LLM Traffic vs Organic Search
                     LLM Traffic vs Organic Search: New 
Research on Conversion Rates
Introduction
Large language models (LLMs) are transforming the way people discover information and assess
their options. While organic search continues to generate the majority of qualified visits to most 
websites, LLMs are now playing a significant role in the discovery, comparison, and decision-
making stages. This article explains each traffic type, reviews the understanding of conversion 
rates as of October 2023, and outlines practical implications for marketers.
Section 1: Definition and Mechanism of LLM Traffic
LLM traffic appears in two ways:
1. External LLM referrals
2. Visits that occur after a user has interacted with an AI answer engine or chat interface and
clicked a cited source. In 2023, many chat experiences provided complete answers with 
limited outbound links, which kept referral cohorts small and difficult to benchmark.
3. On-site LLM–assisted sessions
4. Users who are already on your site and engage with an embedded AI assistant that 
answers questions, compares products, or routes users to the right action. These sessions 
can materially change outcomes even though they do not show up as a unique referral 
source. They are best measured with event tracking tied to post-chat conversions.
Section 2: Definition and Mechanism of Organic Search
Organic search comes from unpaid listings on search engines. The mechanics are well 
understood: query, results page, click, session, conversion. Benchmarks for organic conversion 
are relatively stable. In B2B, median lead conversion commonly falls in the low single digits, 
often around 2–4 per cent depending on offer quality and funnel design. In e-commerce, global 
averages frequently sit near 2–3 per cent, with higher or lower results by vertical and device.
Section 3: Comparative Analysis of Conversion Rates
What the market knew by October 2023
 Organic search had reliable, public benchmarks across industries. That makes it the 
primary yardstick for comparing new channels.
 External LLM referrals were measurable but small. Early analyses indicated fewer 
outbound links from chat answers than from classic results, which limited sample sizes 
for conversion analysis. Where referrals did occur, clicks tended to carry high intent, but 
volumes were not large enough to generalise robust conversion averages.
 On-site LLM assistance showed a promising lift. Many late-2023 pilots reported 
double-digit conversion rates among users who completed a guided, AI-led conversation. 
This does not mean overall site conversion becomes double-digit, but it does suggest that 
LLM guidance can compress decision time and reduce friction for qualified users.
Case example (illustrative)
A retail brand has a baseline ecommerce conversion of 2.6 per cent. The team deploys an LLM 
shopping assistant that clarifies sizing, compatibility, and return policy. Among visitors who 
engage and complete a short chat, the brand observes roughly 14 per cent conversion. Only a 
subset of users chat, so total site conversion rises modestly, but the incremental revenue from 
those assisted sessions is significant. At the same time, direct traffic coming from external chat 
engines remains small, which matches the limited link-out behaviour seen in 2023. The 
takeaway: LLMs were more impactful inside the site than as a new, high-volume referral 
channel.
Section 4: Implications for Marketers
1. Segment the two LLM streams.
2. Track external LLM referrals separately from on-site assistant interactions. Use UTM 
conventions or referrer logic for the former and event-based analytics for the latter.
3. Use organic search as the baseline.
4. Compare any LLM initiative against your organic conversion benchmark. If organic leads
convert at 3 per cent and post-chat sessions convert at 12 per cent, you have a clear, 
defensible uplift story.
5. Design for answerability
6. Publish precise specs, pricing logic, comparisons, and FAQs. Structured data, clean 
internal linking, and canonical discipline help both traditional SEO and LLM answer 
quality.
7. Prioritise high-consideration journeys
8. LLM assistance excels when users must juggle multiple variables or objections. Pilot 
assistants on product comparison pages, pricing, financing, and support deflection flows.
9. Measure rigorously
10. Define a simple event taxonomy: chat_start, chat_resolved, handoff_to_sales, 
add_to_cart_after_chat, conversion_after_chat. Add holdouts to prove causality. Include 
guardrails for hallucinations, privacy, and escalation to humans.
Conclusion
By October 2023, organic search remained the most reliable, scalable source of conversions, 
typically around 2–4 per cent in B2B and about 2–3 per cent in ecommerce. Direct referrals from
external LLMs existed but were small and inconsistent, which limited public conversion 
benchmarks. The strongest early gains came from on-site LLM assistance, where guided 
conversations helped qualified users reach decisions faster. The practical play is clear: maintain 
and grow organic search while piloting LLM assistants in high-intent flows, with analytics that 
isolate their actual impact.
Frequently Asked Questions
1) Do LLM referrals convert as well as organic traffic?
Not at scale in 2023. Referral cohorts were small. Treat them as incremental rather than a 
replacement for organic.
2) Where do LLMs most improve conversions today?
Inside your site. Short, focused chats that remove doubt and guide selection can lift conversion 
among engaged users.
3) How should I instrument measurement?
Track post-chat actions separately and compare them to your organic baseline. Use holdout 
groups to validate lift.
4) What content investments help both SEO and LLMs?
Precise product specs, comparison tables, updated FAQs, accurate pricing details, and a schema 
that reduces ambiguity.
5) What should I test first?
Deploy an assistant on a single high-intent template, such as product comparison or pricing, and 
run an A/B test against your current experience.
About Doaguru Infosystems
Doaguru Infosystems helps brands turn research into growth. We combine proven SEO 
fundamentals with practical Gen-AI deployments such as on-site assistants and conversion-led 
chat flows. If you want a measured rollout plan, clean instrumentation, and a clear view of how 
AI-led interactions contribute to revenue, our team can help.