Overview

The Catalogue Management section in Netcore Unbxd allows you to configure, validate, and monitor your product catalog directly from the console.

It ensures that your product data is correctly structured, indexed, and optimized for use across Unbxd’s Recommendation systems.

Through this section, you can:

Track feed upload and indexing status.
Map catalog attributes to Unbxd’s standardized schema fields.
Maintain consistency between your product data and recommendation logic.
Quickly identify missing or mismatched catalog attributes.

Key Capabilities

Feature	Description
Feed Status	Monitor catalog feed uploads and confirm whether product data is successfully processed and indexed by Unbxd.
Dimension Mapping	Align your catalog attributes (e.g., brand, category, price) with Unbxd’s feature fields to improve data understanding, indexing, and algorithm performance.

Field Mapping

Field Mapping defines the relationship between product attributes in your uploaded catalog and Unbxd’s schema fields. This ensures that the recommendation and search engines understand your catalog attributes correctly.

For example, if your catalog includes a field named amount3 for price, and Unbxd expects the field price, you can map price → amount3 to ensure proper recognition.

Why Mapping Matters?

Mapping ensures that:

Your product data is readable and interpretable by Unbxd’s algorithms.
Attributes like price, brand, or category are correctly used in filters, facets, and recommendations.
No product attribute becomes “unsearchable” or excluded due to naming mismatches.

Feature Field & Custom Field

Field Type	Description
Feature Fields	Predefined fields in Unbxd (e.g., price, brand, title) used for recommendations and search features. Automatically recognized by the system.
Custom Fields	Non-standard attributes unique to your business (e.g., style, fabric, collection). Can be mapped manually to be included in search and recommendation logic.

📘
Note
Only fields present in your catalog schema and feed appear in the console for mapping.

Feed Status

The Feed Status section provides visibility into the processing and indexing status of your catalog uploads. It allows you to confirm that your product data has been successfully received, processed, and indexed by Unbxd — ensuring your recommendations and search results always reflect the latest catalog updates.

Key Capabilities

Track the status of your catalog uploads in real-time.
Verify indexing outcomes such as success, partial upload, or failure.
Monitor number of products processed, indexing time, and upload type.
Detect errors early and re-upload corrected feeds quickly.

Navigate to Feed Status

Log in to the Netcore Unbxd Console.
Go to Manage > Catalog > Feed Status.

The dashboard displays all recent feed uploads and their current status.

Feed Status Table

Field	Description
Feed File Name	The name of the uploaded catalog file. A `[REINDEX]` tag indicates a full catalog reindex.
Upload Type	Specifies the feed upload mode: Reindex (complete upload) or Incremental (partial update).
Status	Displays the current feed processing stage — Processing, Indexed, Error, or Failed.

Best Practices

Always check Feed Status after every catalog update.
Use Reindex for full catalog replacements and Incremental for smaller updates.
Schedule uploads during low-traffic periods to minimize processing delays.
Monitor feed indexing times regularly to detect performance or data quality issues early.

Use Case

If you recently uploaded a product feed and want to confirm successful indexing:

Navigate to Feed Status.
Identify your most recent upload from the list.
Confirm that:

Status = Indexed
Product Count matches your expectations
Upload Type matches your intended update mode (Reindex or Incremental)

If the feed shows an Error or Failed status: Review your feed structure for missing fields or formatting errors. Re-upload the corrected file.

Dimension Mapping

Dimension mapping establishes a relationship between source dimensions like Product ID or Campaign Name in one system and target dimensions in another system or data model. This helps unify data to be aggregated, compared, or analyzed seamlessly. Refer here for the entire documentation.