📊Introduction to Analysis and Reports

Creating a clusterization design in Cluster Designer is only the first, albeit very important, step. For your new segmentation strategies to bring the expected results, it is essential to carefully analyze, evaluate, and based on the findings, potentially further optimize or select the best variant for deployment to production mode.

Karsa Labelizer provides you with a set of clear reports and analytical tools for this purpose.

Why is Analysis Key?

• Strategy verification: Analysis allows you to check whether the AI algorithms have created clusters that match your expectations and strategic goals.

• Understanding structure: You will see in detail how your products were divided, what characteristics individual clusters have, and which products they contain.

• Identification of performance differences: Reports will help you uncover strengths and weaknesses of individual clusters and the products within them.

• Selection of the best design: If you have created multiple test clusterizations with different parameters, analysis helps you objectively compare their quality (e.g., using the Consistency metric) and select the most suitable one.

• Basis for decision: Based on analytical outputs, you will decide whether the clusterization is ready for production deployment and integration with your Google Ads campaigns.

• Continuous optimization: Even after deployment, it is important to monitor reports so you can respond to performance changes and potentially adjust your strategy or product movement settings.

What You'll Find in This Section

This documentation section will guide you through the individual reports and analytical capabilities of Karsa Labelizer:

• Clusterizations Overview: Your central place for managing and comparing all created clusterization strategies. Here you will monitor their status, consistency, and basic parameters.

• Cluster Details: In-depth analysis of performance metrics for each individual cluster within the selected clusterization. It helps you understand the character and performance of each segment.

• Products in Cluster: Detailed view of individual products assigned to specific clusters, including their individual performance metrics.

• Product Movement History: A tool for monitoring and analyzing dynamic product movements between clusters, which is key to understanding long-term optimization and stability.

Analysis of data and clusterization results should be an iterative process. Don't be afraid to experiment with different settings in Cluster Designer, create test variants, and based on analytical outputs, select and refine your segmentation strategy.

Let's now take a closer look at the individual reports and what information you can gain from them.