Until just a few years ago user interest analysis was based on third-party data, static rules, and predefined classifications: systems that tracked browsing across generic categories, often outdated, offering approximative segmentations that were not responsive to real user behavior. In a context dominated by cookies and demographic models, interests were inferred more from assumed affinities than from concrete signals.
With the end of cross-site tracking and the growing importance of first-party data, interest analysis is evolving into a semantic, contextual, and personalized approach, enabled by AI-native technologies. In particular, models based on Natural Language Processing (NLP) and machine learning make it possible to analyze what users actually read, explore, or buy, and to build granular profiles based on real and dynamic interests, updated over time.
This is where the Interest model of the Bytek Prediction Platform comes in: designed to extract, classify, and activate thematic and product interests based on users’ digital behavior. The system leverages topic detection and affinity modeling algorithms to analyze the relationship between consumed content and areas of interest, assigning each user a set of semantic and commercial labels, which can be used to:
- Enrich CRM profiles and audience segments;
- Personalize editorial or promotional content;
- Optimize targeting in media channels.
The goal is not only to classify users, but to understand their intent and value-based or purchase affinities, turning interest signals into actionable insights that can be integrated across all stages of the customer journey—from discovery to re-engagement.
How the AI Interest Module Works
The AI Interest module of the Bytek Prediction Platform analyzes user behavioral data (browsing, interactions, content viewed, categories consulted) and transforms it into structured semantic signals. The analysis is carried out through a proprietary pipeline based on Natural Language Processing models, topic modeling, and supervised machine learning, applied to:
- Page texts and metadata (e.g., tags, product descriptions, CMS categories);
- Content consumed on digital properties (e.g., articles, product pages);
- User actions (e.g., clicks, scrolls, dwell time, micro-conversational engagement).
Based on this information, the system assigns interest labels to each user, updated in real time and associated with relevance and persistence scores.
Two Levels of Interest: Thematic and Product
The module’s output is structured across two distinct but complementary levels, both essential for building more relevant segmentations:
- Thematic interests: derived from the relationship between the user and macro content topics (e.g., sustainability, innovation, wellness, travel). Useful for orchestrating editorial strategies, value positioning, and branded content;
- Product interests: based on active consultation of categories, brands, SKUs, or product families (e.g., “gaming laptops,” “men’s running shoes,” “first home mortgages”). Ideal for recommendation, retargeting, promotional strategies, and bundling.
This distinction makes it possible to combine “what interests the user” with “how and why,” generating more complete profiles for omnichannel strategies.
Use Cases: Activating Interests Throughout the Customer Journey
The interest data collected and classified through the Interest model can be used across numerous operational scenarios, including:
- Content personalization: displaying content aligned with the user's thematic and/or product interests in real time on website, app, or email.
- Cookieless media targeting: exporting interest-based audiences to ADV platforms for highly relevant campaigns.
- CRM & automation: segmenting and nurturing the customer base based on evolving interests, activating automatic or one-to-one flows.
- Reporting and strategic insight: analyzing interest distribution to guide offering, positioning and editorial strategies.
- Retail media and monetization: enriching digital advertising spaces with semantic segments, enhancing inventory through profiled audiences.
Integration with Other Bytek Modules: From Interest to Predictive Activation
The true value of the Interest module emerges when it is combined with other AI models from the Bytek Prediction Platform, to build advanced, activatable, high-impact segments. For example:
- With Predictive LTV: to distinguish users interested in specific products or topics based on their potential economic value;
- With Action Prediction: to isolate users who not only have a strong interest but also a concrete propensity to act (e.g., lead, purchase, booking);
- With AI RFM Clustering: to enrich behavioral clusters with semantic insights and improve campaign relevance.
In this way, interest is not just a label but an operational signal that drives personalization, orchestration, and measurement of every marketing action.