Personalization tool
Dynamic Yield
Personalization and recommendations platform with a merchandising engine and Experience APIs for running recs and server-side experiments on any channel.
Experience delivery
E-commerce, Marketplace, Media & subscriptions
The vendor’s claim
Dynamic Yield's merchandising engine creates recommendation rules that automatically update widgets according to context, behavior, and historical data, using algorithms including deep learning, viewed together, popularity, recently viewed, and bought together.Source
Where it’s strong
- Multiple recommendation algorithms (deep learning, viewed-together, bought-together, popularity, recently viewed), not just one model
- Merchandising rules connect to CRM/segmentation data, so recs can be pinned or filtered rather than left purely algorithmic
- Experience APIs support server-side deployment on any device, not just an onsite widget
Where it runs out
- Starting price around $35,000/year per pricing research. Out of reach for smaller catalogs or teams running only a few campaigns
- G2 reviewers cite developer dependence and time-consuming integrations, especially for custom HTML/CSS/JS work
- Some reviewers note limited built-in analytics relative to dedicated BI tools
Best for
Commerce teams whose personalization is mostly recs and merchandising and who have enterprise budget and dev support.
Questions before you buy more of it
- Where is the recs holdout, and can we see lift numbers separate from merchandising rule overrides?
- How much custom dev work will implementation actually take beyond the packaged widgets?
Connected applications
Inline job complement
One missing piece, named, in the cart.
The cart is missing the thing that goes with it.
Session-first recommendations
This visit's job outranks last year's purchase.
They're ready to buy something new. You keep selling last time.
Compare it in the hub
Put up to three tools side by side, or start from the customer situation you want to personalize.
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