Recommendation Engine

The core problem recommendation engines solve is the overwhelming choice problem. In a physical store, a good salesperson watches what you browse, remembers your past purchases, and suggests items you'll actually want. Online, you have thousands or millions of products and no human guide. A recommendation engine automates this personalization at scale.

These systems work in three main ways. Collaborative filtering learns from what similar customers bought ("people who liked this also liked that"). Content-based filtering matches products to your past behavior ("you watched romance movies, so here are more"). Hybrid approaches combine both methods. The engine continuously learns as customers interact with recommendations, getting smarter over time.

In marketing tools, you see this everywhere. Netflix recommends shows. Amazon suggests products on product pages. Email marketing platforms recommend which products to feature to each subscriber. Spotify creates personalized playlists. E-commerce sites show "customers also viewed" or "recommended for you" sections. Streaming services use engines to decide what to promote on your homepage.

For CMOs evaluating AI tools, recommendation engines directly impact revenue. They increase average order value by suggesting complementary products, improve email click-through rates by personalizing content, and reduce cart abandonment by showing relevant alternatives. The quality of recommendations depends on data quality, algorithm sophistication, and how frequently the system updates. Some platforms use basic rules ("show bestsellers"), while advanced ones use machine learning that adapts to individual behavior in real time.

When selecting tools, ask: How does it handle new customers with no history? How often does it retrain? Can it incorporate business rules (don't recommend discontinued items)? Does it explain why it made a recommendation? These details determine whether the engine drives real revenue or just creates noise.