# Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions

arXiv: https://arxiv.org/abs/2207.14798 · HTML: https://mlsanity.com/paper/2207.14798/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [A Unified Framework for Marketing Budget Allocation](https://mlsanity.com/paper/1902.01128/) (2019)
- [E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling](https://mlsanity.com/paper/2108.13298/) (2021)
- [Customer Lifetime Value Prediction Using Embeddings](https://mlsanity.com/paper/1703.02596/) (2017)
- [Smart Pacing for Effective Online Ad Campaign Optimization](https://mlsanity.com/paper/1506.05851/) (2015)
- [Spending Money Wisely: Online Electronic Coupon Allocation based on Real-Time User Intent Detection](https://mlsanity.com/paper/2008.09982/) (2020)
- [Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models](https://mlsanity.com/paper/1508.06378/) (2015)
- [A framework for massive scale personalized promotion](https://mlsanity.com/paper/2108.12100/) (2021)

## Built on by (most cited first, 4 of 4)

- [Contrastive Multi-view Framework for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2306.14400/) (2023)
- [Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation](https://mlsanity.com/paper/2407.11039/) (2024)
- [Generalized Regression with Conditional GANs](https://mlsanity.com/paper/2404.13500/) (2024)
- [Optimizing Item-based Marketing Promotion Efficiency in C2C Marketplace with Dynamic Sequential Coupon Allocation Framework](https://mlsanity.com/paper/2409.08609/) (2024)

## Abstract (arXiv)

Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.