PERSONALIZATION &
RECOMMENDATION ENGINES

Our AI analyzes user behavior including clicks, views, purchases, time spent, and navigation patterns to build individual preference profiles. Machine learning algorithms then use these profiles to predict what products, content, or offers each user will find most relevant in real-time.

We need historical user interaction data like page views, clicks, purchases, ratings, search queries, and time spent on content. The more data available, the better, but we can start with as little as 3-6 months of history and improve recommendations as we collect more data.

Yes! For new users, we use collaborative filtering to recommend popular items and items similar to what comparable users liked. As the user interacts with your site, recommendations quickly become personalized. We can also use demographic data and real-time behavior to provide immediate personalization.

Recommendations update in real-time as users interact with your site. If a user views a product or reads an article, the AI immediately adjusts subsequent recommendations to reflect this new signal about their interests and intent.

Absolutely! Our personalization engine creates unified user profiles across web, mobile, email, and other channels. Users receive consistent, personalized experiences whether they're on their phone, tablet, or desktop, and recommendations improve with every cross-device interaction.

We track key metrics including click-through rates, conversion rates, average order value, time on site, and engagement rates. We provide detailed analytics showing performance lifts from personalization and continuously A/B test recommendation strategies to optimize results.