Adaptive Ecommerce Personalization

Improved ecommerce engagement and conversion using real-time reinforcement learning–based personalization.

Key Takeaways:

• Real-Time Relevance: Utilized RAG frameworks and Generative AI personas to contextualize user intent and deliver meaningful recommendations.

• Conversion Growth: Validated that dynamic session-level decisioning translates into a 15-20% lift in conversion accuracy.

• Architectural Validation: Confirmed that local LLMs can maintain data privacy and reduce operational costs while achieving high groundedness and faithfulness scores.

Reinforcement Learning Architectures Drive Real-Time Conversion Lift

Static product recommendations act as a hidden bottleneck in e-commerce growth, failing to capture the real-time evolution of customer intent. Transitioning from rigid, pre-calculated lists to adaptive personalization allows recommendations to evolve as fast as the user's live session behavior.

Remediating Engagement Decay Without System Disruption

The retailer’s existing system had access to less than half of the common product information and lacked the context of a shopper’s cart or purchase history. The mandate was to optimize session-level decision-making across digital channels without disrupting existing infrastructure.

Architecture: Semantic Personalization

We designed a solution combining a leading LLM with Retrieval-Augmented Generation (RAG) to anchor the model to the retailer's proprietary data.

• Technical Components:

• Hybrid Search: Implemented a hybrid similarity-search endpoint to retrieve context from a vector database using numeric representations of scientific text.

  • LLM-as-Judge: Evaluated RAG pipelines using other LLMs to score groundedness and faithfulness (achieving 4.5/5).

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Governed Metrics for AI

Single logic across every interface

Compliance Reporting

Reporting automated

Audience Segmentation

Targeting efficiency improved.