Standard recommendation widgets often fail because they lack a true understanding of user intent. They show generic, popular items instead of products that are genuinely relevant to the individual shopper, leading to missed opportunities for up-sells and cross-sells.
The Softeem Solution
Our AI Product Recommendations engine goes beyond basic algorithms. It analyzes user behavior in real-time—clicks, add-to-carts, and viewing patterns—to understand their immediate intent and style preferences. Unlike generic solutions, our engine delivers hyper-relevant product suggestions that feel like a personal shopper, significantly increasing Average Order Value (AOV) and conversion rates.
Key Features
- “Shop the Look” Recommendations: Turn any lifestyle image into a shoppable experience by recommending complementary items to drive full-outfit purchases.
- Real-Time Intent Engine: Our engine adapts recommendations on the fly as a user browses, moving beyond static “people also bought” lists.
- Style & Affinity Matching: We go beyond product similarity to understand a user’s affinity for specific colors, styles, and brands, providing truly personal suggestions.
- Intelligent “Cold Start” Logic: Our system uses product popularity and visual similarity to provide effective recommendations even to first-time, anonymous visitors.
Case In Point: A lifestyle brand integrated our recommendations engine and saw a 25% increase in AOV and a 40% higher conversion rate from users who interacted with the recommendation widgets.
How It Works
1. Behavior Tracking Integration
We integrate a lightweight tracking script on your site to capture real-time user interactions like clicks, views, and add-to-carts.
2. Catalog Embedding
Our AI analyzes your entire product catalog, creating a 'vector embedding' for each item based on its visual and textual attributes.
3. Real-Time Matching
As a user browses, our engine matches their real-time behavior against the product catalog to find and display the most relevant items instantly.
4. A/B Testing & Optimization
We continuously test different recommendation strategies and algorithms to find the optimal model that maximizes your AOV and conversion rate.
At a Glance
Pain Points Addressed
- Low Average Order Value (AOV)
- Generic 'customers also bought' widgets are ineffective
- Difficulty showcasing the full product catalog
- Poor user engagement and high bounce rates
