Note: This blog takes inspiration from a LinkedIn post from Kevin King, an industry expert and influential voice in the ecommerce community.
2023’s run toward AI adoption has been quicker than Wile E Coyote chasing down Usain Bolt and only getting faster. With the rapid acceptance of ChatGPT– 100 million users in 2 months– almost every big company has followed suit. Google created Bard, Snapchat added AI chat, Meta introduced Code Llama, and new AI tools appear daily.
Among these tech giants, Amazon, the ecommerce behemoth, is adopting AI in every part of its product development process. In this post, we’ll look at how they are using AI for their A9 search algorithm– the search engine that powers Amazon search results.
In a recent LinkedIn post from Kevin King, an 8-figure ecommerce seller and AM/PM Podcast host, he talks about how Amazon is at work on their A9 algorithm, using a unique ecommerce LLM (Large Language Model).
“It’s the A9. It evolves over time like you did during puberty. But its name doesn’t change.” - Kevin King.
Amazon unveiled these insights at the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Osaka, Japan. Kevin explains the current limitations of traditional search methods that rely only on exact word matches in product data and descriptions. This approach can overlook products when someone searches with spelling errors or synonyms.
Using LLMs, Amazon addresses this with “semantic matching,” where the LLM can understand the meaning behind words and their context and not rely solely on the keywords. Kevin uses the example, “A search for ‘sneakers’ would also show ‘running shoes.”
As it stands, here are some of the main factors influencing the A9 algorithm:
In his post, Kevin King mentions, "Amazon is working on both physical GPU-based technology and its own unique ecommerce LLM. It can process massive AI-related search analyses to serve up results in the blink of an eye."
He adds, "This could take away the need to be indexed for everything, eliminate keyword stuffing, and change how we try to optimize listings to rank today."
As Kevin describes, the entire landscape of search engines is changing. The focus in the future will be less on keyword stuffing and more on crafting a sophisticated listing focused on conversion and quality.
Now, you may be wondering how Amazon trains this Large Language Model. Let’s break down the process into four easy-to-understand steps.
If you’re scratching your head deciphering the technical jargon, here[s what that means in layperson's terms:
One of the biggest takeaways from Kevin’s post is the potential impact this change has on sellers' keyword strategies: "A single sentence in a product listing could soon match hundreds of search phrases without keywords explicitly mentioning them all."
This A9 algorithm change is potentially paradigm-shifting in how sellers approach product listing optimization. Tools heavily focused on keyword research may need to adapt to this new world of LLM-assisted search algorithms.
In the future, every seller will feel the impact of these AI-driven changes. It’s not just a trend; it’s a game-changer. Tools like PixelMe can help you speed up ad copywriting using the AI-powered headlines feature, and the external traffic and conversion optimization features are what can boost your rank.
At Carbon6, we have solutions for each of these ranking factors:
The key to staying ahead is to embrace AI and combine it with robust strategies and tools like those at Carbon6. The future of ecommerce is here, and it’s more innovative and integrated than ever.