  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.
