Z-score (Standard Score) is a statistical measure that indicates how far a data point deviates from the mean of a dataset, expressed in terms of standard deviations. It helps standardize values across different datasets, making them easier to compare.
When your product belongs to a niche category and the chosen competitor ASINs are relatively weak, it's common to see overall low relevance across the keyword list. In such cases, Z-scores help differentiate keywords relatively, highlighting the stronger ones even if all scores are low in absolute terms.
In the context of keyword relevance analysis, the Z-score evaluates how a keyword’s Occupancy Rate (i.e., relevance) compares to the average relevance across the entire keyword list.
Interpretation of Z-scores:
- Positive Z-score: This keyword performs above average — the higher the score, the better the relevance.
- Negative Z-score: This keyword performs below average — the lower the score, the worse the relevance.
- Z-score = 0: This keyword performs around the average level.
Why Add Z-score as an Additional Relevance Indicator?
1. Handling niche products with weak competitors
When your product belongs to a niche category and the chosen competitor ASINs are relatively weak, it's common to see overall low relevance across the keyword list. In such cases, Z-scores help differentiate keywords relatively, highlighting the stronger ones even if all scores are low in absolute terms.
2. Flexible segmentation for large keyword sets
When you're dealing with hundreds of keywords and unsure how to categorize them, Z-scores offer a dynamic and data-driven method to group keywords by performance level—beyond just labeling them as high/medium/low relevance. This makes filtering and targeting more nuanced.
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