Dynamic Yield (now part of Mastercard) is an experience optimization platform that personalizes web, mobile, email, and app experiences using machine learning. It combines real-time behavioral targeting and segmentation, AI product recommendation strategies, dynamic content and messaging, A/B/n and multivariate testing with automated traffic allocation, predictive personalization, experience APIs, and triggered messages for behaviors like cart abandonment. Sold as an enterprise SaaS with custom pricing tied to traffic and personalized impressions and no standard free tier, it targets retail, e-commerce, travel, media, and financial-services organizations with substantial traffic, and its results depend on solid data integration and ongoing testing and management.
Dynamic Yield is an AI-powered experience optimization platform — now part of Mastercard — that delivers individualized customer experiences across websites, mobile apps, and email. It reads user behavior in real time and automates decisioning for personalized content, product recommendations, and offers, with the aim of lifting conversion, revenue, and loyalty. Its category is best described as CRM and personalization.
Core capabilities
- Behavioral targeting and segmentation — tracks clicks, views, and purchases in real time to build dynamic segments and trigger tailored experiences.
- AI product recommendations — strategies such as “frequently bought together,” “trending,” and “personalized for you,” driven by machine learning.
- Personalized content and messaging — dynamically tailors banners, promos, headlines, and CTAs by segment or individual profile.
- A/B/n testing and optimization — A/B, multivariate, and split tests, with AI able to allocate traffic toward winning variations automatically.
- Predictive personalization — anticipates user intent to serve relevant experiences proactively.
- Experience APIs — extend personalization to custom applications and touchpoints.
- Triggered messages and analytics — behavior-based messages (e.g., cart abandonment) plus reporting on segment performance and test results.
Where it fits. The strongest use cases are e-commerce (lifting AOV and conversion with personalized recommendations and dynamic content on product and category pages), content and media (personalized article recommendations and feeds), travel and hospitality (tailored packages and location-specific offers), and financial services (relevant products and onboarding within compliance). It also handles landing-page and form personalization for lead capture, exit-intent and follow-up offers to recover abandoned carts, and loyalty through more relevant experiences.
Pricing. An enterprise SaaS platform, now part of Mastercard, with no standard free tier. Pricing is customized around traffic volume, number of personalized impressions, required features, and support level, and represents a significant investment suited to mid-sized and large businesses with substantial online traffic. Contact Dynamic Yield (or Mastercard) directly for a demo and quote.
Practical notes. This is a sophisticated platform for organizations serious about deep, AI-driven personalization at scale, and it rewards clear goals and a testing-and-iteration strategy. Proper data integration from web, apps, and other customer sources is what fuels the AI, so treat it as a prerequisite. Start with high-impact use cases — recommendations on key e-commerce pages, or tailored hero banners by segment — then monitor performance and A/B results to refine. Budget for the ongoing resources that managing and optimizing campaigns requires.
Direct link: https://www.dynamicyield.com/ (now part of Mastercard)
- Experience Personalization
- AI Recommendations
- A/B Testing
- Behavioral Messaging
- Customer Data Platform
Ask Lynx Assist
More Guides
Run disciplined SEO A/B tests in seven steps — one metric, two variations, randomized segments, run to significance, track, analyze the winner, and iterate.
Build a topic cluster in seven steps — select and score a pillar, validate it, map subtopics, align to intent, architect internal links, publish, and measure.
Prepare your site for AI search in five steps — content architecture, entity consistency, E-E-A-T, structured data, and machine-readable structure.
Get your content cited by AI in seven steps — answer capsules, link-free extraction, original data, digital PR, community presence, consistent messaging, and tracking.
A seven-step walkthrough for setting up Google Search Console on a new site — property type, DNS verification, sitemap, GA4 link, users, URL checks, and a monitoring routine.