Cluster Details
Last updated
English
Last updated
The Cluster Details report (labeled as "Clusters" in the system) is a key analytical tool in Karsa Labelizer. After selecting a specific clusterization you want to analyze in the report, this report provides you with a detailed view of the performance metrics of each individual cluster contained in that clusterization.
Its main purpose is to allow you to:
Evaluate the effectiveness of product distribution: Assess how individual segments (clusters) are performing.
Identify the highest and lowest performing clusters: Quickly determine which segments generate the best results and which ones are lagging behind.
Uncover anomalies and opportunities for optimization: Find clusters with unusual metric values that may require further investigation or strategy adjustment.
Optimize bidding strategies: Based on metrics such as ROAS, CPC, and CTR, adjust bidding strategies for individual campaigns corresponding to these clusters.
The "Labelizer - List clusters" script works with the selected clusterization category (which contains individual clusters as its subcategories). For each cluster within this selected clusterization, it collects and calculates the following performance metrics for the last 30 days:
ROAS (Return on Ad Spend): Return on advertising investment (Conversion Value / Cost).
CostPerConversion: Average cost of acquiring one conversion.
CPC (Cost Per Click): Average cost per click on an advertisement.
CTR (Click-Through Rate): Click-through rate (Clicks / Impressions).
Cost: Total advertising costs for the given cluster.
Conv. value (Conversion Value): Total value of conversions generated by the cluster.
Conversions: Total number of conversions achieved by the cluster.
Number of products: How many products are currently assigned to the given cluster.
Average product price: Average price of products in the given cluster.
Impressions: Total number of impressions of product advertisements in the cluster.
Clicks: Total number of clicks on product advertisements in the cluster.
Google custom label value: The value of the custom label (e.g., yourprefix_1, yourprefix_2) that was assigned to this cluster and is exported to GMC.
The "Cluster Details" report uses several visual elements for easier and faster data interpretation:
Transposed view: Data is often presented in a transposed table, where:
Rows represent individual metrics (ROAS, CPC, CTR, etc.).
Columns represent individual clusters of your clusterization.
Cluster sorting: Columns (clusters) are typically sorted by ROAS value from highest to lowest (left to right). This allows you to immediately identify the most effective clusters.
Cell color highlighting (Heatmap):
Green, yellow, and red scale: Used for metrics such as ROAS and CTR, where higher values are usually better (green).
Inverse color scale (green, yellow, red): Used for metrics such as CPC and CostPerConversion, where lower values are better (green).
Blue shades (or other neutral scale): May be used for metrics such as Number of products, Impressions, Clicks, Conv. value, Cost, where the color indicates the magnitude of the value within the given report rather than its positive or negative impact.
Important: Color coding is applied when viewing all clusters at once and helps to quickly visually compare the relative performance of clusters with each other. For exact values, always look at the numerical data in the cells.
Comparing cluster performance: Quickly identify which clusters are achieving your ROAS goals and which need attention.
Analyzing clusterization parameters: Look at how the average values of key metrics differ between clusters. For example, if you clustered by ROAS and price, you will see here how these metrics translated into the characteristics of individual clusters.
Monitor the number of conversions and products in individual clusters. If these values approach the minimum (threshold) values set for clusterization, consider reducing the total number of clusters being created. The reason is that if some products are discontinued or due to seasonal changes, the cluster could fall below the threshold of sufficient data volume. Such a cluster would become less predictable for Google algorithms, which could negatively affect its performance. When setting up and evaluating clusterization, it is also important to take into account your expert knowledge of catalog management for the given e-commerce store – how often changes occur in the product portfolio, when seasons begin and end for key segments, etc.
Budget decisions: You can decide to allocate higher budgets to campaigns corresponding to clusters with high ROAS and sufficient conversion volume.
Adjusting bidding strategies: For clusters with low ROAS but high CTR and conversion rate (after clicking), you can consider adjusting bidding strategies or analyzing price competitiveness. For clusters with high CPC and low ROAS, you need to consider lowering bids or refining targeting.
Identifying anomalies: Look for clusters with unusual combinations of metrics (e.g., very high CTR but extremely low number of conversions), which may signal problems with landing pages or product offerings.
Monitoring product distribution: Look at Number of products and Average product price in individual clusters to understand how your products are distributed not only by performance but also by number and price.
The "Cluster Details" report is a powerful tool for in-depth analysis of your segmentation strategy. By regularly monitoring and interpreting this data, you can make informed decisions leading to gradual optimization and performance improvement of your Google Shopping campaigns.
