🎚️Parameter Selection

Welcome to the key subsection of your Cluster Designer settings – the selection of parameters for clusterization. The right choice of metrics by which Karsa Labelizer will segment your products is crucial for creating meaningful, consistent, and high-performing clusters (future campaigns).

Why is Parameter Selection So Important?

The parameters you select directly influence how Karsa Labelizer's AI algorithm "understands" similarities and differences between your products. Based on these parameters, products are grouped into clusters.

Well-chosen parameters will help you:

• Create logical and strategically relevant clusters: Products in one cluster will share similar performance characteristics, making targeting and bid management easier.

• Maximize campaign consistency and predictability: Well-defined clusters are more readable for Google Ads algorithms and allow them to optimize performance more efficiently.

• Uncover hidden patterns in product performance: Multi-dimensional analysis can identify groups of products that would remain unrecognized when using only one parameter.

• Allocate budget more efficiently: More precise segmentation allows better directing investments to the most promising areas of your portfolio.

• Better understand your product portfolio: Analysis of the resulting clusters will provide valuable insight into how different groups of products behave in the advertising ecosystem.

What You'll Find in This Subsection

The following pages will guide you through the process of selecting optimal parameters for your clusterization:

• Parameters Overview: Detailed description of all metrics you can use for clusterization in Karsa Labelizer (e.g., ROAS, Conversion value, Clicks, Product price, etc.), including explanations of what they mean and where they come from.

• Parameter Selection Strategy: Practical guides and examples of various parameter combinations. You'll learn what types of clusters typically emerge when using certain combinations and what goals these strategies are suitable for.

• How AI Works in Cluster Finding: Conceptual explanation of how Karsa Labelizer's machine learning algorithms work with your chosen parameters to find the optimal distribution of products and the optimal number of clusters.

Remember that there is no single universally "best" combination of parameters for all e-commerce stores and situations. We recommend experimenting with different strategies in test mode and evaluating the results (especially the Consistency metric and the meaningfulness of created clusters) to find the optimal settings for your specific needs and goals.

Give parameter selection sufficient attention – it's an investment that will pay off in the form of more powerful and better manageable Google Shopping campaigns.