arXiv:2606.26787v1 Announce Type: new Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement. We propose AIGP, a novel framework that leverages a Large Language Model (LLM) prompted with domain knowledge, structured data and textual context to make interpretable, knowledge-aware pricing decisions. For efficient deployment while maintaining hig
The increasing sophistication of LLMs and the recognition of their potential beyond generative text, combined with the growing need for more adaptive and interpretable e-commerce solutions, makes this development timely.
This framework demonstrates a concrete application of LLMs to critical business functions, moving beyond marketing and customer service to directly influence revenue and long-term strategic objectives in e-commerce.
Dynamic pricing, a core component of e-commerce, will become more interpretable, adaptable, and aligned with complex long-term business goals, potentially democratizing advanced pricing strategies.
- · E-commerce platforms
- · Retail businesses
- · Data scientists
- · Online consumers
- · Traditional algorithmic pricing model providers
- · Businesses relying on opaque pricing strategies
Wider adoption of LLM-driven decision-making in financial and operational sectors of e-commerce.
Increased pressure on LLM providers to develop more domain-specific and auditable models for business applications.
Potential for an 'AI agent' arms race in pricing, leading to more responsive and complex market dynamics across various sectors.
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