You're showing everyone the same products.
Intempt Recommend builds a personalized product feed for every visitor using real behavioral history, purchase patterns, and catalog signals. Not the same ten best-sellers served to everyone.
4.5on G2
Every visitor sees what they'll actually buy.
One behavioral model. Every placement. Every channel.
Recommendations trained on your customers' behavior.
Intempt reads every product view, add-to-cart, and purchase to build a behavioral model specific to your catalog and your customers. The AI recommendations update in real time as behavior changes, not once a day.
- Product view, cart, and purchase event mapping
- Real-time behavioral model updates
- Catalog ingestion via Shopify sync or API
Match the recommendation to the moment.
A homepage widget, a cart upsell, and a post-purchase email each need different logic. Intempt supports multiple recommendation strategies per placement and lets you A/B test them before committing.
- Most Popular, Recently Viewed, Purchased Together, User Affinity
- Image Similarity for visual-first catalogs
- A/B test strategies per placement before going live
Wherever your customers are.
Intempt serves recommendations wherever the moment is right: on-site widgets, inside lifecycle email journeys, in post-purchase flows, and via API. One behavioral model. Every channel.
- JavaScript embed for on-site recommendation widgets
- Personalized recommendation blocks inside Journey emails
- REST API for custom implementations
Use cases built for the metrics that matter.
Three outcomes teams measure from day one.
Connect every trusted source.
Plug into the tools your team already runs on.
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Certified & Compliant
Your customer data stays yours, and stays secure.
The teams that made the switch
“We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.”
Jim Stromberg, CEO
StockInvest
Case Study
StockInvest needed to turn anonymous traffic into registered users before any retention strategy could work. With Intempt's Experiences, they personalized the anonymous visitor flow, surfacing the right content and CTAs to boost signup conversion. Once users signed up, automated Journeys nurtured them through onboarding and deeper engagement, steadily increasing lifetime value.
FAQ
Frequently asked questions
With sufficient catalog events, views, adds-to-cart, purchases, most customers see meaningful personalization within 1 to 2 weeks.
Sources
* CUPED (Controlled-experiment Using Pre-Experiment Data) achieves 7–45% variance reduction depending on covariate selection. Deng et al. (Microsoft Research, 2013); Eppo, Statsig documentation (2024–2025).
Your catalog working smarter, starting this week.
Connect your catalog, drop a widget, and start serving personalized recommendations in minutes. No engineering required.