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Uniform blog/The Evolution of Personalization: Uniform's Approach to Interest-Based Content Delivery
The Evolution of Personalization: Uniform's Approach to Interest-Based Content Delivery
The Evolution of Personalization: Uniform's Approach to Interest-Based Content Delivery
Insights from Alex Shyba, CTO and Co-founder of Uniform, presented at Digital Experience Assembly (DXA) 2025
Personalization has transformed from a luxury feature into a fundamental consumer expectation in the rapidly evolving digital landscape. However, traditional personalization approaches often rely on complex integrations with customer data platforms (CDPs) and require significant technical resources to implement effectively. At Digital Experience Assembly 2025 (DXA), Uniform demonstrated a fundamentally different approach to personalization that focuses on interest-based content delivery without the complexity of traditional systems.
Beyond Traditional Personalization
Traditional personalization methods typically require:
- External data platforms to collect and store user information
- Complex data integrations across multiple systems
- Technical specialists to implement personalization rules
- Significant latency due to data processing requirements
At DXA 2025, Alex Shyba showed how Uniform flips conventional wisdom by building personalization directly into the content platform. Gone are the days of bolting on separate personalization technologies. Instead, Uniform gives marketers and content creators the tools to craft tailored experiences without calling in the development team or wrestling with technical roadblocks.
Enrichments: The Building Blocks of Interest-Based Personalization
Enrichments are at the core of Uniform's personalization strategy. These are essentially intelligent tags users apply to content to indicate topics, themes, or interests represented in that content.
"Enrichments are used for behavioral personalization," explained Shyba during his DXA 2025 presentation. "Think about it as tags for your content. This allows Uniform to personalize based on interest and intent detected by consumers visiting this content."
What makes Uniform's enrichment system powerful is its ability to:
- Detect and apply enrichments automatically: Using the Scout AI agent, Uniform can analyze content and apply appropriate enrichments with weighted scores based on topic relevance.
- Track visitor interactions: As users interact with content, their interest profile develops based on the enrichments associated with the content they engage with.
- Store data locally: Unlike systems that require external data platforms, enrichment data can be stored directly in the browser, eliminating the need for external CDPs for basic personalization scenarios.
- Apply real-time personalization: The system operates without complex data transfers between systems; therefore, personalization is applicable instantly as users navigate through digital experiences.
During the DXA demonstration, Shyba showed how Scout could automatically analyze an article about coffee and assign appropriate enrichment tags with weighted scores. "Scout will match it against all of my enrichments defined in my project and set the appropriate scores based on how heavy a particular topic is being covered and discussed," he explained.
Personalization Without Programming
A key advancement in Uniform's approach is making personalization accessible to non-technical team members. The demonstration showed how content teams can:
- Implement personalization without writing code: Through Uniform's visual workspace, marketers can set up sophisticated personalization rules without developer assistance.
- Preview personalized experiences: Content authors can instantly see how their content will appear to different audience segments based on interest profiles.
- Receive AI-driven personalization suggestions: The Scout AI agent proactively identifies personalization opportunities and can automatically set up personalization rules.
In one demonstration example, Scout identified an untargeted audience segment of "coffee aficionados" and automatically recommended personalizing the hero component for this audience. Scout then created the personalized variant, updated the targeting rules, and applied the changes without requiring the user to write a single line of code or create complex audience rules.
Cross-Channel Context Sharing
Perhaps most impressively, Uniform demonstrated how this interest-based personalization system can operate seamlessly across different channels and modalities:
"Here you can see, we're blending generative UI and traditional web UI together," Shyba explained during his presentation, demonstrating how personalization context could transfer between traditional web pages and a conversational shopping assistant.
This cross-channel capability allows:
- Consistent personalization across touchpoints: The same interest profile that drives personalization on a website can inform experiences in other channels, such as conversational interfaces.
- Bi-directional context transfer: Interest signals detected in one channel (such as a chatbot) can influence personalization in another channel (such as product recommendations on a website).
- Seamless experience continuity: Users receive consistent, personalized experiences regardless of how they engage with the brand.
During the DXA demonstration, Shyba showed how a user browsing espresso machines on a website developed an interest profile that was automatically transferred to a shopping assistant chatbot. When the user later expressed interest in a different product category through the chatbot, that new interest information was synchronized back to the website, adjusting product recommendations accordingly.
Real-World Applications
The practical applications of Uniform's interest-based personalization approach include:
Personalized Product Recommendations
Uniform can deliver contextually relevant product suggestions without requiring complex product recommendation engines based on detected user interests. The demo showed how product recommendation components could adapt to show espresso machines to users who had demonstrated interest in that category.
Search Results Optimization
Beyond explicit personalization components, interest data can enhance search functionality by adjusting result ordering to match user preferences. "Even without users explicitly selecting the filters, we can boost our search using standard Uniform search capability and display more relevant products by default based on visitor profile," Shyba explained.
Smart Messaging That Makes Sense
If you have ever received out-of-season messaging, like an email about winter coats while sitting on a beach in July, know that Uniform helps brands end this misalignment. As a visitor browses content about summer destinations or beach gear, those interest signals shape what messages they'll see next, without the creepy factor of being followed around the internet.
During the demo, Shyba showed how a promotion for cold brew coffee appeared for visitors who had previously browsed coffee content, while others saw a different promotion entirely—no complex rule-building or segmentation exercises required.
Chatbots That Remember What You Like
The most frustrating thing about most chatbots is having to repeat yourself. Uniform's approach tackles this head-on by sharing interest signals across channels.
At DXA, Shyba demonstrated how a visitor browsing high-end espresso machines on the website could jump straight into a conversation with an assistant about specific models without navigating through generic options. When the same visitor later expressed interest in coffee grinders through the chat interface, that preference was instantly reflected in the website's recommendations when they returned—a level of continuity rarely seen in real-world implementations.
What This Means for Business
Uniform's approach fundamentally reshapes the economics of personalization. By moving personalization capabilities into the content platform, companies avoid the integration tax that traditional methods impose in dollars and developer hours.
The business reality is sobering: most personalization projects die slow deaths in IT backlogs. When they finally launch, they're often shells of what marketing envisioned. Uniform tackles this dysfunction at its source by putting the tools directly in marketers' hands.
Performance concerns have always forced uncomfortable tradeoffs between personalization depth and site speed. Uniform's browser-based approach eliminates this compromise by handling personalization logic locally without the constant server communication that bogs down traditional solutions.
Perhaps most importantly, Uniform transforms personalization from a major technical initiative into an ongoing marketing practice. When marketers can implement and adjust personalization rules, experimentation becomes continuous rather than episodic. This creates a virtuous cycle where insights from one experiment immediately inform the next without waiting for development cycles.
Privacy and Data Ownership
An often-overlooked advantage of Uniform's approach is its privacy-friendly nature. Because basic personalization can operate with locally stored data rather than requiring extensive user profiles in external systems, organizations can deliver personalized experiences while maintaining stronger data privacy practices.
This approach aligns well with evolving privacy regulations and growing consumer expectations around data handling, providing a path to personalization that respects user privacy while still delivering relevant experiences.
The Future of Interest-Based Personalization
As demonstrated at DXA 2025, Uniform's approach represents a significant evolution in how digital teams think about and implement personalization. By making personalization an inherent capability of the content platform rather than a separate technology layer, Uniform has removed many traditional barriers limiting personalization adoption.
The integration of AI agents like Scout further accelerates this evolution by automating much of the work previously required to implement effective personalization strategies. As these capabilities advance, we expect to see even more sophisticated personalization becoming accessible to organizations regardless of their technical resources or specialized expertise.
For digital teams struggling with the complexity of traditional personalization approaches, Uniform's interest-based model offers a compelling alternative that delivers personalized experiences without the traditional technical overhead.
Watch Alex's presentation and more here.
Uniform Recognized as a Visionary in 2025 Gartner® Magic Quadrant™ for Digital Experience Platforms
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