LCM-Promo: An LLM-Based Uplift Model for Promotion Allocation
Abstract
LCM-Promo, a finetuned large language model trained on transactional commerce data, predicts promotion response through monotonic curves and single-token conversion-threshold probabilities, improving conversion and reducing promotion cost in a production A/B test.
We present LCM-Promo, an LLM-based uplift model for promotion allocation. LCM-Promo is a task-specific Large Commerce Model (LCM): a finetuned LLM trained on transactional commerce data to predict user intent and promotion response. It learns monotonic promotion-response curves from production logs by interpreting single-token outputs as conversion-threshold probabilities, while conditioning on session context such as day and delivery shift. In a production A/B test on churn reactivation, LCM-Promo increased conversion by 1% and reduced promotion cost by 6% relative to the Segment-EMA production baseline.
Community
Here is our work on Promotion uplift modeling in collaboration with iFood.
A 6% drop in promotion cost with conversion going up is a result a business can actually feel. How did you check that the learned monotonic curves make sense for rare promotion levels?
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- TRIDENT-Seq: Profile-Matched Prefix-Future Contrast for Label-Efficient Payment Risk Modeling (2026)
- Cost-Aware Abstention for LLM-Based IT Ticket Classification (2026)
- CascadeRec: Deployed Adaptive LLM Compute Allocation for Multi-Tenant Content Recommendation (2026)
- OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios (2026)
- QueryFormer: Winning Solution for KDD Cup 2026 Tencent UniRec Challenge (2026)
- RecGroupMemory: Feedback-Aware Cohort Memory for E-Commerce Recommendation (2026)
- Generative Search and Recommendation at Shopify (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Get this paper in your agent:
hf papers read 2610.37570 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper
Collections including this paper 0
No Collection including this paper