The Reverse Multiple ASINs feature works similarly to Reverse ASIN, capturing all search terms that brought an ASIN into the top 3 pages of Amazon’s search results in the past 30 days. Unlike Reverse ASIN, this feature allows you to analyze multiple ASINs at once, merge the keywords, and remove duplicates automatically.
The system will also identify whether the ASIN you entered is the top-performing variation within its parent listing (i.e., the one with the most keywords). You can then choose to analyze all variations under the listing, only the top-performing variation or just the ASIN you entered.
Export your filtered and refined keywords or add them directly to My Keyword List. You can use this for listing optimization, PPC campaign creation or market opportunity analysis.
For multi-variation products, traffic is often concentrated in just one or two variations.
Example: A makeup bag comes in 4 colors, but black generates the most sales and search visibility. Other colors benefit passively.
With Reverse Multiple ASINs, you simply enter any ASIN from a parent listing, and the system will detect whether it’s the top variation. If not, it prompts you to switch to the top-performing or all variations. This saves time and ensures you're analyzing the most relevant data.
Remove irrelevant or low-traffic keywords to build a cleaner keyword list.
Use filters such as: ABA Rank/W < 100,000 or M. Searches > 5,000
(Tip: Adjust thresholds based on your category size)
Use “Top 10 Products” to visually check keyword relevance. If the top listings don’t match your product type, it’s likely not a good keyword. Then you can click × icon to delete irrelevant ones.
High-frequency terms are words that appear frequently across traffic keywords. The number in parentheses indicates how many times the word appears — the higher the number, the more keyword phrases it’s included in, meaning the higher its weight.
By analyzing term frequency, you can quickly identify common usage scenarios for the product and categorize terms based on attributes and keyword importance.