AI product recommendations

Personalized product recommendations that lift revenue

Generic "you may also like" widgets underperform because they ignore who is actually looking. AI recommendations use behavior, purchase history and product relationships to surface what each customer is most likely to buy, lifting conversion and average order value. Starcodia implements recommendations on Shopify as a revenue lever, chosen, placed and measured, not just switched on.

Types of AI recommendation

Personalized. Products matched to individual behavior and preferences.

Cross-sell. Complementary items shown with the product or in cart.

Upsell. Higher-value alternatives or upgrades during browsing and checkout.

Similarity with affinity. Items like those browsed, weighted by predicted interest.

Post-purchase. Reorder and expansion suggestions after the sale.

Engine selection and placement

Two decisions drive results: which engine, and where recommendations appear. We evaluate engines such as Rebuy, Nosto and LimeSpot against your goals, Rebuy for merchandising control and cart/checkout upsells, Nosto for journey-wide personalization, LimeSpot for fast turnkey placements, and integrate the choice cleanly with your theme and checkout. Then we place recommendations where they convert: product page, cart and checkout, personalized homepage for returning visitors, and post-purchase, designed to help rather than clutter and built not to drag down page performance.

Data quality, cold start and merchandising rules

Recommendations are only as good as the data and the guardrails. We make sure product data (attributes, categories, relationships) is clean enough for the model to reason over, handle cold start for new products and first-time visitors with content-based similarity and rules, and layer merchandising control on top, boosting margin or new-season stock, excluding clearance where inappropriate, pinning strategic items, so the output serves both the algorithm and your commercial priorities.

Measuring incremental revenue

We set a baseline before launch and track recommendation-influenced revenue and attach rate, and where traffic allows, run a holdout so you see the revenue recommendations genuinely added. That keeps the feature accountable rather than taking credit for sales that would have happened anyway. This fits within Starcodia's wider CRO and performance work.

Add AI recommendations

Tell us your platform, catalog size and current AOV, and we will recommend an engine, placement plan and measurement approach.

Discuss recommendations

Frequently Asked Questions

There is no single best; it depends on your goals and stack. Rebuy is strong for merchandising control and cart/checkout upsells, Nosto for behavioral personalization across the journey, and LimeSpot for turnkey placements. We select based on catalog size, whether you need rules-based merchandising, and how it integrates with your theme and checkout, rather than defaulting to one vendor.
Placement matters as much as the algorithm. High-impact positions are the product page (complementary and similar items), the cart and checkout (relevant upsells and cross-sells), the homepage (personalized for returning visitors), and post-purchase (reorder and expansion). We place and design them to add value, not clutter, and to load without slowing the page.
Pure behavioral models struggle with new products and first-time visitors because there is no history. We combine AI with merchandising rules and content-based similarity (attributes, category, price band) so recommendations are sensible from day one and improve as behavioral data accumulates.
By measuring incrementality, not just attributed clicks. We track recommendation-influenced revenue and attach rate, and where volume allows, hold out a control group so you see revenue the recommendations genuinely added rather than sales that would have happened anyway.
Yes, and you should. We layer business rules over the AI, boosting margin or new-season items, excluding out-of-stock or clearance where inappropriate, and pinning strategic products, so recommendations serve the algorithm and your commercial priorities together.