PSYKHE AI offers an e-commerce personalization platform that leverages deep learning and psychological principles to improve AI-driven merchandising. The platform analyzes customer behavior and preferences to optimize product placement and recommendations, increasing sales and customer engagement.
Funding
Funding not disclosed
Founders
Product
Problem
E-commerce platforms struggle to provide truly personalized shopping experiences, often relying on generic recommendations or basic behavioral data. This results in missed sales opportunities and a less engaging experience for customers who are not shown products that align with their individual tastes and preferences.
Solution
PSYKHE AI offers an AI-powered merchandising engine that leverages psychographic intelligence to deliver per-user personalization within e-commerce environments. By analyzing customer personality traits in addition to their behavior and purchase history, the platform ensures that shoppers see the right products, in the right order, every time they visit the online store. PSYKHE AI's models adapt in real-time, personalizing the entire product category for each user and surfacing relevant adjacencies to quicken the time to discovery and purchase. This approach creates a more human-like digital experience, improving customer satisfaction and driving revenue for brands and retailers.
Target Audience
The primary target audience includes fashion retailers, home and furniture stores, and other e-commerce businesses seeking to enhance personalization and improve customer engagement.
Features
- Per-user psychographic personalization that considers individual personality traits
- Real-time adaptation of merchandising based on session context, updated every 10 seconds
- AI models built for fashion and interiors, catering to the specific needs of these retailers
- Compatibility with guest users, providing instant personalization without prior data
- Integration of visual AI, product intelligence, and psychology for comprehensive recommendations
- A/B testing methodologies to ensure continuous optimization of merchandising strategies
- Algorithms that predict matches across different product categories to augment cross- and up-selling