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Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash

Yixin Tang, Yicong Lin, Navdeep S. Sahni

arXiv 10 Nov 2023 · Statistics — Methodology · 1 citations (OpenAlex)

arXiv:2311.14698 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.

Citation extraction

27
references
31
in-text mentions
27
distinct cited
2
self-citations
10,804
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1G. E. Box, J. S. Hunter, and W. G. Hunter (2005) Statistics for experimenters0.64422100%
2E. M. Feit and R. Berman (2019) Test & roll: Profit-maximizing a/b tests0.64422100%
3G. J. Hitsch and S. Misra (2018) Heterogeneous treatment effects and optimal targeting policy evaluation0.64422100%
4G. J. Hitsch and S. Misra (2018) Heterogeneous treatment effects and optimal targeting policy evaluation0.64422100%
5R. L. PLACKETT and J. P. BURMAN (1946) THE DESIGN OF OPTIMUM MULTIFACTORIAL EXPERIMENTS0.40511100%
6D. Xiang, R. West, J. Wang, X. Cui, and J. Huang (2022) Multi armed bandit vs. a/b tests in e-commerce - confidence interval and hypothesis test power perspectives0.40511100%
7W. Duan, S. Ba, and C. Zhang (2021) Online experimentation with surrogate metrics: Guidelines and a case study0.40511100%
8G. Taguchi and S. Konishi (1987) Taguchi methods orthogonal arrays and linear graphs0.40511100%
9S. Athey, R. Chetty, G. W. Imbens, and H. Kang (2019) The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely0.40511100%
10P. K. Chintagunta (2018) Structural models in marketing0.40511100%

Showing the top 10 of 27 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Randomization Tests in Switchback Experiments0.40511