AEO For Ecommerce: How To Drive Traffic To Your Store From AI

Kyle Risley is senior SEO lead at Shopify.

For the past 15 years or so, the ecommerce SEO playbook was pretty simple. We got very good at optimizing sites for keywords to win that valuable click from Google search results, get the user to our site, and make the sale. Then AI answer engines came along.

Traditional search engines provide a list of links relevant to a single query, whereas answer engines like ChatGPT generate a written response based on their training data and supplemental resources like organic search results. This has the potential to change how users find information and products, and we’re already seeing growth in share of traffic and orders from AI search engines. 

While it’s still early days, all of this points at a future where growth is undeniable, and the time to prepare is now. In this article, I’ll cut through the noise surrounding AI and ecommerce SEO to give you a clear overview of what’s going on and a data-driven playbook for staying ahead with answer engine optimization (AEO), also known as generative engine optimization (GEO). (Spoiler alert: Staying focused on SEO fundamentals is critical.)

How do AI search engines work?

The mechanism powering AI search engines is called query fan-out. This is how an AI search engine does its research: It takes a single user prompt or search query and fans it out into many related questions. 

It then gathers and synthesizes information relevant to each question, ultimately outputting a conversational answer. This is a type of retrieval augmented generation (RAG), which I’ll discuss shortly.

Think of these fan-out queries as the subtopics and related queries that search engines have typically answered with additional links and features (e.g. Google’s People Also Search For). AI search tools simply automate that process of topical exploration on the user’s behalf.

For example, when the AI answer engine Perplexity receives the prompt “What are good quality sheets that don’t cost too much?” it translates that into three fan-out queries:

1. High-quality sheets under $50

2. Affordable sheet sets with good reviews

3. Best budget cotton sheets

These queries can be executed against Google, Bing, and other resources. The system combines the results of these queries with the training data of the large language model (LLM) to synthesize an answer to the user’s prompt. 

This brings us to the most critical point for your SEO strategy today: If you are visible in those fan-out query results, you’re more likely to be cited in AI search results. Understanding the basics of RAG can help you increase your brand’s visibility in AI search results.

How AI understands brands

To succeed in AI search, it is important to distinguish between the two main ways tools like ChatGPT and Gemini source their answers: training data and RAG.

Training data vs. RAG 

Training data acts as the model’s baseline knowledge. This establishes the AI’s understanding of concepts and general brand reputation. If a brand appears frequently in the data the model was trained on, the AI is more likely to view it as a reputable entity and understand its associations.

For example, based on training data, a model may know that the brand On is commonly associated with running shoes, or Gymshark is well known for its fitness apparel.

RAG, by contrast, allows the model to browse the live internet. LLMs use RAG to fetch specific, real-time details that are not available in the training data. 

Common examples of this include current pricing, stock levels, and product specifications. It may also incorporate recent customer reviews or Reddit threads into its answers.

Consider our earlier example query: “What are good quality bed sheets that don’t cost too much?” An AI search engine may incorporate a product recommendation thread on Reddit and a product roundup article from Better Homes & Gardens to provide a higher-quality answer. This is the key advantage of RAG: it allows the AI search engine to incorporate fresh information in its answers, even if its training data hasn’t been updated in several months.

Securing the recommendation

An effective strategy will make your brand visible in training data and the RAG data. If a brand lacks presence in the training data, the AI may find the website via RAG but prioritize a competitor it recognizes as more popular. Conversely, strong brand recognition offers little value if the AI cannot find current prices, specs, or reviews through RAG.

Improving training data visibility requires PR and citations in authoritative publications. Optimizing for RAG involves being mentioned in the web pages returned for an AI search engine’s fan-out queries. 

Here’s how you can start to measure the progress of these efforts.

How to drive traffic to your store from AI search

1. Get a handle on your core KPIs
2. Check your AI visibility
3. Master product citability
4. Create pre-purchase tools
5. Become a primary source
6. Foster community
7. Increase mentions in trusted publications
8. Measure progress

Here are the strategies you need to put in place to get started with treating AI search as a first-class traffic channel:

1. Get a handle on your core KPIs

The absolute first step is to establish a baseline for which pages on your site are earning the most traffic from AI search, how well that traffic is converting, and what those conversions are worth.

You can do this right now with the tools you already use. 

If you’re a Shopify merchant, you can open any existing report in your Shopify Analytics dashboard and filter the results by AI answer engines. Navigate to “Filters” in the bottom right corner, select “Referrer name” or “Order referrer name,” and enter “ChatGPT.” You can add other LLMs, but ChatGPT is the most popular.

You can then compare ChatGPT to other referrers, like Google, and to your store’s overall numbers. For traffic, you can look at reports like “Sessions over time” or “Sessions by referrer,” and for sales, you can look at “Sales over time” or “Sales by referrer.”

You can do the same thing with Google Analytics, provided you have connected your ecommerce store data.

2. Check your AI visibility

Next, you need to assess your brand’s AI visibility. You can do this with prompt tracking, which is the corollary to keyword tracking in traditional SEO.

The difference is that with traditional search engines like Google, you have impression data, so it’s easier to know which keywords to track. With AI search, you’re dealing with a lot less data to understand which prompts are most popular.

Popular keyword research tools are adapting to this change by adding new tools for tracking AI visibility. For example, Ahrefs has converted its keyword database into millions of AI prompts it automatically tracks, and you can use its Brand Radar tool to see how often your brand appears in these tracked prompts.

Other tools, like Profound, allow you to provide custom prompts to track. 

There are a couple of ways to do this: You can convert your existing list of keywords into a set of prompts, or you can do some searching in an AI answer engine yourself. Right now, nobody knows exactly which prompts are representative. Even an imperfect list of prompts is a good place to start, and using your existing keyword data is a reasonable starting point.

Of course, you don’t need a paid tool to track custom prompts. You can also manually monitor the prompts most relevant to your products, simply by entering them into ChatGPT (or your preferred answer engine) on a regular cadence.

Pay attention to which sources the AI cites in answers to prompts in your niche. These are the publications you need to pitch, the affiliate partners you need to cultivate, and the communities you need to engage with to achieve AI visibility. For example, if you sell sheets, you might track the prompt “Which sheets are best for hot sleepers.”

The top sources are Reddit, Mattress Clarity, and Wirecutter. Mattress Clarity accepts free products, so you might send them your sheet set to review. Wirecutter does not accept products, but it does use affiliate links, so it’s a good idea to set up an affiliate program if you want them to link out to your site. 

For Reddit, you may want to monitor the r/Bedding community. Consider ways you can bring value to that platform, such as by hosting an AMA (ask me anything) or answering questions users may have about how sheets are made.

3. Master product citability

Next, you need to master product citability. This means your product pages must be structured for an AI model to read and understand them. All that really comes down to is making sure you’re including important details about your products and writing in simple declarative language.

You want to go beyond marketing copy and think like a database: Is every dimension, material, and use case answered in a clear, factual way? These systems love facts, and a page built on facts is a page built to be cited. Brooklinen does a great job of this.

They include compelling copy, like winning a Good Housekeeping award, but they also provide rich factual detail. They tell you how the sheets feel and look. They list the exact dimensions for every single size, and they go even deeper into the materials, the features, the certifications, and how to care for the product. This is a fact-rich product page that leaves no question unanswered.

4. Create pre-purchase tools

One concern with the shift from traditional search to AI-powered search is that AI-generated answers can result in “zero click” searches, in which a user is able to answer their question without visiting any external web pages. To get more traffic to your site, consider creating pre-purchase tools an AI search engine can’t easily replicate. 

The Behr paint visualizer is a great example. Behr’s tool provides a mid-funnel solution for someone whose next step is to buy paint. The user uploads a photo of their room and can preview paint colors. It’s value that can’t easily be delivered within a text answer and would reasonably require a click off the answer engine to the brand’s site. 

If you ask ChatGPT “How can I visualize how paint will look in my bathroom?” the answer engine will link out to Behr’s tool:

5. Become a primary source

Publishing original research is a great way to become a primary source and get cited by publications and answer engines. 

When a brand like Eight Sleep uses its own data to release a study on sleep fitness, it’s creating an asset that journalists will cite. This, in turn, signals to AI search engines that Eight Sleep is the definitive authority on the topic. This increases the likelihood that Eight Sleep will show up in AI search results—especially since Eight Sleep’s study appears on the National Institutes of Health (NIH) website. That .gov URL provides additional domain authority. 

Eight Sleep’s study shows up as a source in answer engine Perplexity’s answer to the question, “Do temperature-controlled mattresses work?” (It’s the NIH URL.)

6. Increase mentions in trusted publications

AI answer engines rely on trusted publications to act as sources for product recommendations. Getting featured by these publications can increase your chances of showing up in AI search results. One reason for this is that answer engines generate responses based on both traditional search results and their learning models. The learning models rely on both consensus and authority to determine whether a brand or product is trusted enough to be cited in a live answer.

There are a couple of ways to help your product get picked up by trusted sources.

You can run a PR campaign by identifying the publications most often cited by AI answer engines in queries related to your product. Then send a press kit to the writers and editors who cover your niche. 

Another option is to start an affiliate program. Online publications often rely on affiliate links for income, so providing that option can encourage them to feature your product in roundups. An affiliate program also incentivizes publications to link directly to your site instead of to a third-party seller like Amazon.

If you’re new to affiliate marketing, programs like Affiliatly make it easier to get started.

7. Measure progress

To summarize your action plan: First, don’t panic. Keep nailing the SEO basics, like having detailed product pages. 

Make sure you’re measuring revenue referred by AI search, not just traffic. Then, measure AI search like any other channel, tracking revenue, traffic, and conversion rate to go deeper. You can track your AI share of voice, and if you want to go the extra mile, track the rankings for your top fan-out queries using your preferred tool.

But above all else, you must build a brand that is the obvious choice in your industry.