LCM-Promo: An LLM-Based Uplift Model for Promotion Allocation

Midavaine, Floris Fok, Paulo Mendes, Joao Silva, onder akacik, Dimas Lima, Zülküf Genç

LCM-Promo: An LLM-Based Uplift Model for Promotion Allocation: 5 upvotes on Hugging Face Daily Papers, #21 of 39 papers on 2026-11-10. Day-by-day upvote history.

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It had 2 upvotes by its first day, 3 upvotes by the next day, 5 upvotes by day 3, 5 upvotes by day 7, and has 5 now. It ranked #76 of 133 papers in its week and #223 of 340 in its month.

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.

Paper page on Hugging Face · arXiv

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