strategist-recommendations

What problem does it solve?

This skill helps teams choose, design, and operationalize personalized item recommendation strategies for Braze campaigns and Canvases, resolving uncertainty about when to use rules-based logic, Braze-native AI recommendations, or third-party engines and how to integrate them reliably into messaging workflows.

Core Features & Use Cases

• Strategic decision framework: compares rules-based Liquid recommendations, Braze AI Item Recommendations, and external ML services to match approach to catalog shape, signal volume, and editorial constraints.
• Integration patterns: documents Connected Content, Catalogs, and API-driven storage patterns for external recommendation outputs (e.g., Amazon Personalize, Dynamic Yield, Certona, Movable Ink, FullStory).
• Operational guidance: training timelines, fallback planning, analytics to monitor precision and coverage, and testing patterns for post-action discovery, cross-sell/up-sell, and re-engagement flows.
• Real-world example: how to wire an AI recommendation to a post-viewing in-app Canvas using a linked catalog, event mappings, Liquid templating, and a most-popular fallback when user history is insufficient.

Quick Start

Ask the strategist-recommendations skill to recommend an approach for a catalog-backed e-commerce campaign, including fallback logic, training timeline considerations, and which integration pattern to use.