research-article

A Unified Framework for Marketing Budget Allocation

Authors:

Cheng YangAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 1820 - 1830

https://doi.org/10.1145/3292500.3330700

Published: 25 July 2019 Publication History

Abstract

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.

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Cited By

Sun ZYang HLiu DWeng YTang XHe X(2024)End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688147(560-569)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688147
Zhou HHuang RLi SJiang GZheng JCheng BLin WBaeza-Yates RBonchi F(2024)Decision Focused Causal Learning for Direct Counterfactual Marketing OptimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672353(6368-6379)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672353
Zhang XWang KWang ZDu BZhao SWu RShen XLv TFan CBaeza-Yates RBonchi F(2024)Temporal Uplift Modeling for Online MarketingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671560(6247-6256)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671560
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A Unified Framework for Marketing Budget Allocation

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cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 ACM.

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Published: 25 July 2019

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Sun ZYang HLiu DWeng YTang XHe X(2024)End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift ModelingProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688147(560-569)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688147
Zhou HHuang RLi SJiang GZheng JCheng BLin WBaeza-Yates RBonchi F(2024)Decision Focused Causal Learning for Direct Counterfactual Marketing OptimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672353(6368-6379)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672353
Zhang XWang KWang ZDu BZhao SWu RShen XLv TFan CBaeza-Yates RBonchi F(2024)Temporal Uplift Modeling for Online MarketingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671560(6247-6256)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671560
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Gong CYao DZhang LChen SLi WSu YBi JAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)CausalMMM: Learning Causal Structure for Marketing Mix ModelingProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635766(238-246)Online publication date: 4-Mar-2024
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Huang YWang SGao MWei XLi CLuo CZhu YXiao XLuo YChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent MarketingCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648320(226-234)Online publication date: 13-May-2024
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Moraes FManuel Proença HKornilova AAlbert JGoldenberg DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Uplift Modeling: From Causal Inference to PersonalizationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615298(5212-5215)Online publication date: 21-Oct-2023
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