# Drive upsells and increase RPV with hyper-personalized product recommendations

Use full, verified clickstream data to help shoppers discover products they didn’t know they needed, all while boosting revenue and building brand loyalty — thanks to Constructor’s product recommendations engine.

## What We Do

### Deliver personalized product suggestions that convert

Finally be able to intuitively understand customer intent to deliver engaging, KPI-driven recommendations. Thanks to full, verified clickstream data and advanced machine learning algorithms, Constructor Recommendations starts delivering personalized recommendations as soon as a user clicks or searches on your website — in support of a KPI goal you set.

When placed in the right location, Recommendations can dramatically increase your average order value (AOV) and lower abandoned cart rates.

### Help shoppers discover relevant products they’ll love
- Show the right product suggestions, like alternative or complementary products, depending on where customers are in their journey
- Suggest relevant products that users are most likely to convert on based on individual user behavior data, shopper preferences, and shopping context
- Get access to merchandiser-friendly tools to see recommendation system pods, understand their performance, and optimize them
- Significantly enhance the user experience, leading to higher customer satisfaction and loyalty​

### You choose the recommendations strategy, we do the rest
- Generate accurate recommendations in strategic locations to align well with the shopper’s journey and intent (e.g., bundles for product pages, upsells for checkout, etc.)
- Power your Recommendations strategies with 9 sets of logic that collect data on user intent and behavior
- Create searchandizing rules to determine what is shown in each type of Recommendation pod

### Optimize product recommendations to meet your unique KPIs
- Use data from the product recommendations engine to gain insights into purchase behavior and user preferences, which can help in refining recommendation strategies
- Inform every discovery experience with customer data captured in Recommendations — personalizing search, browse, and more
- Increase customer retention rates and encourage repeat purchases by regularly generating recommendations that are best-fit for customers
- Boost sales and user engagement by implementing strategies like showing popular products, rating-based recommendations, personalized suggestions, and frequently bought together items

## Industry Applications

### How leading industries use an AI-powered recommendations engine to improve product suggestions
- **For Fashion & Apparel**  
  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.

- **For Grocers**  
  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.

- **For Furniture & Home Goods**  
  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.

- **For Department & General Merchandise**  
  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.

- **For B2C**  
  Constructor’s Recommendations leverage AI to deliver personalized content and product suggestions across product pages, category pages, carts, and post-purchase touchpoints. Recommendations can not only be tailored for users based on their browsing and purchasing patterns but also for demographic filtering and user profile building.

- **For B2B**  
  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.

## Frequently Asked Questions
- **What are the benefits of a personalized product recommendations engine?**  
  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.
- **How are the personalized recommendations generated?**  
  Our recommendations engine is powered by advanced machine learning algorithms that take into account your shoppers’ behavioral data, affinities to products, and content-based signals to generate attractive, relevant product suggestions.
- **Where should we place personalized recommendations to get results?**  
  Common placements include product detail pages, carts, checkout, and category/search pages.