User behavior-based content recommendations — Personalization use cases
2026-02-28 19:04 Diff

Serving personalized content recommendations based on user behavior

Who:

Multimedia publisher

Strategy:

To deliver relevant content recommendations to its visitors, the publisher uses visitors’ browsing behavior and activity to craft recommendations, which are automatically optimized to drive CTR.

Hypothesis:

Publishers must constantly work toward maintaining and driving user engagement with their content. Understanding visitor behavior is essential in doing this because it allows teams to understand a user’s unique interests, from which they can deliver highly targeted content via recommendation widgets. And using machine learning algorithms, publishers can optimize the content recommended per audience, automatically serving different groups of users the optimal piece of content and maximizing engagement opportunities.

Templates that can be used to achieve this:

Your Dynamic Yield account comes preloaded with a rich library of personalization templates, so that you can launch personalization use-cases instantly without requiring any additional design and development effort.

Discover the Template Library

What real growth looks like with personalization

  • How Valamar converts travel intent into bookings with real-time context

  • What makes Saks’ personalization engine a game-changer for driving 10% more conversions?

  • Bringing the in-store expert online: How Bergzeit reinvented gear discovery with conversational commerce

  • 32% of Total Purchases from AdaptML Recommendations

  • +88% Conversion Uplift with Mastercard Predictive Spend Insights

  • Making Personalization a Breeze for Leading Financial Institutions

  • +10.3% uplift in add-to-cart rate

  • 39% decrease in same-month cancellations

  • 16% contribution to D2C revenue from Dynamic Yield

  • 16% of revenue from recommendations