Drive upsells and increase RPV with hyper-personalized product recommendations

Fashion shoppers often rely on inspiration and visual cues to complete their purchase. Constructor’s AI-powered Recommendations surface complementary items, complete-the-look suggestions, and relevant alternatives based on style affinity, size availability, and real shopper behavioral data. Recommendations also adapt in real time across product pages, carts, and post-purchase moments, helping fashion & apparel shoppers discover items that fit their taste and intent when the moment is right. This drives higher average order value, improves outfit completion, and reduces returns for retailers.

Grocery recommendations focus on speed, relevance, and habit-building. Constructor’s Recommendations learn from frequent purchases, household preferences, and seasonal behavior to suggest replenishments, substitutes, and commonly bought-together items. Shoppers see relevant suggestions, add-ons, and replacements at the right moments, even when inventory changes. The result is larger baskets, fewer abandoned trips, and stronger repeat purchasing over time.

Furniture and home retailers benefit from offering thoughtful, contextual recommendations, considering these sorts of purchases require careful consideration on behalf of the shopper. Constructor’s Recommendations highlight coordinating pieces, accessories, and alternatives based on style, price range, and shopper behavior. From suggesting matching furniture sets to surfacing complementary décor, recommendations help shoppers visualize complete spaces without overwhelming them. This increases engagement, supports higher-value purchases, and builds confidence in complex buying decisions.

With broad, multi-category assortments, effective recommendations help shoppers navigate what’s relevant next. Constructor’s Recommendations personalize suggestions across categories — from electronics to beauty to home — using real-time performance and behavioral data. The engine can also incorporate a user's location to enhance the relevance of product suggestions and tailor messaging. By surfacing the most attractive next-best products at each stage of the journey, retailers reduce friction, increase cross-category discovery, and lift conversion across the entire store.

Modern consumers expect relevant product suggestions wherever they browse. Constructor’s Recommendations leverage AI to deliver personalized content and product suggestions across product pages, category pages, carts, and post-purchase touchpoints — adapting in real time to shopper behavior and user preferences. Recommendations can not only be tailored for users based on their browsing and purchasing patterns, but also for demographic filtering and user profile building. By optimizing for outcomes like conversion and revenue (not just similarity), recommendations keep shoppers engaged, increase order value, and encourage repeat visits, strengthening long-term loyalty.

B2B recommendations must be precise, relevant, and account-aware. Constructor’s Recommendations surface compatible products, accessories, and bulk add-ons based on purchasing history, account rules, and industry logic. By suggesting the right cross-sells and replenishments at the right time, B2B companies increase order size, streamline repeat purchasing, and support efficient self-serve buying without slowing down professional buyers.

By showing customers the right product at the right time across their entire product discovery journey, you significantly enhance the user experience, leading to improved customer satisfaction and loyalty. The right product suggestions also tangibly affect business KPIs, boosting average order value and cross-selling opportunities alongside site revenue.

Our recommendations engine is powered by advanced machine learning algorithms that take into account your shoppers’ behavioral data (full, verified clickstream data), affinities to products, and content-based signals to generate attractive, relevant product suggestions. These signals feed into a continuous feedback loop, where the recommender system learns from outcomes such as clicks, add-to-cart actions, and purchases, and then adjusts what it shows next time.

Because of its underpinnings, the recommendations engine is hyper-tuned to each individual shopper and their specific wants in that exact moment. It serves up the right product at the right time for the right person, every time.

Common placements include product detail pages (complements, alternatives, bundles), cart (frequently bought together, complements), checkout (last-minute add-ons), and category/search pages (contextual pods).

For best results, adhere to a solid strategy when optimizing your recommendations strategy for the best site experience. Follow the fundamental building blocks, which include combining the product recommendations engine with UX elements such as visual cues and keywords, to achieve a business goal.

Yes. You can optimize toward the outcomes you track — such as RPV, conversion, average order value, or retention — and then use performance data to continue improving. Constructor uses real shopper behavior and reinforcement learning to continuously and dynamically test, learn, and improve performance over time.

Recommendations is part of Constructor’s integrated suite, and it shares signals across products. In practice, that means learnings from recommendations can inform search and browse rankings, and vice versa. The site experience gets more consistent as the reinforcement learning loop collects more full, verified clickstream data.

Constructor pairs each customer with a dedicated team and begins with a low-risk, data-driven Proof Schedule. Most retailers integrate in weeks, not months. We handle ingesting your product data, setting up event tracking, and tuning configurations so Recommendations performs optimally from day one.

Yes, Constructor integrates with multiple ecommerce platforms to instantly sync your product catalog and deliver on-site and off-site product recommendations at scale, boosting conversions and revenue.