sales

Traditional revenue forecasting approaches struggle to uncover the underlying customer behavioral drivers—such as customer acquisition, repeat purchase rates, and average transaction value—that influence revenue dynamics. To address this limitation, this work proposes the Customer-Based Multi-Task Transformer (CBMT), which uniquely integrates multi-task learning with a Transformer architecture to jointly model customer behavioral metrics and total revenue through shared representations. Furthermore, CBMT incorporates a downstream alignment mechanism to enhance both interpretability and predictive accuracy. Empirical evaluation on real-world customer transaction panel data demonstrates that CBMT outperforms existing methods across 23 out of 24 evaluation metrics, achieving a 30% reduction in total sales prediction error compared to the strongest baseline and significantly surpassing single-task models employed by 74.3% of firms.

Existing retail simulators struggle to model the cross-stage influence of sellers’ early decisions on final purchases through multi-stage interactions. This work proposes RetailSim—the first end-to-end retail simulation framework—that integrates a diverse product space, persona-driven large language model agents, and multi-round buyer-seller interactions to holistically model the entire purchase journey from persuasion to transaction. RetailSim enables high-fidelity simulation of cross-stage dependencies for the first time, supporting both sales strategy evaluation and buyer persona inference. Its validity is established through dual verification: human behavioral fidelity assessment and meta-evaluation against established economic principles. Experiments successfully reproduce key empirical regularities—including demographic purchasing disparities, price-demand relationships, and heterogeneous price elasticities—demonstrating the framework’s practical utility for strategy testing.

This work addresses the high cost and limited scalability of manually constructing high-quality data products—such as question-answer pairs and database views—which traditionally rely on domain experts. To overcome these challenges, the authors propose an automated optimization framework based on a multi-agent system, introducing for the first time an agent control center architecture. This architecture continuously identifies user queries, monitors multidimensional quality metrics, and integrates a human-in-the-loop mechanism to ensure observability and iterative refinement of data assets. By maintaining human oversight while automating core optimization processes, the approach substantially reduces manual effort and significantly enhances the relevance, coverage, usability, and trustworthiness of data products.

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.

Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework. Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs. Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.

This work addresses the discrepancy between high offline metrics and poor online performance of lead-ranking models in CRM systems by proposing SalesLoop, a closed-loop reinforcement learning framework. SalesLoop introduces a performance-aware reward mechanism and a novel Discriminative Group Relative Policy Optimization (Discriminative GRPO) method, which for the first time adapts group relative policy optimization to discriminative ranking models. This enables listwise objective optimization and dynamic policy adaptation under temporal distribution shifts. Experimental results demonstrate that the approach improves NDCG@K and P@K by 7.9% and 15.8%, respectively. A 160-day A/B test shows a significant 4.7%–8.7% increase in cumulative conversion rate, achieves a 44.1% recall rate within the top-10% ranked leads, and enhances conversion rates for high-intent leads by 2.3×.

This work addresses the lack of evaluation benchmarks for end-to-end operational capabilities of large language model (LLM) agents in real-world business settings. We introduce the first simulated marketplace grounded in authentic cross-border trade data from Alibaba.com, enabling AI agents to execute long-horizon decisions—including procurement, pricing, sales, and compliance—to maximize profitability. To assess performance realistically, we propose an end-to-end evaluation framework incorporating opportunity estimation, skill-level metrics, and action-level reward attribution, thereby avoiding overreliance on single profit indicators or environmental shortcuts. Systematic evaluation of 15 state-of-the-art LLMs reveals up to a nine-fold difference in final net worth, with even the best-performing agent significantly underperforming human strategies. Our analysis further uncovers key value-creating and value-destroying behaviors and distinct operational styles across agents.

This study addresses the challenges of extreme demand volatility, delayed pricing responses, and misalignment between short-term revenue and long-term profitability during major fashion e-commerce promotions. To tackle these issues, the authors propose a high-frequency “predict–optimize” automated pricing system that breaks away from traditional weekly decision cycles by operating at a minute-level granularity. The system achieves the first industrial-scale deployment of daily multi-objective dynamic pricing in large-scale e-commerce settings, combining gradient-boosted tree models for daily demand forecasting with a multi-objective optimization framework to generate real-time pricing strategies that jointly maximize long-term profit and net merchandise value. Evaluated across 23 A/B tests in 12 Zalando markets from 2023 to 2024, the system delivered approximately 6% higher profit while maintaining sales volume and has since been fully deployed for promotional pricing.