AI Product Recommendations
Most stores show every visitor the same homepage carousel. We replace that with a behavior-aware engine that decides — per session, per device, per traffic source — exactly what to show next. The result is higher AOV, longer sessions, and a measurable lift on every PDP and cart page. See how we deployed this for UrbanStyle Co. — they hit +320% revenue in 60 days.
Why generic recommendations leave money on the table
Most Shopify recommendation apps run a single algorithm — usually 'frequently bought together' — and apply it everywhere. That ignores intent. A returning customer browsing accessories has different needs than a first-time visitor landing from a TikTok ad. When you treat them the same, conversion stays flat and AOV plateaus. Worse: aggressive cross-sells on the wrong page often hurt conversion instead of helping — which is why we always pair this with conversion optimization testing before scaling.
- Custom recommendation engine trained on your store's data (no off-the-shelf widget)
- Per-surface tuning: PDP carousels, cart upsells, post-purchase, exit-intent, email
- Real-time A/B testing infrastructure built in
- Cold-start handling for new products and new visitors
- Weekly tuning sessions for the first 90 days
- Performance dashboard with revenue attribution
- 01
Data audit
We connect to your store, ad platforms, and analytics. Within a week we have a map of where revenue leaks and where personalization can move the needle most.
- 02
Engine deployment
We deploy a custom recommendation model trained on your catalog, customer history, and seasonality. No iframe widgets — it ships native in your theme.
- 03
Always-on testing
Every algorithm change is split-tested. We promote winners and kill losers automatically — no quarterly review meetings required.
Does this replace our existing recommendation app?
Usually yes. Generic recommendation apps (Rebuy, LimeSpot, etc.) are a good starting point but become a bottleneck once you're past €1M ARR. We replace them with a custom model and typically see a 2–3x improvement on attributed revenue.
Do you need a minimum amount of traffic?
We recommend at least 10,000 monthly sessions for the model to learn meaningfully. Below that, we use a hybrid approach that leans more on catalog signals than behavioral signals.
What platforms do you support?
Shopify (Liquid and Hydrogen), WooCommerce, BigCommerce, and custom Next.js/Remix storefronts.
Ready to scale in your sleep?
Book a free 30-minute strategy call. No pitch, just an honest look at what's holding your store back.
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