AI recommendations
How AI turns declared preferences into relevant recommendations, including for first-time visitors with no history.
The Cold-Start Problem in eCommerce (and How AI Solves It)
The cold-start problem is when personalization fails because a new visitor has no browsing history to draw on. AI solves it by collecting declared, real-time signals such as swipes, preferences, and goals instead of past behavior, building a relevant shortlist in a few answers. Roccai's guides do this cookieless and GDPR-compliant, working from the first visit.
Product Matching: Transforming E-Commerce Success
Accurate product matching connects each shopper with the right product across data sources, and it directly lifts e-commerce conversions. Roccai does this with an AI-powered machine learning algorithm and its interactive Product Guide, turning browsing into a personalized journey: shoppers answer simple questions, declare their intent, and get matched with exactly what they need.
Embracing Hyper-Personalization: The Future of Digital Marketing
Hyper-personalization uses AI, machine learning, and real-time behavioral data to deliver experiences tailored to each individual, going beyond basic demographic segmentation. Done right, it lifts engagement, conversions, and loyalty while respecting data privacy and consent. Roccai delivers it through interactive guides that turn declared customer intent into actionable zero-party data.
The power of Multiple Swipe AI Technologies Unlocked
Swipe-based AI (Multi-Swipe AI) turns simple swipe interactions into declared preferences and actionable insight, letting brands personalize product recommendations and lift engagement. Roccai puts this to work: customers design branded swipe cards, share their intent, and receive tailored suggestions, while your team gains preference data that feeds the tools you already use.
Improving decision-making using Group Recommendations
Group recommender systems solve messy group decisions by letting each person swipe through relevant questions or images privately, then blending everyone's preferences into one recommendation the whole group feels good about. For travel, entertainment, and e-commerce brands, this surfaces zero-party data on group needs while lifting conversion and customer insight.