Traditional marketing analytics often fall short in addressing the complexities of today’s retail landscape. Retail marketing is less effective due to a variety of challenges including:

• Siloed channel optimization
• Static customer segmentation
• Delayed customer feedback cycles
• Channel cannibalization

Powered by AI technology, reinforcement learning can optimize retail marketing in today’s digital age. Let’s explore how reinforcement learning (RL) and store assistants can optimize retail marketing strategies and improve the customer experience (CX).

Reinforcement learning: AI-driven marketing optimization

AI-powered Reinforcement Learning (RL) enables retailers to optimize their marketing by continuously learning and adapting from real-time customer interactions.

RL enables retailers to:

• Optimize their marketing for multiple objectives simultaneously, such as online sales, store visits, and long-term customer value.
• Create a balance between new marketing strategies and proven approaches.
• Manage delayed reward signals, such as in-store visits occurring days after digital interactions.

A major North American retailer implemented an RL system to optimize cross-channel marketing and achieved impressive results. After six months of operation, the system delivered a 10% improvement in conversion rates, a 7% increase in average order value, and a 12% improvement in marketing ROI. The retailer also saw an 8% increase in cross-channel “Click and Collect” orders and a 5% increase in total sales without channel cannibalization.

The core RL architecture utilizes a Deep Q-Network (DQN) model that operates on a 120+ dimensional state space, which captures customer attributes, context, and journey stage – while selecting from 40 discrete marketing actions across channels. The model delivers a multi-term reward function with balanced competing objectives, including a negative term for channel cannibalization.

Here are the datasets curated using Onix Birds:

• Customer interaction data including:
  • Purchase history with product IDs, timestamps, and channel information
  • Digital touchpoints (clicks, views, cart additions)
  • Basic RFM (recency, frequency, monetary) metrics
• Product data including:
  • Product catalog with categorization
  • Price points and margin information
  • Inventory availability by location
• Action data including:
  • Marketing campaign history
  • Promotional calendar
  • Channel capacity constraints

Here are some of the key insights from the RL implementation:

• Data quality is critical: Identity resolution accuracy and temporal precision directly correlate with model performance.
• Data discovery drives performance: Integrating data across domains produces the highest-value features.
• State representation matters more than model complexity: Expanding the feature set yields more improvement than architectural changes.
• Reward engineering requires iteration: Balancing short-term engagement and long-term value metrics is crucial.

AI-powered store assistant: enhancing retail efficiency

In addition to marketing optimization, AI can also enhance store operations and customer experience. An AI-Powered Store Assistant provides retailers with actionable intelligence, automation, and operational efficiency.

Here are some of the key capabilities of an AI-powered store assistant:

• Associate & in-store interface: Provides real-time visibility into store performance.
• AI assistant: Provides conversational AI assistance for retailer staff and customers.
• Customer analytics & sales dashboard: Delivers real-time Insights into retail sales, foot traffic, and customer behavior.
• Inventory & staff management: Helps optimize stock levels and workforce allocation.
• Agentic AI: Utilizes AI-enabled agents to automate routine tasks and enhance decision-making.

The store agent dashboard displays comprehensive company information, including customer policies, claim statuses, and risk profiles. Users can ask questions and receive insights about specific items, with Gemini 2.0 models used for generative capabilities. Agentic AI constantly monitors data for potential fraud, risk, and churn and can automatically take actions like mitigating risks or initiating targeted campaigns for customer retention.

For customers, an AI assistant can streamline processes such as filing new claims. Customers can interact directly with the AI assistant to create new claims by sharing information like GPS location and photographs.

Conclusion

By leveraging AI-powered solutions like Reinforcement Learning and Store Assistants, retailers can streamline operations, enhance customer engagement, and improve profitability. These technologies provide real-time intelligence and automation, leading to data-driven decisions, cost optimization, and a scalable, resilient foundation for the future.

Our team of AI professionals can help address your specific challenges. As an AI solution provider, Onix has worked to reimagine and modernize the retail sector. With our retail expertise, you can capture relevant customer signals across all touchpoints and use valuable insights to improve your CX.

Onix Canopy is a dynamic, interactive platform designed to demonstrate the tangible impact of Onix’s innovative industry and solution offerings. Canopy demonstrates how Onix delivers business value to enterprises using intelligent agents, AI automation and unified knowledge graph. We can provide a 1-1 overview of AI Powered Store Assistant at your convenience, contact us for more.