AI-Powered Product Recommendations

Deliver real-time, AI-powered product recommendations across web, mobile, and omnichannel platforms to boost engagement, increase conversions, and grow customer loyalty.
Built on AWS, this scalable, modular solution empowers retailers to personalize every digital touchpoint using customer data and machine learning—without the need for in-house AI expertise.

Challenge

Modern consumers expect tailored, seamless shopping experiences across every digital touchpoint. Yet, many retailers struggle with fragmented data, inflexible platforms, and the inability to deploy real-time personalization at scale—leading to missed revenue and reduced customer loyalty.

Solution Overview

With AWS Gen AI solutions, retailers can accelerate personalization using services like Amazon Bedrock, Foundation Model (FM), Amazon Personalize, enabling real-time, AI-powered recommendations without requiring ML expertise. This modular, API-first approach integrates easily with existing systems, helping retailers quickly build scalable, customer-centric experiences.

The solution can be embedded across websites, mobile apps, emails, and kiosks, enabling consistent and personalized interactions across all channels.

Key Capabilities

• Real-time personalization at scale: Instantly tailor product recommendations for every shopper using AI models that learn from behavior, preferences, and context.
• Unified view of customer and product data: Break down data silos by consolidating customer interactions and product catalogs into a centralized, cloud-based foundation—enabling smarter personalization.
• Flexible integration across channels: Easily embed recommendations into websites, mobile apps, emails, and other touchpoints using APIs—without needing to overhaul your existing platforms.
• Automated, responsive experiences: Dynamically update recommendations as customers interact in real time, creating a seamless and engaging journey from first visit to checkout.
• Global reach with built-in scalability: Deliver consistent, high-performance personalization to customers anywhere in the world—even during high-traffic events like sales or holiday promotions.

Business Value

• Achieve a 20–35% boost in conversion rates through AI-driven personalization
• Higher basket sizes through AI-powered cross-sell/upsell
• Reduced churn via personalized, relevant user journeys
• Faster time-to-value with minimal infrastructure overhead
• Built-in scalability and cost optimization through pay-as-you-go architecture

Use Cases

• Product and content recommendations on web/mobile (PDP – product detail page, homepage, cart)
• Email campaigns with dynamic product personalization
• “You may also like” and “Customers also bought” modules
• Personalized search and merchandising
• Omnichannel experiences across mobile, kiosks, and contact centers

Customer Readiness Checklist 

To initiate the POC for personalized recommendations, ensure the following items are available:

• Customer interaction data (e.g., clicks, views, add-to-cart, purchases) in CSV, JSON, or database format
• Product catalog with metadata (name, description, category, availability, etc.)
• User identifiers (anonymized if needed) to map behavior to users
• Access to marketing or e-commerce tech stack (CMS, storefront API, ESP, etc.)
• Target touchpoints defined (e.g., homepage, PDP, email) for deploying recommendations
• Internal stakeholder alignment (e.g., product owner, e-commerce lead, data engineer)
• Success Criteria: Clearly state the basic technical outcome needed for the POC to be successful

Architecture, cost estimation, POC timeline

POC timeline