Enterprise personalisation, recommendations and experimentation engine — the platform McDonald's built drive-thru personalisation on, now part of Mastercard's services stack
Dynamic Yield is one of the most heavily-used real-time personalisation engines in global e-commerce — the system that powers product recommendations on large fashion, grocery and travel sites, and the technology McDonald's used to personalise the menu shown on its drive-thru screens worldwide. The company was founded in 2011 in Tel Aviv by Liad Agmon and Omri Mendellevich, acquired by McDonald's in March 2019 for ~$300M (one of the largest tech acquisitions ever made by a non-tech buyer), then sold to Mastercard — deal announced December 2021, closed in 2022. It now operates inside Mastercard's data and services division. For Indian product teams, Dynamic Yield is enterprise-tier and not for early-stage SaaS or consumer apps. It is the right answer when you have ~10M+ monthly visitors, real-time personalisation is a P0 problem, and your team has the engineering bandwidth to actually feed and tune a personalisation system. For most teams, Optimizely Personalization, VWO, or pairing GrowthBook with a homegrown recommender will be a more honest starting point.
Dynamic Yield is an end-to-end personalisation, recommendations and experimentation platform aimed at large e-commerce, retail, travel and quick-service-restaurant brands. It combines a customer data layer (segments, traits, real-time behaviour), a decisioning engine (which message / product / variant to show whom), an A/B and MVT testing surface, and a recommendation engine — all wired into the customer-facing website, app or in-store kiosk. The product's defining capability is sub-second decisioning at scale: serving the right personalised content during the page render, at hundreds of millions of decisions per day.
The company has had an unusually visible corporate history. Founded in 2011, it raised about $84M across multiple rounds (Bessemer, Marker LLC, Glilot Capital among others), and in March 2019 became McDonald's first tech acquisition since 1999 — McDonald's paid ~$300M to use Dynamic Yield's decisioning engine on the drive-thru menu boards in markets including the US. After three years of operating Dynamic Yield as a McDonald's subsidiary while the platform continued to serve external customers, McDonald's sold the company to Mastercard. The deal was announced 21 December 2021 and completed in 2022. Dynamic Yield now operates as part of Mastercard's Data & Services group, branded "Mastercard Dynamic Yield".
For Indian product teams, that history matters for two reasons. First, it explains the tool's positioning: Dynamic Yield is an enterprise-grade infrastructure product, sold by a financial-services giant, and its commercial motion is calibrated for procurement-heavy, multi-stakeholder buying processes — not for self-serve adoption. Second, it explains the integration depth: the McDonald's chapter forced Dynamic Yield to harden the kiosk / in-store / mobile-app SDKs that most pure-web personalisation tools never built, which is why it remains one of the few credible options for omnichannel personalisation at scale.
Sub-100ms decisioning that runs in the page-render path. Pick which experience, banner, recommendation set, or A/B variant to serve to each visitor based on real-time signals (current session, weather, inventory, location, segment).
Multiple recommendation strategies out-of-the-box (collaborative filtering, content-based, recency, complementary, frequently-bought-together) with per-strategy tuning and A/B testing. Among the most mature recommender stacks in commercial software.
Native experimentation surface — Bayesian or frequentist, with mutual-exclusion across personalisation campaigns. Less marketed than the personalisation features but used heavily by Dynamic Yield customers internally.
Built-in audience builder that combines first-party events, declared traits, and integrated CDP/CRM data. Not a full CDP replacement (use Segment or RudderStack for that) but enough for most personalisation use cases.
Web, iOS, Android, server-side, and (uniquely) in-store-display SDKs. The McDonald's-era investment in non-web channels remains a real differentiator versus web-only tools.
Post-acquisition, Mastercard is bundling Dynamic Yield with its broader data and analytics offerings (consumer-spend benchmarks, audience insights). Useful if you're already a Mastercard merchant; mostly noise if you're not.
Dynamic Yield does not publish prices. Real-world contracts in 2024–2026 typically land in the $60K–$300K+ per year range depending on traffic, channels (web vs app vs in-store), and which add-on modules (recommendations, omnichannel SDKs, CDP integrations) are bundled. For Indian buyers, that translates to roughly ₹50L–₹2.5Cr per year all-in — and the implementation services / professional-services attach typically adds 25–50% on top in year one. The page's previous "₹10,00,000+/year typical" figure (~$12K) materially understated the real cost; we've corrected this. Always validate via a quote on dynamicyield.com and budget separately for the systems integrator (most Indian deployments use Wipro, Infosys Digital, or a regional partner for implementation).
Dynamic Yield is the wrong call when you're early-stage, when web-only A/B testing would already serve you (use VWO or Convert.com instead), when you don't have a dedicated team to run it, or when the ARR uplift you can credibly claim from personalisation is below ~₹10 Cr/year — at that point the platform cost alone won't pay for itself.