A Unified Framework for Marketing Budget Allocation

Zhao, Kui; Hua, Junhao; Yan, Ling; Zhang, Qi; Xu, Huan; Yang, Cheng

doi:10.48550/arxiv.1902.01128

Cited by 2 publications

(8 citation statements)

References 26 publications

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“…To allocate the budget in an optimal way, a two-stage solution [13,22] is a common choice. First, a response model 𝑦 = 𝑓 (𝑥, 𝑡) estimates the users' MPPs for each user described by 𝑥 and each price 𝑡.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

Chen

Liu

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, incentive allocations are commonly solved in a two-stage procedure. After training a response estimation model to estimate the users' mobile payment probabilities (MPP), a linear programming process is applied to obtain the optimal incentive allocation. However, the large amount of biased data in the training set, generated by the previous biased allocation policy, causes a biased estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign. CCS CONCEPTS• Applied computing → Marketing.

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Section: Proposed Methodsmentioning

confidence: 99%

“…However, as a black-box model, there are some gaps between the prediction and decision-making of deep neural networks [2]. Therefore, [13,22] propose a semi-black box model that extends the logarithmic demand curve through neural network and graph learning to solve this problem. Allocation Optimization.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

Chen

Liu

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

show abstract

“…Thus users' responses to incentives are not smooth across the entire interval. It is not reasonable to model these curves with just a linear representation with a fixed smooth exponential function as in (Zhao et al 2019). In this part, inspired by traditional hazard regression, we treat the time t in hazard regression as the value of coupons in our setting.…”

Section: Response Modelsmentioning

confidence: 99%

“…Ito et al (2017) solved price optimization by first forecasting the relationships between sales and prices. Zhao et al (2019) proposed a general online budget allocation framework consisting of two components: forecasting models and decision making. However, existing methods ignore the phenomenon of mutual influence between merchants and customers, i.e., bipartite influence.…”

Section: Introductionmentioning

confidence: 99%

“…Another issue is that the redemption data on the customer-side are partially observed, i.e., censored data. To address this issue, Zhao et al (2019) propose a response model with a sigmoid function, but ignore the effect of censored data. In reality, when a customer successfully redeems a coupon at a certain value, we cannot observe whether this customer will still redeem at a smaller coupon value, i.e., left-censored data.…”

Section: Introductionmentioning

confidence: 99%

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Joint Incentive Optimization of Customer and Merchant in Mobile Payment Marketing

Cai

et al. 2021

AAAI

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In the mobile Internet era, mobile payment service becomes the foundation of inclusive finance, which brings convenience and security to people. Various marketing strategies are designed to encourage mobile payment activities by allocating incentives such as coupons or commissions to customers or merchants. We summary two significant issues. First, there is a phenomenon of mutual influence between merchants and customers, i.e., bipartite influence issue, thus making the independent optimization of customers and merchants non-optimal. Second, the redemptions of coupons are partially observed, as we can only observe that the customer redeems the coupon or not at a specific incentive value, but cannot observe that at other incentive value, i.e., data censorship issue. In this paper, we propose a novel joint incentive optimization framework to address the above two issues. We propose to use a graph neural network to represent customers and merchants jointly by modeling the underlying bipartite influences. We then formulate the response model under the hazard regression setting and model the hazard rate with a piecewise nonlinear function to capture the changes of responses to different incentive values. Finally, we propose a linear programming method to allocate approximated optimal incentive values to customers and merchants in real-time. Extensive offline and online experimental results demonstrate the effectiveness of our proposed approach.

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A Unified Framework for Marketing Budget Allocation

Cited by 2 publications

References 26 publications

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

Joint Incentive Optimization of Customer and Merchant in Mobile Payment Marketing

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