self-normalized IPS (SNIPS). Large-scale online A/B tests demonstrate significant improvements over propensity score matching and static baselines across user retention targeting, campaign incentivization, and spending-threshold assignment—yielding higher revenue, improved task completion rates, and strict adherence to experience constraints.

This study addresses persistent inefficiencies in traditional R&D—namely, fragmented knowledge discovery, inadequate cross-disciplinary integration, and delayed decision-making. To overcome these challenges, we propose an integrated intelligent R&D framework that unifies scientific literature, patent databases, and experimental data for multimodal deep mining. Crucially, we pioneer the application of large language models (LLMs) to real-time market intelligence analysis and adaptive R&D management, thereby enhancing hypothesis generation, accelerating knowledge transfer, and enabling cross-domain insight fusion. Complementing this, we introduce a lightweight ethical governance mechanism to ensure technical reliability and sustainability. Empirical evaluation demonstrates that the framework significantly improves R&D agility and decision quality: the time-to-market for frontier technologies is reduced by 32% on average, and innovation conversion rates increase by 27%.

This work addresses key challenges in scoring high-value sales leads—namely, prolonged decision cycles, sparse labels, difficulty in modeling unstructured interaction semantics, and the lack of quantifiable lead prioritization—by proposing a discriminative scoring framework grounded in large language models (LLMs). The framework integrates structured CRM features with unstructured customer text and introduces a novel hierarchical preference ranking optimization (HPRO) method, which transforms sparse binary labels into preference pairs encoding funnel-stage information. Joint learning leverages both pointwise and pairwise supervision signals, enabled by funnel-aware preference construction and a margin-aware Bradley–Terry model, marking the first effective discriminative application of LLMs to sales lead ranking. Evaluated on real-world data from a leading new-energy vehicle brand, the model achieves an AUC of 0.8161, improves top-lead ranking precision by 39.7%, and drives a 9.5% increase in revenue over a 132-day online A/B test, demonstrating substantial commercial impact.

Existing dialogue benchmarks inadequately assess the persuasive capability and conversion efficacy of large language models (LLMs) in sales scenarios. To address this gap, this work proposes SalesLLM, the first bilingual sales dialogue benchmark explicitly centered on transaction outcomes, spanning financial and consumer goods domains with over 30,000 script configurations and more than 1,800 multi-turn dialogues, featuring controllable difficulty levels and role specifications. We develop an end-to-end automated evaluation pipeline integrating an LLM-based scorer and a fine-tuned BERT classifier, alongside a trained CustomerLM user simulator to mitigate role confusion. Experiments demonstrate that SalesLLM scores exhibit strong alignment with expert human judgments (Pearson r = 0.98), effectively differentiate performance across 15 mainstream LLMs, and reveal that certain models achieve human-level proficiency, thereby validating the benchmark’s validity and scalability.

This work proposes SalesCopilot, the first end-to-end real-time AI assistant designed to address the 25–65 second delays caused by sales representatives manually retrieving product information during customer calls—a bottleneck that severely degrades user experience and operational efficiency. SalesCopilot integrates streaming speech-to-text transcription, large language model (LLM)-driven query understanding, and retrieval-augmented generation (RAG) over a structured product database to deliver domain-agnostic, instant responses. Evaluated in an insurance sales setting, the system achieves an average response latency of 2.8 seconds with 100% query detection accuracy, yielding a 14-fold speedup over manual lookup and effectively eliminating the information-access bottleneck in live sales interactions.