GrowthOS for Merchandisers
Turn complex product catalogs into dynamic, personalized recommendations across web, email, SMS, and push notifications without engineering help.
Generate tailored product recommendations
Upload your catalog and let GrowthOS do the rest. Our recommendation engine supports multiple algorithms and adapts in real time to each user's behavior and profile.
Catalog ingestion at scale
Import thousands of SKUs with full support for category, price, inventory, brand, tags, and other attributes.
Recommendation algorithm library
Choose from catalog-based, purchase-based, view-based, affinity-based, or similarity-based models - part of our 16+ algorithm library that covers a wide range of merchandising logics.
Real-time personalization
Serve dynamic recommendations across all channels, tailored to each user's browsing history, purchase patterns, and profile attributes.
Activate recommendations across channels
Deliver personalized products anywhere users interact, be it inside messages, on-site, or through triggered campaigns.
Email, SMS, and push support
Embed real-time recommendations into any message type with drag-and-drop ease.
Experience-driven placement
Drop personalized product modules directly into web and mobile experience flows.
Targeted merchandising journeys
Trigger follow-ups based on cart behavior, category views, or inventory shifts.
Test and optimize performance
Track lift from product placement and recommendation logic, experiment with layouts, and compare strategies - all from one place.
Campaign-level analytics
View conversion impact by placement, message type, or segment.
Experience testing tools
Run A/B tests on layouts, product blocks, or logic variations in your experiences.
End-to-end visibility
Connect recommendation views to downstream actions like add to cart and purchases.
Unlock advanced growth tactics
Dive into a curated directory of use cases tailored to your industry. Filter by product, industry and use case to discover advanced tactics that drive growth with GrowthOS.
Frequently Asked Questions
You can upload it directly. Intempt supports catalog ingestion at scale, so thousands of SKUs aren't a problem. It handles category, price, inventory, brand, tags, and any other attributes you need for recommendations.
There's a library of 16+ algorithms covering different merchandising logics. You can choose from catalog-based, purchase-based, view-based, affinity-based, similarity-based models, and more. Pick what fits your use case, or combine them.
Absolutely. You can embed real-time product recommendations into any message type using a simple drag-and-drop editor. Same smart personalization, just delivered wherever your users happen to be engaging.
You drop personalized product modules right into your web or mobile experience flows. It's built into the experience editor, so there's no coding involved; just place the block where you want it, and you're good to go.
Yes. You can set up targeted merchandising journeys that trigger based on cart behavior, category views, or even inventory shifts. Someone abandons a cart? Follow up with those exact products plus related items.
No. Catalog uploads, recommendation placements, messaging, testing, it's all designed so you can run it yourself. No tickets, no sprint backlogs, no waiting. You own the whole workflow.
Ready to optimize your commerce?
Start turning your product catalog into personalized recommendations that drive sales.