Amazon A9 / A10: How the Search Algorithm Ranks Products in 2026

How the A9 algorithm actually works

Amazon's search engine has two jobs: first, it filters the entire catalog down to products that are relevant to a shopper's query; second, it ranks those relevant products by how likely they are to lead to a purchase. Relevance is decided mostly by text matching — your title, bullet points, backend search terms, and even your product description are scanned for the words shoppers actually type into the search bar.

Once a set of relevant products has been identified, Amazon ranks them using performance signals collected from real shopper behavior. Click-through rate on your search result, conversion rate on your product page, and sales velocity (how quickly and consistently you sell units) are the three signals that matter most. A listing that is technically relevant but converts poorly will still be pushed down the page over time.

This two-stage system means optimization is never just about keywords. A perfectly keyword-matched title with a weak main image, thin bullet points, or a high price relative to competitors will rank below a less "optimized" listing that shoppers actually click and buy. A9 rewards listings that satisfy demand, not listings that merely describe it.

A9 vs A10: what actually changed

"A10" is not an official Amazon product name — Amazon has never publicly rebranded its algorithm — but sellers and agencies use the term to describe how ranking behavior has evolved since the original A9 model. The core relevance-plus-performance logic is unchanged, but the weighting has shifted in a few observable ways.

Organic, off-Amazon traffic (from social media, Google ads, or influencer links) appears to carry more weight than it used to, rewarding brands that drive external demand rather than relying purely on internal Amazon advertising. Sales history and seller authority — consistent inventory, account health, and a track record of reviews over time — also seem to matter more than short bursts of paid-traffic-driven sales.

Practically, this means a modern optimization strategy can't stop at keyword placement. It has to combine strong on-listing relevance signals with real demand generation: driving qualified traffic from outside Amazon, maintaining consistent stock and pricing, and building a genuine review history rather than chasing one-off ranking spikes.

Keyword research: finding what shoppers actually search

Every optimization decision starts with knowing which exact phrases your target customers type into Amazon's search bar. Guessing keywords based on how you would describe your product internally is one of the most common — and costly — mistakes sellers make, because shopper language is often simpler and more literal than brand language.

• Use Amazon's own autocomplete suggestions as a free, high-signal starting point — they reflect real, frequent queries.
• Check the 'Customers also search' and related-search modules on competitor listings for adjacent terms.
• Prioritize keywords with clear purchase intent over broad, generic terms with high volume but low relevance.
• Group keywords into primary (goes in the title), secondary (bullets), and long-tail (backend terms).
• Re-check keyword performance quarterly — search behavior shifts with seasons, trends, and new competitor entrants.

The title formula that balances relevance and readability

Your title is the single most heavily weighted relevance field, but it also has to convert a browsing shopper into a click. Amazon's character limits vary by category — some allow up to 150 or even 200 characters, though mobile search results typically truncate the visible title after roughly 80 characters. That means your most important keywords and selling points need to appear first.

Rather than stuffing every keyword you found into the title, structure it so a human can scan it in under two seconds and instantly understand what the product is and why it's different.

Writing bullet points that inform and convert

Amazon gives every listing five bullet points, and each one is indexed for search relevance while also serving as the main conversion copy shoppers read before scrolling further. Treat each bullet as answering one specific question or objection a shopper might have — dimensions, materials, compatibility, care instructions, or what makes the product different from near-identical competitors.

Front-load each bullet with a short, bold-style benefit statement (even without literal bold formatting, phrasing the first few words as a clear headline helps skimming shoppers), then follow with a sentence of supporting detail. Avoid repeating the exact same keyword across all five bullets — spread relevant terms naturally so you cover more search variations without sounding robotic.

Because bullets carry real search weight, this is also where you should place secondary keywords that didn't fit in the title, along with any terms specific to how the product is used, who it's for, or where it works best.

Backend search terms: the hidden relevance field

The backend search terms field lets you add keywords that don't need to appear anywhere in your customer-facing copy but still help Amazon match your listing to relevant searches. Each line-item field is limited to 250 bytes, so every character counts — commas and other punctuation are unnecessary since Amazon parses the field as space-separated terms, and using them just wastes characters.

Never repeat a word that already appears in your title or bullets; the algorithm has already indexed it once, so repetition inside the byte limit is wasted space. Similarly, never include competitor brand names or trademarked terms — this violates Amazon's policies and can result in a listing suppression or account warning.

Use this field for synonyms, alternate spellings, common misspellings, and regional or colloquial terms your target shoppers might use but that would sound awkward in a title or bullet. It's also the right place for use-case or audience keywords that round out relevance without cluttering customer-facing copy.

Conversion signals: what happens after the click

Because A9/A10 tracks real shopper behavior after your listing appears in search, everything that affects whether a shopper clicks and then buys eventually feeds back into your ranking. Your main image, price positioning, star rating, and review count all influence click-through rate directly on the search results page, before a shopper even opens your listing.

Once on the product page, A+ Content (enhanced brand content) is worth noting: it is not indexed for search relevance, but it reliably lifts conversion rate by answering questions and building trust with richer imagery and comparison charts — and higher conversion rate feeds back into better rankings over time.

Keyword stuffing, irrelevant terms, or a title optimized purely for search rather than clarity tend to backfire: shoppers who click on a confusing or misleading listing bounce quickly, which lowers conversion rate and can push you down the page even though your relevance score looks strong on paper.