This release combines data enrichment, user experience improvements, and platform-level enhancements. We continue to invest in making product data more structured and actionable, while also improving how it is accessed, presented, and maintained across the system.
Key updates include enhanced related product exports with relation types, a more informative Reviews tab, the introduction of churn monitoring and reporting, expanded MCP capabilities, UI improvements, and important infrastructure upgrades.
For additional details, please refer to the previous Icecat Release Notes.
We have enhanced the export of related products to include relation type information in both XML and JSON outputs. This update makes product relationships more explicit and usable for integrations, ecommerce platforms, and internal systems.
From now on, every related product in the export is accompanied by a relation type code that defines the nature of the relationship. Instead of using generic links between products, you can clearly distinguish whether a product is an accessory, an alternative, or serves another purpose.
This change is particularly relevant if you use Icecat data to power product pages, recommendations, or decision-support logic. The additional context allows you to structure and present related products in a way that aligns with real user expectations.
Previously, all related products were treated equally, which often required additional logic on your side to interpret their meaning. With relation types now available directly in the export, you can:
The result is more predictable behavior and faster implementation on your side.
The change is additive. A new attribute is included for each related product entry in both XML and JSON.
Example XML:
<ProductRelated ID="" Category_ID="" Preferred=""> <Product ID="" Prod_id="" ThumbPic="" Name=""> <Supplier ID="" Name=""/> </Product> <ProductRelatedLocales> <ProductRelatedLocale ID="" langid="" Preferred="" StartDate="" EndDate=""/> </ProductRelatedLocales> <RelationType Code=""/> </ProductRelated>
Example JSON:
{ "ProductRelated": [ { "ID": 123456789, "CategoryID": 1234, "Preferred": 0, "IcecatID": 987654321, "ProductCode": "", "ThumbPic": "", "ProductName": "", "Brand": "s", "BrandID": 123, "ProductRelatedLocales": [ { "ID": 0, "Language": "EN", "Preferred": 0, "StartDate": "", "EndDate": "", } ] "RelationTypeCode": "" } ] }
No existing fields were removed or modified, so current integrations will continue to function without changes.
💡Important Notes: Relation types are only included for classified relationships. Unclassified relations are still available in the export, but the relation type code is not available. The assignment of relation types follows the established logic within Icecat, ensuring consistency across all outputs.
This update is a foundational step toward more intelligent product relations. It gives you immediate control over how related products are interpreted and displayed, while also preparing the ground for more advanced capabilities in the future.
In this release, we improved the Reviews tab on the icecat.biz product page to make expert review content more accessible, structured, and valuable for both end users and integrators. The update transforms the Reviews tab from a simple viewer into a more informative and conversion-oriented component.
This change is based on the goal of better exposing Icecat’s aggregated review data and encouraging its use across ecommerce platforms and integrations
The Reviews tab is now displayed whenever a product has at least one expert review in any locale. Even if reviews are not available in the requested language, the system still provides aggregated information instead of leaving the tab empty.
The tab now includes:
Users can see a combined rating, total number of reviews, and individual review summaries with pros and cons, improving both transparency and usability.
The system introduces clearer messaging depending on user status and subscription level.
This ensures that review data is both discoverable and aligned with distribution policies
The updated Reviews tab strengthens product pages by adding credible, structured expert insights. It improves user trust and supports better purchasing decisions, while also increasing visibility of Icecat Reviews as a service for partners and integrators.
In this release, we introduced a new Weekly Users Health Report based on the previously released Churn Signals framework. This enhancement provides structured visibility into user activity, engagement, and churn risk, enabling data-driven decision-making across product, sales, and management teams.
The solution combines continuous activity tracking with automated reporting, creating a consistent and reliable view of user health across the platform.
At the core of this release is a unified approach to user activity monitoring. The system continuously evaluates user behavior and determines:
The system processes valid activity signals daily and always retains the most recent activity for each user. This ensures that all downstream metrics and reports are based on consistent, up-to-date data.
Users are now classified into a well-defined lifecycle, allowing teams to quickly understand engagement levels and risks:
The thresholds are explicitly defined, ensuring consistent interpretation across all systems and reports.
In addition to status, users are segmented by different levels, which helps prioritize actions.
This segmentation is applied consistently across channels and brand partners, allowing teams to focus on the most sensitive segments first.
Building on this data, the system now automatically generates a weekly report and delivers it to the sales and management teams.
The report provides a structured overview of user health, including:
All metrics are grouped and summarized, making trends easy to interpret at a glance.
The report includes only users who were active within the last 6 months. Users inactive beyond this period are excluded, ensuring that the analysis reflects relevant engagement.
All calculations in the report follow the same logic as the activity tracking system, guaranteeing consistency between operational data and reporting outputs.
This release provides a single, reliable source of truth for user engagement and churn risk. It enables:
Instead of reacting to churn after it happens, teams can now monitor signals proactively and take action earlier.
In this release, we expanded the MCP server’s capabilities with two new tools: Product Story and Additional Product Documents. These enhancements are designed to provide AI assistants and integrations with more complete and structured product content.
Together, these tools ensure that product-related assets are no longer fragmented across different sources, but instead delivered in a consistent and predictable way.
The new product_story capability allows AI assistants to retrieve branded storytelling content directly for a given product and locale.
When requested, the system extracts story content from the Icecat database. The response returns direct URLs to available story versions, enabling seamless integration into user-facing experiences.
This ensures that product storytelling assets, which are often critical for marketing and engagement, are now easily accessible in AI-driven flows without additional processing or custom logic.
The second enhancement introduces the product_additional_documents tool, which focuses on retrieving documents not covered by specialized tools.
The system evaluates all multimedia assets and applies a clear filtering strategy. It excludes content already handled elsewhere, such as manuals, energy labels, safety sheets, etc. Instead, it returns only the relevant “additional” documents, including other supporting materials.
Each document in the response includes:
This creates a clean and standardized structure that can be easily consumed by downstream systems.
Both capabilities are also integrated into the broader product overview response. When full product data is requested, the system includes product story and additional documents using the same logic and structure as the dedicated tools.
This guarantees consistency regardless of how the data is accessed.
These improvements address a common gap in product data delivery: incomplete or inconsistent access to supporting assets. With this release:
The result is a more complete product experience, especially in AI-driven and conversational use cases.
Additionally, this sprint includes several user experience upgrades to make both Icecat Brand Cloud and icecat.biz interactions more consistent, intuitive, and effective.
These changes focus on reducing user errors, improving usability, and strengthening product communication on the platform.
We introduced a unified multiselect component for the “User access list” field, aligning it with the interaction model already used in the Specs block.
Previously, different multiselect implementations introduced inconsistencies and increased the likelihood of user errors. With this update, the system now provides a single, standardized interaction pattern across components.
The updated component includes:
The new approach replaces the older, fragmented design with a cleaner, more scalable layout, improving usability for editors working with complex datasets.
The main goal of this change is operational efficiency. By standardizing how multiselect fields behave, users can rely on familiar interaction patterns regardless of context.
This reduces cognitive load and minimizes input errors, particularly in scenarios where large lists of users or values must be managed.
We updated the Icecat PIM banner on the homepage to better communicate the service’s value and drive user engagement.
The new banner introduces a clearer structure, an improved visual hierarchy, and a stronger call to action. It highlights the key message and encourages users to start a free trial.
User interaction is also enhanced with hover states and consistent styling aligned with the design system.
While these updates may appear incremental, they address critical usability and communication gaps. The unified multiselect improves day-to-day efficiency for internal users, while the enhanced homepage banner strengthens product positioning and user acquisition.
This sprint focused on reducing technical debt and maintaining the platform, aimed at improving system stability, performance, and operational resilience. While these changes are not directly visible in user-facing features, they address critical infrastructure and reliability aspects that support all product capabilities.
One of the key improvements was an upgrade of the caching layer to support batch queries with composite primary keys.
Previously, the cache handled only single-record lookups. Requests involving multiple records or composite keys were bypassing the cache and hitting Cassandra directly, leading to unnecessary load and performance degradation.
With this update:
This change reduces database pressure and improves response times, especially for high-volume operations.
A significant part of this sprint focused on Kafka reliability, targeting both architectural and operational visibility.
The cluster architecture has been improved by separating the responsibilities of the control plane (controllers) and the data plane (brokers). This prevents heavy data traffic from impacting cluster coordination tasks such as leader elections.
In practical terms, this results in:
Additionally, monitoring for Kafka controllers has been introduced, providing better observability and faster detection of potential issues.
An investigation was also conducted into unusually high message volumes to ensure that abnormal load patterns are identified and addressed proactively.
Several maintenance tasks were completed to improve the reliability of the infrastructure:
These changes contribute to faster delivery cycles and more predictable system behavior.
The mailing system was updated to improve security by introducing application-specific passwords for mail clients.
Users can now generate dedicated credentials with expiration policies, reducing the risk associated with using primary account passwords in external clients.
This aligns the system with modern security practices and improves overall account protection.
To support long-term stability, we invested in refactoring and expanding automated tests.
The work included:
This ensures better protection against regressions and increases confidence in future changes.
Release 248 strengthens both the functional capabilities and the technical foundation of the Icecat platform. From enabling more precise product relationships and richer content delivery to improving internal usability and system reliability, this release is designed to support better integrations, smarter user experiences, and scalable growth.
We encourage you to explore these updates and start leveraging the new capabilities in your integrations and workflows. If you have questions or would like to understand how these changes can benefit your use case, please reach out to your Icecat contact or support team.
Read further: Icecat, e-commerce, ecommerce, Icecat, MCP, product content, release notes
