Amazon SEO research study:

Research methodology behind the results

The following section summarizes the methodology we used to select keywords for our study, extract data from Amazon’s search engine results pages (SERPs), and measure the strength of each ranking factor.

Keyword Selection

Our keyword selection involved a combination of keyword suggestions from the Google Keyword Tool as well as manual pruning to ensure our search terms matched logical queries for Amazon.

To start, we identified the search nodes (or top level categories) that we wanted to include in the study. For the sake of ensuring our findings on prime eligibility and the seller/vendor relationship were valid, we restricted our study to only categories that supplied a physical product.

We manually selected 30 “head terms” based on the category for which we were building a keyword list. Next, we fed those 30 terms into the Google Keyword Tool to get 20 mid-to-long tail suggestions for those terms.

We would then skim the resulting list to remove informational queries (which wouldn’t reflect Amazon’s on-site search) as well as potential duplicates. In some categories, a relatively high number of duplicate keywords meant we could not meet our self-imposed quota of 20 mid-to-long tail suggestions per head term.

Though this was a painstaking process, it ensured we were working with the best possible keyword sample possible. Here is our keyword count for each category.

Appliances 543 Home & Kitchen 612
Arts, Crafts & Sewing 569 Industrial & Scientific 596
Automotive 551 Luggage & Travel Gear 578
Baby 558 Movies & TV 550
Beauty 557 Musical Instruments 580
Books 619 Office Products 598
CDs & Vinyl 566 Patio, Lawn & Garden 581
Cell Phones & Accessories 582 Pet Supplies 560
Clothing, Shoes & Jewelry 460 Sports & Outdoors 594
Collectibles & Fine Art 332 Tools & Home Improvement 588
Computers 593 Toys & Games 602
Electronics 582 Video Games 557
Grocery & Gourmet Food 571 Wine 254
Health & Personal Care 598


Once our keywords were collected, we queried Amazon’s API to collect data on search results and products. We collected the data using Amazon’s relevance rank, their default search method on the site. Each search query was categorized to its appropriate search node to bring back the correct results.

For each keyword, we brought back 50 search results. In instances where we found fewer than 50 search results, we omitted the keyword from our study.

Enforcing a search result minimum was a way to ensure that data from keywords with significantly less statistical significance were not considered in our summary of data correlations and subsequent key findings.

Statistical Analysis

Our primary method for measuring the strength of each ranking signal was the mean of Spearman correlation coefficients.

In statistics, Spearman's rank correlation coefficient is a nonparametric measure of statistical dependence between two variables.

Specifically, we looked at the Spearman correlation between each ranking factor and SERP. Then, we took the mean of every correlation.

For information on the strength of each ranking factor, check out the correlations section of this report.

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