Tradly MemoryProduct recommendations
Show each customer a more relevant next step.
A behavior-based recommendation engine turns real browsing, cart, checkout, and purchase signals into ranked product suggestions — without forcing your team to build a machine-learning pipeline.
How it works
From activity to a useful recommendation
The system combines individual intent with popularity, so it is useful for both returning and first-time visitors.
Understand intent
Learn from searches, product views, repeat visits, carts, checkouts, and purchases.
Rank what matters
Match people to products and categories that fit their behavior, not only similar-item rules.
Improve over time
Use a popularity fallback for new visitors, then become more personal as signals accumulate.
One engine, many surfaces
Recommendations wherever customers already engage
Place a recommendation strip on a homepage, product page, or post-purchase screen. Use the same ranked results in lifecycle emails, an app, or an embedded commerce experience.
It is not limited to one catalogue format
The engine can work with marketplace listings, product catalogues, content-like items, and other entities that carry a stable identifier and optional metadata such as category, price, and image.
Transparent fallback behavior
When a visitor has no history, recommendations are clearly treated as popular items. Once identified activity is available, category affinity and recent intent can take priority.
What teams can build
A practical personalization layer
Recommend the next step, not just the next product
Start with the behavior your business already captures and turn it into a more useful customer experience.
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