The Real Reason A10 is a Myth

Part of the Ranking on Amazon (A9 Algorithm) Series

How Amazon’s A9 Search Engine Stacks Up Against Google and Bing

Search engines like Google, Bing, and Amazon’s A9 utilise the internet to provide relevant information. Under the hood, their technologies are more similar than different despite being made by competing companies. This article examines how Amazon’s search engine A9 compares on key functionality.

The Foundation: Indexing Webpages

  • All search engines start by crawling and indexing webpages.
  • The engines use programs called spiders or bots to scour the internet and collect copies of billions of pages.
  • The text of each page gets stored in a massive index.

When you search for something, the engine checks its index for matching webpages. Indexing more pages from more sites allows finding obscure information. Google claims over 130 trillion indexed pages while Bing reports indexing over 31 billion.

Amazon’s A9 engine indexes fewer pages as it focuses on helping shoppers find products on Amazon. But it uses the same basic indexing approach as Google and Bing. The index connects searched words to relevant pages.

Understanding Searches with AI

  • The real magic happens in understanding what people want from their searches.
  • Keywords alone are not enough context.
  • Recent AI advances help make search engines smart.

Google pioneered new neural network systems like BERT (Bidirectional Encoder Representations from Transformers). BERT models the relationships between words to understand text meaning. So it knows that “Jaguar speed” refers to fast cars rather than the animal.

Amazon quickly developed similar AI called SLM (Search Language Model) to power A9 search. Microsoft also utilises neural networks in Bing. While details differ, all now use this type of AI to grasp search contexts.

Knowledge Graphs Connect Facts

  • Search engines also rely on knowledge graphs.
  • These are huge databases linking facts about people, places, companies, etc.
  • The graphs can provide direct answers rather than just web links.

So a Bing search for “Bill Gates” surfaces a sidebar with his age, career, charity work and more thanks to collected facts. Google, Amazon, and others have their own knowledge graphs powering smart responses.

Amazon’s approach focuses more on product knowledge to aid shopping. But indexing facts provides the same help to users across major search engines.

Figuring Out Relevant Results

  • With queries understood, search engines rank results by relevance.
  • Basic keyword matching is not enough.
  • AI again helps identify the most relevant pages.

Search engines represent queries and pages with math vectors. Close vector matches indicate semantic similarity, with relevant results ranked higher. This goes beyond just word frequency statistics. The vectors capture meanings and relationships.

So neural networks allow Amazon’s A9, Google, and Microsoft’s Bing to mathematically assess result relevance. The exact algorithms differ, but learned ranking is now crucial for good search across engines.

Advances Through Healthy Competition

  • No single company monopolises search technology. In fact, they closely watch and mimic each other’s innovations.
  • When Google releases a new technique, Amazon and Microsoft teams work to incorporate similar functionalities into A9 and Bing search.

They adapt solutions like knowledge graphs and neural networks to fit their own strengths. Bing focuses on replicating Google’s features. A9 adapts techniques to emphasise Amazon shopping.

There is still plenty of room for improvement. Advances come from individuals across companies publishing new research. Good ideas spread quickly. So search engines evolve together, rather than one disappearing.

The Outlook for Amazon’s A9

Amazon’s A9 started as an alternative to Google, but now helps guide shopping searches. It indexes less than Google and Bing while utilising similar AI. But A9 continues developing its own advances like SLM models.

Amazon keeps A9 focused on product search rather than general web search. But core techniques like neural networks, knowledge graphs, and learned ranking allow A9 to compete with the broader web engines.

It seems likely that search engines will continue sharing ideas and progressing together. The complexity means no single solution dominates completely. A9 provides unique value while benefiting from and contributing to the broader search ecosystem.

The competitive marketplace incentivises collaboration rather than isolation. Amazon’s A9 relies on both internal innovation and replicating external breakthroughs to satisfy users. Collective progress enables its ongoing evolution.

5 Takeaways for Amazon Sellers

  • Optimise for A9’s product knowledge graph by ensuring listings have accurate titles, details, and attributes.
  • Focus on words shoppers use rather than filler keywords, as A9 incorporates semantic search.
  • Build your brand on Amazon through helpful content and product offerings as A9 values the customer experience.
  • Stay up to date on Amazon’s A9 announcements and new features like visual search.
  • Remember A9 aims to put relevant products ahead of SEO tricks or manipulation, so build shopper trust.


Unlocking Click Bias Secrets Seller Sessions A9
Shopping queries dataset Seller Sessions
Multi objective ranking
seller sessions the complete guide to autocomplete on amazon
understanding product photos
answering product questions A9
A10 is a myth
Man with shopping list in the supermarket
shopping online Seller Sessions A9 Algorithm
adressing the cold start Seller Sessions A9
1 2
databrill logo

Looking for a Better Agency?

Are you a 7 or 8-figure Amazon seller who is…

Databrill Logo

Looking for a Better Agency?

Are you a 7 or 8-figure Amazon seller who is…