Personalized content recommendations are one of the most effective ways to drive engagement, increase conversions, and deliver personalized experiences across the entire customer journey. Opti-X makes it possible for marketers to act on this quickly and independently with the use of AI-powered, out-of-the-box recommendation models.
Key takeaways:
Marketers can launch AI-powered recommendations without coding or engineering support
Opti-X supports a range of models, including popular items, new products, and similar user behavior
The platform enables full control, fast deployment, and measurable impact across channels
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What Opti-X Offers
Opti-X gives marketers the tools and independence they need to generate real-time recommendations without relying on technical teams. These recommendations are powered by AI models that ingest real-time customer data and deliver personalized content across your websites, apps, and marketing channels. The platform combines sophisticated AI and human decisioning, giving marketers full control over how and where these recommendations are deployed.
Why Recommendation Models Matter
To personalize efficiently at scale, you need recommendation models to automate and optimize who gets what, and when. Without them, personalization is inconsistent, inefficient, and ultimately just guesswork. These models are essential to driving key outcomes such as:
Increased engagement and conversions through tailored content
Scalable delivery of relevant experiences across channels
Improved customer lifetime value through ongoing personalization
Recommendation models support performance-driven marketing strategies by automatically surfacing content or products most likely to resonate with each customer.
Opti-X includes a range of recommendation models to address different marketing objectives. Here are a few examples from our library of 20+ out-of-the-box recommendation models:
Popular Items: Highlights products or content that are most frequently purchased or viewed by others.
New Items: Surfaces recently launched items to encourage discovery and adoption.
Recently Viewed or Played: Reminds users of items they have previously interacted with to re-engage intent.
Similar Users: Uses behavioral similarities to suggest items based on similar customers’ responses.
Hybrid Model: Combines multiple models to deliver more balanced and optimized recommendations.
Together, these models give marketers the flexibility to tailor recommendations to any customer scenario, without relying on external teams or custom development.
In Summary: Supporting Marketing Without Silos
Opti-X is part of Optimove’s Positionless Marketing platform, built to eliminate dependencies and enable faster, smarter execution across teams. It offers marketers the freedom to operate without waiting on technical resources and puts powerfulrecommendation models directly in their hands, so they can move from insight to action without delay.
Edward is an experienced Product Marketing Manager in the B2B SaaS industry with a background in chemical engineering. He is currently driving product marketing efforts at Optimove, focusing on Go-To-Market (GTM) strategies, messaging & positioning, and competitive intelligence for the Opti-X platform. He is passionate about simplifying complex and technical concepts into engaging stories that resonate with customers. Edward holds a master’s in Chemical and Process Engineering from the University of Surrey.
