LLMs are driving referral traffic – but what about engagement?
From answering complex queries to generating creative content, large language models (LLMs) like ChatGPT, Gemini, and others are becoming integral to how users seek and consume information online.
This technology presents a paradox for brands and website owners: the potential for new avenues of traffic generation alongside the growing challenge of zero-click results.
The initial excitement surrounding LLMs often focuses on their ability to simplify workflows and provide instant answers.
However, for businesses that rely on website traffic for leads, sales, and brand visibility, the implications are far more nuanced.
While LLMs can act as powerful referral engines, directing users to relevant websites, they also have the capacity to satisfy user intent directly within their interface.
This could potentially diminish the need for a website visit altogether and, thus, challenge a brand’s traditional presence online.
The promise of referral traffic: A new discovery channel
Many LLMs are designed to provide comprehensive and accurate responses.
They also cite their sources, offering users the chance to explore topics further.
This presents a significant opportunity for brands to be discovered by users who might not have found them through traditional search engine results.
Imagine a user asking an LLM for recommendations on the best sustainable coffee brands.
After processing vast amounts of information, the LLM might list several brands and provide links to their websites.
This allows the user to explore the brands’ offerings, read reviews, and make a purchase – all in one place.
This method replaces the traditional experience of clicking through several links to compare options.
Similarly, a user researching a medical condition might receive a summary from an LLM and links to reputable healthcare websites for more detailed information.
The key difference here lies in the user’s intent.
Unlike traditional search, where users actively type in keywords, LLMs respond to more natural language queries.
This enables LLMs to uncover brands and resources that might be less visible in standard search results.
This can be especially beneficial for niche businesses or those offering highly specific products.
As LLMs become more integrated into various platforms and applications, the potential for referral traffic could expand significantly.
For example, imagine an LLM embedded within a social media platform recommending products based on user conversations or a virtual assistant suggesting relevant services based on calendar entries.
These scenarios highlight the potential for LLMs to act as a new and dynamic discovery channel for brands.
Dig deeper: LLMs are disrupting search – is your brand ready?
Tracking referral traffic growth and market shifts
After analyzing the referring traffic over the past 12 months, I found steady growth in LLM-driven traffic.
Indexed against the average from the previous year (1 = average), referral traffic from LLMs has increased eightfold since March 2024.
This surge is largely due to the growing use of LLM tools and the continued inclusion of links, both as citations and direct links.
However, the data revealed an unexpected dip in December, contradicting expectations for strong holiday retail traffic growth.
Shifts in LLM market share and traffic insights
Looking at the mix of LLMs driving traffic, there are a few notable takeaways:
- ChatGPT has dominated referrals since August. Prior to that, referral traffic wasn’t smaller – it was due to different referral URLs from OpenAI and Microsoft, eventually shifting to ChatGPT (chatgpt.com vs. chat.openai.com). This migration shows the complicated relationship between OpenAI and Microsoft, which runs Bing.
- While Perplexity’s referral traffic appears smaller, it has doubled over the past year. However, ChatGPT’s growth has been much faster, and it now has a larger market share.
- Referral links from Meta are nearly nonexistent. The data shows only one month with double-digit referring traffic numbers. While Meta has significant volume due to LLaMA being integrated across its apps, there hasn’t been much referral traffic from its platforms.
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The zero-click conundrum: Satisfying intent within the LLM
While all this referring traffic is critical to understand, there’s another side to this: the rise of zero-click results.
This has been the goal of LLMs for some time and has long existed in traditional search through Google’s answer boxes, featured snippets, or Knowledge panels.
However, with LLMs, the impact is even more pronounced.
LLMs are designed to provide comprehensive answers directly within their interface.
If a user asks for the capital of France, the LLM will likely respond with “Paris” without linking to a website.
Similarly, if a user requests a summary of a recent news article, the LLM can generate a concise overview, negating the need to visit the original source.
This capability, while incredibly convenient for users, poses a significant challenge for websites that rely on traffic for revenue, lead generation, or brand building.
If the LLM can effectively answer a user’s question or fulfill their request without requiring a click, the website misses out on a potential visitor.
This is why you must develop a new approach beyond click-through traffic.
LLMs are designed to get to an answer and not take users away from the chat interface.
That makes visibility within these models even more critical in the decision-making process, even if there’s no direct attribution.
Dig deeper: Why SEO is your best defense against declining organic traffic
Navigating the new landscape: How your brand can adapt
As LLMs change how users discover content, refining your approach is key to staying competitive. Here’s how to adapt.
Focus on expertise and original research
While LLMs can synthesize information, they often rely on existing content.
When you offer unique insights, original research, and deep expertise in your niche, you’ll likely be cited as a source and potentially earn referral traffic.
Optimize for natural language queries
Understanding how users phrase questions in natural language is crucial for content creation.
Optimizing content to answer these types of queries directly could increase the likelihood of an LLM citing your website.
Build brand authority
A strong brand reputation can increase the chances of an LLM recommending your website as a trusted source.
Focus on building credibility and thought leadership within your industry.
Explore new forms of engagement
As LLMs evolve, new ways to interact with them may emerge.
This could include developing specialized content formats or leveraging APIs to provide information directly to LLMs.
Monitor LLM performance and attribution
Actively track how LLMs reference your content and try to understand the attribution of any traffic you receive from these sources.
This will help refine strategies over time.
Dig deeper: How to segment traffic from LLMs in GA4
Consider the user experience
Ultimately, the goal is to provide value to the user.
Even if a user doesn’t immediately click through to your website, the information provided by the LLM may lead them to consider your brand or seek out your products or services later.
That interaction can still be valuable.
Dig deeper: How to evolve your organic approach for the rise of answer engines
Embracing the evolution
LLMs are fundamentally changing how users find and consume information.
While referral traffic is rising, zero-click results challenge traditional website engagement.
Brands that create authoritative content, optimize for natural language queries, and monitor attribution will be best positioned to remain visible – even when clicks aren’t guaranteed.
Adapting to this shift isn’t optional – it’s essential for long-term digital success.