/ portfolio /
Recommendation and decisioning systems.
Recommendation systems only matter when they improve customer experience and business outcomes. This page tracks my portfolio around product recommenders, next-best-action systems, search relevance, churn prevention, and personalisation measurement.
What this will demonstrate
Business framing
How to connect recommendations to conversion, retention, average revenue per user, lifetime value, and customer usefulness.
System design
Candidate generation, ranking, feedback loops, cold start, eligibility rules, and operational constraints.
Decisioning
Next-best-action logic across CRM, ecommerce, content, lifecycle marketing, and churn prevention.
Measurement
Offline metrics, online experiments, incrementality, guardrails, and long-term learning loops.
Planned assets
- Product recommender case study template.
- Next-best-action architecture note.
- Churn prevention as a decisioning system.
- Vertex AI Search for ecommerce: where it fits and where it does not.
Recommended next
Keep reading from this thread
From 491 indexed pages and articles.
- Insight Recommendation Systems in Production How recommendation systems become production decisioning systems through signals, ranking, constraints, feedback loops, and experimentation Related by decisioning
- Insight Insights Essays and executive notes on enterprise AI in production, recommendation systems, ecommerce AI, measurement, and AI operating models Related by notes
- Wiki concept AI Decisioning Learning-based marketing personalisation where AI agents decide which experience to deliver to each customer, optimising toward defined business goals within explicit Related by decisioning