A Unified Framework for Marketing Budget Allocation

by   Kui Zhao, et al.

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.


A Framework for Multi-stage Bonus Allocation in meal delivery Platform

Online meal delivery is undergoing explosive growth, as this service is ...

Cross-channel Budget Coordination for Online Advertising System

In online advertising (Ad), advertisers are always eager to know how to ...

An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint

Online platforms often incentivize consumers to improve user engagement ...

A Unified Framework for Conservative Exploration

We study bandits and reinforcement learning (RL) subject to a conservati...

Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers

Bayesian optimization (BO) is a powerful black-box optimization framewor...

DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-temporal Models

In this paper, we present a cost-based approach for the automatic select...

Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing

Sequential incentive marketing is an important approach for online busin...

Please sign up or login with your details

Forgot password? Click here to reset