How to Build AI-Powered Recommendations Into Your SaaS

Netflix recommends movies. Spotify recommends songs. Amazon recommends products. Recommendation systems increase engagement and reduce the effort users need to spend finding what they need. You can add a version of this to your SaaS without a data science team. Here's how.

Types of Recommendations for SaaS

• Content recommendations — "Because you read X, you might like Y" (for knowledge base SaaS, newsletter tools, course platforms)
• Feature recommendations — "Users like you also use [feature X]" (increases feature discovery and adoption)
• Action recommendations — "Based on your data, we recommend doing X" (adds intelligence to workflow SaaS)
• Similar items — "Similar products/projects/clients" (for e-commerce, project management, or CRM SaaS)

Collaborative Filtering (Simple Version)

The classic recommendation approach: "Users who did X also did Y." Implementation for SaaS: track which features each user uses. Find users with similar usage patterns. Recommend features that similar users use but the current user hasn't tried yet. This can be implemented with a few Postgres queries — no ML required.

AI-Powered Contextual Recommendations

For more sophisticated recommendations: use OpenAI embeddings to represent items (articles, products, features) as vectors. When a user interacts with an item, find the most similar items using cosine similarity in pgvector. Present the top 3–5 as "Recommended for you." This handles cold start problems better than collaborative filtering.

Next Best Action

The most valuable recommendation for B2B SaaS: telling users what to do next. Analyze user data, compare to successful user patterns, and recommend the specific action most likely to move them toward their goal. "Your close rate is 15%. Users with 25%+ close rates typically spend 20+ minutes per lead on research. Here's how to do that in [your product]."

Measuring Recommendation Quality

Track: click-through rate on recommendations, conversion rate (did the recommended action lead to a positive outcome), and comparison of churn rate between users who follow recommendations vs those who don't. This data tells you whether your recommendations are actually helping users — not just generating clicks.