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New RL framework optimizes customer targeting for business actions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for Next Best Action (NBA) systems, which are used to determine customer interventions like discounts and promotions. This framework formulates NBA as a Constrained Markov Decision Process (CMDP) and integrates off-policy causal correction into its Linear Programming (LP) formulation. The system aims to maximize long-term economic value while adhering to operational constraints and causal grounding. In a case study involving rideshare coupon targeting, the optimized policy improved customer lifetime value by 19% compared to the estimated behavior policy. AI

IMPACT This framework could enhance customer lifetime value and optimize intervention strategies in various business targeting systems.

RANK_REASON The item describes a novel research framework and its validation on an industry case study, fitting the research bucket. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RL framework optimizes customer targeting for business actions

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The item describes a novel research framework and its validation on an industry case study, fitting the research bucket. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jun Liu ·

    Next Best Action: Causal Constrained Offline RL for Business Action Targeting

    Next Best Action (NBA) systems must select among customer interventions (e.g., discounts, promotions, and lifecycle actions), maximize long-term economic value, satisfy complex operational constraints, and remain causally grounded. Existing approaches lack a rigorous framework fo…

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