Rivac LabsProduct Recommendation & Personalization Engines
Generic "customers also bought" widgets leave revenue on the table because they ignore what actually drives your specific shoppers — browsing behavior, purchase history, inventory position, and margin. This service builds a recommendation engine trained on your own catalog and customer data, surfacing the products most likely to convert for each shopper at each point in their journey, from homepage to cart to post-purchase email. It's for retail and e-commerce teams who've outgrown their platform's built-in recommendations and need lift a generic plugin can't deliver. We test the engine against a control group before it touches your full traffic, so the revenue impact is proven, not assumed. The result is a measurable increase in average order value and conversion, tied to a number you can defend to finance.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Diagnose current recommendation performance and identify where generic or rule-based suggestions are underperforming.
- 2
Pilot the model against a held-out segment of traffic, A/B tested against your current recommendations.
- 3
Review lift in average order value and conversion with merchandising before rolling out to full traffic.
- 4
Launch across your storefront and email channels, with ongoing retraining as catalog and behavior shift.
What You Get
Deliverables from this engagement
- Trained recommendation model integrated into storefront and email
- A/B test results quantifying lift in AOV and conversion
- Merchandising controls for boosting or excluding specific SKUs
- Real-time personalization API for any customer touchpoint
- Ongoing retraining schedule as catalog and behavior change
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about Product Recommendation & Personalization Engines