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Promotions play a crucial role in e-commerce platforms, and various cost
structures are employed to drive user engagement. This paper focuses on
promotions with response-dependent costs, where expenses are incurred only when
a purchase is made. Such promotions include discounts and coupons. While
existing uplift model approaches aim to address this challenge, these
approaches often necessitate training multiple models, like meta-learners, or
encounter complications when estimating profit due to zero-inflated values
stemming from non-converted individuals with zero cost and profit.
To address these challenges, we introduce Incremental Profit per Conversion
(IPC), a novel uplift measure of promotional campaigns' efficiency in unit
economics. Through a proposed response transformation, we demonstrate that IPC
requires only converted data, its propensity, and a single model to be
estimated. As a result, IPC resolves the issues mentioned above while
mitigating the noise typically associated with the class imbalance in
conversion datasets and biases arising from the many-to-one mapping between
search and purchase data. Lastly, we validate the efficacy of our approach by
presenting results obtained from a synthetic simulation of a discount coupon
campaign.
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