Does LLM Traffic Convert Better Than Organic? A New Data-Backed Study

With the rise of large language models (LLMs) and AI chatbots, there have been a lot of claims about how these platforms accelerate the customer journey and essentially replace traditional search engines. Many people in the industry suggest that traffic from LLM referrals is more qualified and converts at a higher rate than traditional Search. I have mostly aligned with this thinking, but I wanted to test this and conduct an in-depth analysis using our first-party data to see if it would support the idea.

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Research Question: Testing the Conversion Rate of LLM Traffic vs. Organic Search

There are many ways that we could analyze this and various different metrics to look at, but I tried to narrow it down to one question that would give us the biggest insight and comparison:

Does LLM traffic have a higher conversion rate?

By identifying whether LLM traffic converts at a higher rate systematically, I believe this would help us determine the long-term impact and contributions we can expect. The goal is to provide an evidence-based analysis of conversion performance, moving beyond assumptions to measure how LLM traffic actually compares to organic.

Methodology & Data Challenges

The research began with a broad sample of websites across multiple industries. However, not every site made sense to include for a meaningful comparison. To ensure consistency and reliable results, strict criteria were applied when defining the final dataset. The goal was to focus only on sites with measurable, business-driven conversions.

Selection Criteria

1. Macro Conversions

1. Only websites with measurable, business-driven conversions were included.
2. For B2B sites, this included demo requests or form submissions.
3. For e-commerce sites, the equivalent was purchases.
4. Sites without clear macro conversions (e.g., publishers focused only on engagement metrics) were excluded, as they do not represent a direct business outcome.

2. Conversion Tracking Validation

1. Event and conversion tracking had to be accurate and functional.
2. Each site’s conversion events were manually audited and validated to confirm setup integrity and reporting accuracy.

Final Sample Size

After applying these criteria, the sample was refined to 54 websites that met the defined requirements. All metrics were collected from GA4, covering the most recent six months of data for each site.

Conversion rates were calculated on a session basis rather than a user basis to keep results consistent across both B2B and B2C sites. Session-based measurement is the industry standard and avoids skewed results for e-commerce sites where multiple conversions per user are common.

Challenges

Even after narrowing the dataset, several challenges remained. Conversion events varied widely across sites, making direct comparisons more complex than simply lining up numbers. Obviously, event names are different for each website, which makes it difficult to do this at scale.

This meant going through each site one by one to not only validate the events but also identify and map each event name to a comparable business conversion.

This manual effort was time-intensive but necessary to ensure the analysis was based on accurate and comparable measures of conversion.

Statistical Methods

To ensure the analysis was rigorous and not based on simple averages alone, several statistical methods were applied. The focus was on tests that confirmed whether observed differences were consistent and meaningful across the dataset:

• Site-level differences between organic and LLM conversion rates
• Averages vs. medians to account for skewed distributions
• Variability measured through standard deviation (SD) and interquartile range (IQR)
• Paired t-tests for organic vs. LLM comparisons within sites
• Welch’s test for group comparisons (e.g., B2B vs. B2C)

These methods provided multiple checkpoints for reliability and set the foundation for the results presented in the following sections.

Results

When looking at averages across all sites, organic traffic converted at 4.60% while LLM referrals converted at 4.87%. At first glance, this suggested a modest advantage for LLM traffic.

However, averages alone can be misleading. To validate whether the difference was consistent across sites, we calculated the mean site-level difference (LLM – Organic), which came out to +0.27 percentage points (pp). The median difference was even smaller at +0.09 pp, with variability measured at 7.53% (SD) and 1.78% (IQR). A paired t-test confirmed that the difference was not statistically significant (p = 0.794).

Table 1. Conversion Rates: Organic vs. LLM

These results show that LLM traffic did not convert significantly differently from Organic. Both channels converted at a similar rate, and the apparent uplift in the averages was not consistent enough across sites to be considered meaningful. At a global level, this suggests that LLM referrals are not yet behaving differently from organic search traffic in terms of conversion efficiency.

About the statistical test

The paired t-test compared each site’s organic vs. LLM conversion rates to see if the difference held consistently across the dataset. A p-value of 0.794 means the variation we see is likely due to random chance (outliers) rather than a systematic difference between the two channels. For context, a p-value below 0.05 is generally required for significance. In other words, the modest average uplift was not supported once site-level variability was accounted for.

Does LLM Traffic Consistently Outperform Site Averages?

To assess whether LLM traffic consistently converted more efficiently than site averages, we analyzed site-level differences. The scatterplot in Figure 1 compares each site’s share of LLM sessions (x-axis) against its conversion rate ratio (y-axis).

Conversion Rate Ratio = LLM CVR ÷ Overall Site CVR

How to read the scatterplot (y-axis ratio):

• If it’s =1 → LLM converts at the same rate as the site average.
• If it’s >1 → LLM converts better than average.
• If it’s <1 → LLM converts worse than average.

Figure 1. LLM Share of Sessions vs. Conversion Rate Ratio

The distribution shows no clear directional trend. Sites are scattered both above and below the parity line (y = 1.0), with no consistent dominance in either direction. Some sites saw LLM traffic converting more efficiently than their averages, while others saw lower efficiency.

To quantify this distribution, we also measured how many sites fell into each category.

Figure 2. LLM Conversion Efficiency Breakdown

Breaking this down further, 56% of sites saw LLM traffic convert at higher rates than their site average, 41% performed worse, and 4% were the same. This near-even split reinforces the earlier finding that LLM traffic is not delivering a consistent conversion lift across sites.

Sensitivity Analysis: What High-Traffic Sites Reveal About LLM Conversion Lifts

To test the robustness of the findings, we applied thresholds to filter out sites with very low traffic or too few LLM conversions. The goal was to make sure results were not being skewed by sites with insufficient data volume.

Thresholds applied:

• ≥100,000 total sessions
• ≥50 LLM sessions
• ≥5 LLM conversions

Applying these criteria reduced the dataset from 54 sites to 33 sites, using the same six-month data window.

Table 2. Sensitivity Analysis: Full Sample vs. Thresholded Sample

While the average difference widened under the thresholded sample (+1.24 pp vs. +0.27 pp), the results still failed to reach statistical significance (p = 0.376). This confirms that even when focusing on higher-volume sites with more LLM traffic and conversions, LLM traffic did not consistently outperform organic traffic in terms of conversion efficiency.

Business Model Segmentation: How LLM Traffic Performs Compared to Organic Traffic

We also segmented the data by business model to see if results differed between B2B and B2C websites.

Figure 3. Conversion Rates by Business Model

In the full dataset, LLM traffic converted slightly higher than organic for B2B sites (2.17% vs. 1.16%) and slightly lower for B2C sites (6.58% vs. 6.78%). At face value, the two models leaned in different directions, but the pattern showed no consistent advantage for LLM traffic.

To ensure the comparison was meaningful, we applied the same thresholds used earlier (≥100,000 sessions, ≥50 LLM sessions, ≥5 LLM conversions). This reduced the sample to 33 sites, split between B2B and B2C.

Table 3. Thresholded Results by Business Model

The thresholded results showed that both B2B and B2C sites leaned slightly positive for LLM traffic, but neither difference was statistically significant (p = 0.705 for B2B, p = 0.423 for B2C). We also ran Welch’s test to evaluate whether the gap between B2B and B2C sites was significant. This tested whether LLM traffic had a stronger impact on one business model compared to the other. The result (p = 0.546) confirmed that no such difference was present.

Overall, it appears that the business model does not meaningfully change how LLM traffic converts relative to organic. Both B2B and B2C show small positive differences, but the variability across sites is too high to draw a conclusive pattern.

Traffic Share Context: Why LLM Traffic Still Makes Up Less Than 1% of Overall Site Traffic

LLM Share of Sessions

To understand the scale of LLM traffic within overall site performance, we first measured its share of total sessions across all sites. As shown in Figure 4, the vast majority of sites had less than 1% of sessions from LLM traffic, highlighting how limited this channel remains overall.

Figure 4. LLM Share of Sessions

While a handful of sites registered higher shares, the percentage breakdown highlights just how minimal the contribution typically is:

• Nearly 90% of sites had LLM traffic contribute less than 0.6% of their total site traffic.
• And only about 10% of sites had LLM traffic contribute more than 0.6% of the total site traffic

Average Channel Contribution: Organic vs. LLM

In addition to the graph above to compare contributions more directly, we looked at the average share of sessions and conversions by channel across all sites. Figure 5 (below) shows the sharp contrast.

Figure 5. Average Channel Contribution (Organic vs. LLM)

• Organic traffic accounted for roughly one-third of total sessions (31.9%) and conversions (33.8%).
• LLM traffic contributed less than 1% on average (0.24% of sessions, 0.42% of conversions).

Paired t-tests confirmed that these differences were statistically significant (p < 0.001). This indicates that while conversion efficiency may appear similar between organic and LLM, the scale of LLM traffic is negligible in comparison to organic.

Exploratory Segmentation by Industry

We also segmented the data by industry vertical to see if any broad patterns emerged. Figure 6 shows that while conversion rates varied widely across industries, no consistent channel-level advantage appeared.

Figure 6. Conversion Rates by Industry Vertical

Looking at individual industries:

• LLM traffic converted higher in some verticals such as Financial Services and Travel & Tourism.
• Organic traffic converted higher in verticals like eCommerce and Consumer Services.

These exploratory results provide directional insights, but given the small sample sizes within each vertical, they should be interpreted with caution.

Interpreting the Data

The evidence from this study shows that LLM traffic is not delivering a measurable conversion advantage over organic at this stage. The burden of proof was on LLM traffic to demonstrate that it converts at a higher rate than organic, and this dataset did not support that claim. While averages suggested a slight uplift, statistical testing confirmed the difference was not significant.

What this tells us is that, at least in this dataset, LLM traffic is converting about the same as organic and site averages. The scatterplot and efficiency breakdown made that even clearer. Some sites leaned higher with LLM, others lower, the split was essentially even. That kind of mix shows there is not a consistent advantage here.

When we filtered out low-volume sites, the gap between LLM and organic widened a little, but still not enough to matter statistically. Although there are cases where LLM traffic looks good, the overall trend across sites is not strong enough to draw a confident conclusion.

The segmentation checks told the same story. B2B and B2C both leaned slightly positive for LLM, but the variation was too high to call it meaningful. We also ran Welch’s test to check if the gap between B2B and B2C was significant, since the group sizes were different. The result confirmed that there was no measurable difference. The industry splits were mixed as well, and small sample sizes made it hard to read much into them.

The biggest factor is scale. Organic traffic consistently drove about one-third of total sessions and conversions across nearly all sites and industries. LLM traffic, by comparison, was under one percent. Even in cases where efficiency looked slightly higher, the overall impact remained minimal relative to organic.

Limitations and Considerations When Comparing LLM and Organic Conversions

This analysis measures macro conversions at the point of form submissions (for B2B sites) or purchases (for e-commerce sites). While purchases represent true customers, form fills and demo requests do not necessarily translate into paying clients. As such, this research does not account for lead-to-customer conversion rates.

It is also important to note that conversions in the buyer journey are rarely linear. Users often engage across multiple touchpoints before becoming a customer. For the purposes of this analysis, conversions are treated using last-touch attribution.

To better understand conversion quality in practice, I typically recommend that organizations implement self-reported attribution within lead forms (for example, including an open-ended question such as “How did you hear about us?”). This type of data provides additional context for evaluating traffic quality and channel impact.

Conclusion: Organic Search Still Leads as LLMs Popularity Increases

My conclusion is that the customer journey is becoming more complex and continues to evolve. It would be a mistake to frame any single channel as the best or the silver bullet for qualified traffic.

In recent studies it’s becoming more evident that users are using both traditional search and LLMs throughout the customer journey.

A recent Invoca report provides some useful context:

• 46% of buyers rely exclusively on traditional search for complex purchase decisions.
• 44% use both AI and traditional search, though most lean more on search.
• 2% only depend primarily on AI tools.

Brands shouldn’t ignore optimizing for LLMs and AI search. Quite the opposite, this channel represents an emerging part of the search landscape and should be strategically incorporated into a comprehensive search strategy in alignment with business goals.

Organic search continues to dominate across industries, both in traffic share and conversion share. This was the most consistent and indisputable takeaway from the data. For most websites in the study, LLM traffic remains minimal; but, a small number of sites did receive a more meaningful share of sessions and conversions from LLMs. In such cases, prioritizing LLM optimization becomes an even more strategic initiative.

Businesses should begin tracking and monitoring LLM traffic monthly, with attention to:

• Growth trends over time (e.g., how share of traffic has shifted in the past year).
• Which types of pages are being surfaced by LLMs.
• How conversion performance compares to organic search.

For sites receiving only marginal traffic from LLMs, a measured approach that values being an early mover while still prioritizing channels with proven business impact is the best strategy for long-term success as the LLM ecosystem continues to mature.

Dig into how you can be an early mover in the LLM landscape with our guide: Answer Engine Optimization (AEO): Your Complete Guide to AI Search Visibility

Curious about which brands and domains are leading in AI search? Explore insights from exclusive data from Profound, or let’s talk about how to achieve more for your marketing—and your business.