营销预算分配的统一框架
A Unified Framework for Marketing Budget Allocation
摘要：
虽然营销预算分配在传统企业中已经研究了几十年，但如今，由于动态环境和复杂的决策过程，在线业务带来了更多挑战。在本文中，我们提出了一个新的统一的营销预算分配框架。通过利用丰富的数据，拟议的数据驱动方法可以帮助我们克服挑战，做出更明智的决策。在我们的方法中，一个半黑箱模型被建立来预测动态市场响应，并提出了一个有效的优化方法来解决复杂的分配任务。首先，在每个细分市场的反应是通过探索历史数据，通过一个半黑箱模型，其中的logit需求曲线的能力是由神经网络增强预测。反应模型揭示了销售额与营销成本之间的关系。基于学习的模型，预算分配，然后制定为一个优化问题，我们设计了有效的算法来解决它在连续和离散设置。在一个统一的优化范式中支持多种业务约束，包括成本上限、利润下限或ROI下限。所提出的框架是易于实现，并随时处理大规模的问题。在阿里巴巴集团已经成功应用于多个场景。离线实验和在线A/B测试的结果表明了该方法的有效性。
英文摘要：
While marketing budget allocation has been studied for decades in traditional business, nowadays online business brings much more challenges due to the dynamic environment and complex decision-making process. In this paper, we present a novel unified framework for marketing budget allocation. By leveraging abundant data, the proposed data-driven approach can help us to overcome the challenges and make more informed decisions. In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task. First, the response in each market-segment is forecasted by exploring historical data through a semi-black-box model, where the capability of logit demand curve is enhanced by neural networks. The response model reveals relationship between sales and marketing cost. Based on the learned model, budget allocation is then formulated as an optimization problem, and we design efficient algorithms to solve it in both continuous and discrete settings. Several kinds of business constraints are supported in one unified optimization paradigm, including cost upper bound, profit lower bound, or ROI lower bound. The proposed framework is easy to implement and readily to handle large-scale problems. It has been successfully applied to many scenarios in Alibaba Group. The results of both offline experiments and online A/B testing demonstrate its effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
