Show every visitor what they buy next.
Recommendations run on the same profiles and catalog as your journeys. No standalone tool, no ETL, no data sync.
Free forever. Scale with usage.
Homepage Best Sellers
PDP Similar Items
Category Trending
New Arrivals Feed
Spring Collection — Meta
3 designs · 2 variants
Google Shopping — All Products
1 design · Single variant
TikTok + Pinterest — Summer
0 designs · Single variant
Configure the recommendation feed algorithm and filters.
Feed name
What products should the customers view first?
Choose an algorithm that determines which items to recommend.
Sorting strategy
Time range
"Our most-loved picks - people can't get enough of these."
Best for: Homepage Fallback: Most Popular — Newest
What additional filters would you like to apply?
Narrow recommendations using Include, Exclude, or Pin...
4.5ON G2
The right product, in the right place, at the right time
The right algorithm for every feed
Best sellers, similar items, frequently bought together, trending, personalized ranking. Configure the right model per placement, not one model for everything.
Catalog ingests itself. Always current.
Connect your store and product data flows in automatically. Title, price, status, inventory, attributes. No manual feed. No ETL pipeline to break.
Ask Blu what's working, and why
Surface feeds driving CTR and revenue, find under-performing placements, and get cross-sell strategies grounded in your actual purchase data.
01 / 03
One feed builder for every surface.
- Algorithm per placementBest sellers, trending, similar items, frequently bought together, or personalized ranking. The right model per slot, not one global model.
- Include, exclude, and pin filtersDrop out-of-stock items, hide already-purchased products, exclude categories, or pin promotional SKUs to specific slots.
- Graceful fallback chainsSet cascading fallbacks (Most Popular → Newest) so every visitor sees a relevant feed even with sparse behavioral data.
Homepage Best Sellers
PDP Similar Items
Category Trending
New Arrivals Feed
Spring Collection — Meta
3 designs · 2 variants
Google Shopping — All Products
1 design · Single variant
TikTok + Pinterest — Summer
0 designs · Single variant
Configure the recommendation feed algorithm and filters.
Feed name
What products should the customers view first?
Choose an algorithm that determines which items to recommend.
Sorting strategy
Time range
"Our most-loved picks - people can't get enough of these."
Best for: Homepage Fallback: Most Popular — Newest
What additional filters would you like to apply?
Narrow recommendations using Include, Exclude, or Pin...
02 / 03
Catalog syncs itself. Ships in minutes.
- Auto-sync product dataTitle, price, image, status, inventory, and custom attributes ingest from your store and stay current automatically.
- Only active SKUs serveDrafts and archived items are excluded automatically. No manual pruning of stale products from feeds.
- Custom attributes for filteringTag products with margin, brand, collection, or any attribute to power downstream filters and segmentation.
Ask BluFeature adoption varies significantly across tiers
Dashboard and Reports maintain 90%+ adoption. Content Collaboration and Subscriptions features show under 40% adoption and may need onboarding improvements. Enterprise users engage with 3.2x more features than free-tier users.
| Image | Title | Price | Status | Created by | Last updated | ID | |
|---|---|---|---|---|---|---|---|
 | Adania Pant | $99 | Draft | CKCasey Kim | Oct 24, 2023 · 1:13 PM | 2369467908154 | |
 | Floral Wrap Dress | $185 | Active | SCSarah Chen | Nov 02, 2023 · 2:45 PM | 2369467940922 | |
 | Cashmere Crew Sweater | $245 | Active | ARAlex Rivera | Dec 01, 2023 · 9:30 PM | 2369467973690 | |
 | Leather Ankle Boot | $320 | Active | JLJordan Lee | Jan 10, 2024 · 7:50 PM | 2369468006458 |
03 / 03
Know which feeds earn. Fix the rest fast.
- Feed performance insightsAsk Blu which placements drive the most revenue, where CTR is decaying, and what algorithm change to try next.
- Cross-sell strategy from real dataBundle and affinity suggestions grounded in your actual purchase co-occurrence, not generic people-also-bought patterns.
- Built-in lift measurementEvery feed tracks CTR, add-to-cart, and revenue against control. A/B test algorithms with the same statistical engine as your experiments.
BluWhat are the key usage patterns across our product features? How do different user segments interact with features?
Looking at your Intempt platform, here's how different user segments typically interact with your core features:
Primary Feature Usage by Role
VP Marketing/Head of Growth (45% of usage)
• Journey Builder: 68% weekly active usage for lifecycle campaigns
• Segmentation: 78% daily usage for audience targeting
• Analytics Dashboards: 82% daily usage for performance monitoring
• Email/SMS Messaging: 71% weekly usage for campaign execution
VP Product/Product Managers (32% of usage)
• Behavioral Tracking: 85% daily usage for user activity
Ask BluFeature adoption varies significantly across tiers
Dashboard and Reports maintain 90%+ adoption. Content Collaboration and Subscriptions features show under 40% adoption and may need onboarding improvements. Enterprise users engage with 3.2x more features than free-tier users
| Image | Title | Price | Status | Created by | Last updated | |
|---|---|---|---|---|---|---|
| Adania Pant | $99 | Draft | CK | Oct 24, 2023 · 1:13 PM | |
| Floral Wrap Dress | $185 | Active | SC | Nov 02, 2023 · 2:45 PM | |
| Cashmere Crew Sweater | $245 | Active | AR | Dec 01, 2023 · 9:30 PM | |
| Leather Ankle Boot | $320 | Active | JL | Jan 10, 2024 · 7:50 PM | |
 | Silk Camisole - Blush | $110 | Active | MP | Feb 15, 2024 · 2:00 PM | |
 | Wide Leg Trouser - Navy | $175 | Draft | CK | Mar 05, 2024 · 4:30 PM |
Ask Blu anything about your recommendations
Type a question or invoke a skill. Blu picks algorithms, builds cross-sell strategies, and finds feeds that are decaying.
From catalog to served feed, in minutes
Connect store and catalog
Add your store integration in minutes. Products, prices, and inventory ingest into the same data layer that powers profiles and journeys.
PRODUCT SOURCES
Shopify
Products · prices · inventory
BigCommerce
Catalog · variants · stock
Custom CSV
Upload · auto-map fields
Catalog API
Sync via REST · webhook
Build feeds with algorithms and filters
Pick a sorting strategy, set a time range, and add include / exclude / pin rules. Each placement gets the right algorithm.
Serve, measure lift, and iterate
Render feeds on web, email, or app. Track CTR, add-to-cart, and revenue per placement, and A/B test against control.
Works with your store, your email, and your app
Catalog and behavioral data flow in automatically. No middleware, no manual feeds.
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Unlimited feeds. No per-click fees. From $24/mo
No per-recommendation fees. No traffic caps on Pro and above. One platform replaces your recommendation engine, CDP, and email feed tool.
What most teams stitch together
- Standalone recommendation engine (Nosto / Barilliance / Clerk.io), per-click or MTU pricing
- Separate CDP for behavioral profile data, manual sync, always lagging
- Separate email feed provider for dynamic email recommendations
- Engineering time to maintain catalog syncs and fallback logic
What you get with Intempt Recommend
- 40+ recommendation algorithms, per-placement, not one global model
- Catalog ingestion built in, auto-syncs from your store, no ETL
- Include/exclude/pin filters and graceful fallback chains
- Web, email, and app feeds from one configuration
- Built-in lift measurement vs. control, same statistical engine as experiments
- No per-recommendation fees, no traffic caps on Pro+
Real customers. Real results. Inside 90 days.
Faster launches, higher conversion, lower tool spend, measured in the first quarter, not the first year.
“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.
Go deeper on recommendations
Questions we actually get asked
Intempt supports collaborative filtering (users who bought X also bought Y), content-based filtering (similar product attributes), trending and popular items, frequently bought together, and personalized rankings based on individual browsing and purchase history. You can configure a different algorithm per placement or let AI auto-select the best one.
Product pages, cart pages, homepage, category pages, search results, email campaigns, and post-purchase flows. Each placement uses a different algorithm and is independently personalized based on the viewer's live profile and context.
Yes. Exclude out-of-stock items, already-purchased products, specific categories, or products below a price threshold. Pin specific products to slots or boost items by margin, inventory, or promotional priority. Set fallback chains so feeds never run empty.
Every placement tracks impressions, CTR, add-to-cart rate, and attributed revenue. A/B test different algorithms against control with built-in statistical rigor, the same engine that powers your experiments.
Trending and popular feeds serve the same day catalog connects. Personalized recommendations improve as behavioral data accumulates, typically within days of deployment depending on traffic volume.
Connect your store via the same integration layer that powers profiles and journeys. Products, prices, inventory, and attributes flow in automatically and stay in sync as your store changes. No manual product feed. No ETL pipeline to maintain.
Yes. Recommendations share the unified profile and segment library with journeys, experiments, and personalization. A high-LTV segment in journeys is the same segment in recommendation feeds, no audience sync required.
Yes. Render personalized recommendation feeds inside email campaigns using the same algorithm, segment, and filter rules you use on-site. Each recipient gets a feed personalized to their live profile at send time.
Yes. Recommendations share the experimentation engine, lift is measured against control with the same statistical rigor as your other experiments. Test algorithm vs. algorithm, or feed configuration vs. control.
Recommendations run on the same unified profile as every other Intempt product, no separate data sync, no warehouse round-trip. Behavior, purchases, and consent all live in one place. SOC 2 Type II certified, encrypted at rest and in transit.
Show every visitor what they buy next.
Connect your catalog in 10 minutes. Serve your first personalized recommendation feed by tomorrow.