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

• URL: http://arxiv.org/abs/2302.04477v1
• Date: Thu, 9 Feb 2023 07:39:34 GMT
• Title: An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint
• Authors: Ziang Yan, Shusen Wang, Guorui Zhou, Jingjian Lin, Peng Jiang
• Abstract summary: We propose a novel end-to-end framework to directly optimize the business goal under budget constraints. Our core idea is to construct a regularizer to represent the marketing goal and optimize it efficiently using gradient estimation techniques. Our proposed method is currently deployed to allocate marketing budgets for hundreds of millions of users on a short video platform.
• Score: 25.89397524825504
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level budget allocation is an essential task in marketing campaigns. Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution. Since the objectives of these two stages might not be perfectly aligned, such a two-stage paradigm could hurt the overall marketing effectiveness. In this paper, we propose a novel end-to-end framework to directly optimize the business goal under budget constraints. Our core idea is to construct a regularizer to represent the marketing goal and optimize it efficiently using gradient estimation techniques. As such, the obtained models can learn to maximize the marketing goal directly and precisely. We extensively evaluate our proposed method in both offline and online experiments, and experimental results demonstrate that our method outperforms current state-of-the-art methods. Our proposed method is currently deployed to allocate marketing budgets for hundreds of millions of users on a short video platform and achieves significant business goal improvements. Our code will be publicly available.

Related papers

• Procurement Auctions via Approximately Optimal Submodular Optimization [53.93943270902349]
  We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. Our goal is to design computationally efficient auctions that maximize the difference between the quality of the acquired services and the total cost of the sellers.
  arXiv  Detail & Related papers  (2024-11-20T18:06:55Z)
• End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling [12.160403526724476]
  We propose a novel End-to-End Cost-Effective Incentive Recommendation (E3IR) model under budget constraints. Specifically, our methods consist of two modules, i.e., the uplift prediction module and the differentiable allocation module. Our E3IR improves allocation performance compared to existing two-stage approaches.
  arXiv  Detail & Related papers  (2024-08-21T13:48:00Z)
• Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning [22.993339296954545]
  We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies.
  arXiv  Detail & Related papers  (2023-09-06T02:35:46Z)
• Towards General and Efficient Online Tuning for Spark [55.30868031221838]
  We present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent.
  arXiv  Detail & Related papers  (2023-09-05T02:16:45Z)
• Model-based Constrained MDP for Budget Allocation in Sequential Incentive Marketing [28.395877073390434]
  Sequential incentive marketing is an important approach for online businesses to acquire customers, increase loyalty and boost sales. How to effectively allocate the incentives so as to maximize the return under the budget constraint is less studied in the literature. We propose an efficient learning algorithm which combines bisection search and model-based planning.
  arXiv  Detail & Related papers  (2023-03-02T08:10:45Z)
• Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning [156.5667417159582]
  This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs) Planning in MPPs faces the unique challenge in finding a signaling policy that is simultaneously persuasive to the myopic receivers and inducing the optimal long-term cumulative utilities of the sender. We design a provably efficient no-regret learning algorithm, the Optimism-Pessimism Principle for Persuasion Process (OP4), which features a novel combination of both optimism and pessimism principles.
  arXiv  Detail & Related papers  (2022-02-22T05:41:43Z)
• Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions [74.00030431081751]
  We formalize the notion of user-specific cost functions and introduce a new method for identifying actionable recourses for users. Our method satisfies up to 25.89 percentage points more users compared to strong baseline methods.
  arXiv  Detail & Related papers  (2021-11-01T19:49:35Z)
• Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions [80.78077900288868]
  We decompose the design methodology of early-exit networks to its key components and survey the recent advances in each one of them. We position early-exiting against other efficient inference solutions and provide our insights on the current challenges and most promising future directions for research in the field.
  arXiv  Detail & Related papers  (2021-06-09T12:33:02Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)
• Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing [2.752817022620644]
  We propose a treatment effect optimization methodology for user marketing. This algorithm learns from past experiments and utilizes novel optimization methods to optimize cost efficiency with respect to user selection. Our proposed constrained and direct optimization algorithms outperform by 24.6% compared with the best performing method in prior art and baseline methods.
  arXiv  Detail & Related papers  (2020-04-21T01:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.