More revenue is already in your customer base. Go get it.
Intempt combines AI product recommendations and personalized lifecycle nudges to increase average order value without discounting your way to worse margin.
“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
Higher basket value. Without discounts.
AI recommendations and lifecycle nudges that move average order value from day one.
Show what they'll actually buy.
- Collaborative filtering trained on your full catalog and behavioral historyEvery product view, cart add, and purchase shapes the model. What gets served is based on what actually gets bought, not editorial picks.
- Real-time model updates as behavior changes during a sessionBrowse three running shoes, and the recommendation updates mid-session. Not tomorrow. Right now.
- Purchased Together, Recently Viewed, User Affinity per placementDifferent placements need different logic. The cart wants complementary products. The homepage wants recency. Intempt matches strategy to moment.
Homepage Best Sellers
48 items
PDP Similar Items
124 items
Category Trending
36 items
New Arrivals Feed
20 items
Feed preview
LiveMerino Crewneck
Canvas Tote Bag
Slim Chino
Linen Shirt
Feed name
Sorting strategy
Time range
Performance (30d)
Upsell at exactly the right second.
- Cart event trigger for in-session upsell and cross-sellThe highest-intent moment is the cart. Intempt fires personalized upsell modules the moment items land in the basket.
- Complementary product logic by category and purchase historyProducts that actually go together, based on what your customers have bought, not just catalog proximity.
- Bundle suggestions built from catalog relationshipsCreate bundles from real co-purchase data. Surface them at the moment a customer has half the bundle already in their cart.
Blu · insight12 products synced · 4 active in feeds
2 draft items excluded from live feeds. Catalog last synced 2 min ago.
| Product | Price | Status | Updated | |
|---|---|---|---|---|
 | $99 | Draft | Oct 24, 2023 | |
 | $185 | Active | Nov 02, 2023 | |
 | $245 | Active | Dec 01, 2023 | |
 | $320 | Active | Jan 10, 2024 |
The sale isn't the end.
- Purchase event trigger with recommendation-driven next messageThe moment a customer checks out, Intempt calculates the logical next product and queues the post-purchase message.
- Replenishment journeys for consumable and repeat-purchase productsConsumables surface at exactly the right replenishment interval based on each customer's purchase cadence.
- LTV-segment-aware post-purchase cadenceChampions get a different post-purchase sequence than first-time buyers. Higher-value customers get higher-value offers.
Blu · insightHomepage feed drives 34% of total recommendation revenue
PDP similar items feed CTR is declining. Try switching to collaborative filtering for this placement.
| Product | Price | CTR | Revenue | |
|---|---|---|---|---|
| $99 | 4.2% | $2.1k | |
| $185 | 6.8% | $4.9k | |
| $245 | 5.1% | $3.7k | |
| $320 | 3.9% | $5.8k |
Connect every trusted source.
Plug into the tools your team already runs on.
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Your customer data stays yours, and stays secure.
FAQ
Frequently asked questions
Results vary by catalog and traffic, but most customers see 8 to 20% AOV improvement within 60 days of enabling recommendations across on-site and email placements.
Yes. Intempt serves personalized recommendations inside lifecycle email journeys, powered by the same behavioral model as on-site.
No. Recommend is built into Intempt. One platform, one customer profile, one data model across on-site and email.
Intempt auto-syncs with Shopify or your catalog feed. Changes propagate without manual updates.
Yes. Intempt connects acquisition source to purchase behavior so you can see if email-nurtured customers spend more per order than paid social customers, and adjust your channel mix accordingly.
Your catalog has more value in it than you're capturing.
Connect your product catalog and start surfacing personalized recommendations in minutes. No data science. No engineering.