AI Search vs Traditional Search: Which Drives Better B2B Pipeline?

We analyzed over 117,432 inbound leads across multiple B2B brands to understand how AI search traffic compares to traditional search and other sources in terms of pipeline quality and downstream impact. 

As more buyers turn to AI search and LLMs to ask product and vendor-related questions, we set out to answer the most critical question:

Does traffic from AI search sources lead to better pipeline outcomes than traditional sources like Google and Bing?

This question led us to conduct one of the largest B2B focused AEO studies in the AI search space. 

The study analyzed traffic from AI platforms (ChatGPT, Perplexity, and Gemini) compared to  traditional search engines (Google and Bing  as well as other traffic sources including paid, direct and referral traffic.

The findings aren’t just interesting, they’re a wake-up call for growth leaders, SEO teams, and content marketers clinging to legacy strategies and models of attribution and optimization.

The Study: Funnel Data from B2B Brands

We analyzed data from B2B SaaS and professional services companies, across the U.S., with ARR ranging from $1M to $108M. 

Over 117,432 inbound leads were reviewed from October 2024 through April 2025. All data used last-click attribution and tracked full-funnel performance:

Traffic → Leads → MQLs → SQLs → Opportunities → Closed Won

We segmented traffic into three categories:

• AI Search (ChatGPT, Perplexity, Gemini)
• Traditional Search (Google Search, Bing)
• Other Channels (non-search, mostly direct, paid, referral, and email)

Headline Finding: AI Search Delivers Higher-Quality Leads

Despite the lower volume, AI search traffic converts into pipeline at significantly higher rates than traditional search.

That’s a 56.3% higher close rate from leads that originated in AI search agents compared to Google or Bing.

Among all platforms analyzed, ChatGPT is the most efficient B2B traffic source that exists today with nearly 2X the lead-to-close rate of traditional search engines. 

What this Means for B2B Marketers 

Marketers are still spending billions optimizing for Google, while ignoring the fact that discovery behavior is shifting. AI-native agents like ChatGPT and Perplexity are becoming front doors to product research, vendor comparisons, and commercial queries.

Here’s what we observed:

• ChatGPT leads had the highest close rate (4.08%) of any source in the study.
• Google still dominates top-of-funnel traffic (58% of total visits) but had average downstream efficiency especially when compared to ChatGPT. 
• Other non-search inbound sources generated fewer MQLs and were the least efficient channel in terms of lead-to-close yield.

What's Driving AI Search Efficiency?

We identified four key factors that drive the increased funnel conversion rates from AI search traffic.

1. Zero-click behavior: Users complete most of their research and often make a decision inside the AI platform, they mostly click through for navigation or transactions purposes. 
2. Intent density: AI queries tend to be more specific and buyer-oriented (e.g. “best cybersecurity vendor for mid-size law firms”).
3. Contextual trust: Recommendations from LLMs inherit perceived objectivity and credibility.
4. Content compression: AI summarizes and synthesizes research data, helping consumers get clarity on their needs and decide faster.  This favors brands with strong topical authority and clean citations.

What is the dominant traffic source for B2B brands today?

Google continues to dominate B2B website traffic, despite the rapid growth in AI search platforms. ChatGPT emerged as the second-largest traffic source, surpassing Bing and other traditional search engines. Among AI platforms, ChatGPT leads decisively with 6.5x more traffic than Perplexity and 17.4x more than Gemini.  

How to Start Your B2B AI Search Strategy 

This is no longer news at this point; If you’re not optimizing for AI search, you’re missing out on where the highest-quality organic demand is forming. Here’s how to start:

1. Track AI Search Traffic & Conversions 

Set up referrer tracking for traffic from ChatGPT browser plugins, Perplexity, and other assistants. Use UTM parameters or real-time traffic enrichment tools to segment that data.

2. Audit and Monitor Your Visibility in LLMs

Use an AEO platforms, such as Goodie, to observe and monitor where and how your brand is being mentioned in answers. Is your content being cited? Are you showing up in comparisons? Are competitors more or less visible than your brand? From this information you can develop a strategy that addresses topical/perception gaps to improve visibility. 

3. Structure Content for AI Retrieval

LLMs value clarity, citations, structure, and verifiable claims. Don’t just write for humans; write for retrieval. Write to be summarized.

4. Segment AI Search in Your Pipeline Reports

Track AI search-driven leads as their own cohort. Measure their MQL-to-Closed rates separately. Let the data speak for itself.

AI Search B2B SaaS Benchmarks

To help B2B marketers understand what “good” looks like for AI search traffic, we’ve broken down the key performance metrics by platform. These benchmarks can act as a guide to evaluate your own AI search performance.

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What’s Next 

The reality is search has changed. Buyer behavior has changed and the resulting funnel has changed. The highest-quality leads now come from ChatGPT, not Google. If your strategy hasn’t evolved, you’re not just behind in fact, you’re optimizing for the wrong benchmarks entirely. This study highlights the opportunity for brands that get in early, measure the right things, and optimize where the next conversion happens, not just where the last one did.

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_{Methodology Summary}

_{This study analyzed the full-funnel performance of 117,432 inbound leads from U.S.-based B2B SaaS and professional services companies between October 1, 2024 and April 30, 2025.} 

_{The goal: to determine whether AI search traffic drives better pipeline outcomes than traditional search or other channels. Claude Sonnet 3.7 was used to help analyze and normalize the data.} 

_{Sample & Scope}

• _{Companies: Thirteen between small, mid-market and enterprise B2B brands with ARR between $1M–$108M}
• _{Industries: SaaS, AI mar-tech, ad-tech, HR tech, healthcare, and services}
• _{Markets: U.S. and Canada, English-language searches}
• _{Sales Cycle: Median of 71 days}
• _{All data was aggregated and anonymized, sourced from CRM and marketing automation tools.}

_{Funnel Definitions}

_{We standardized funnel stages across all companies:}

• _{Traffic: Unique sessions}
• _{Lead: High-intent action (e.g. form fill, demo request)}
• _{MQL: Met basic behavioral and ICP-fit criteria}
• _{SQL: Accepted by sales}
• _{Opportunity: Created deal in CRM}
• _{Closed Won: Signed agreement or revenue event}

_{Attribution & Tracking}

• _{Attribution: Based on last-click, using referrer data, and session IDs}
• _{AI Source Tracking: ChatGPT, Perplexity, and Gemini sessions were identified via known referrer URLs. ChatGPT typically shows multiple URLs so we had to group them together under the same channel.} 
• _{Three participants had self reported and post conversion survey data so we factored that in.} 

_{Channel Classification}

_{Traffic was grouped into:}

• _{AI Search: ChatGPT, Perplexity, Gemini}
• _{Traditional Search: Google and Bing (paid + organic)}
• _{Other: Direct, referral, paid social and email}

_{Data Controls & Validation}

• _{Funnel conversion rates were validated across CRM and marketing automation platforms (e.g. Salesforce, HubSpot)}
• _{Duplicate leads, bots, and session misattributions were filtered out}
• _{Anomalies (e.g. extreme conversion spikes) were excluded}
• _{Data was analyzed at the channel level, not by individual brand}

_Limitations

• _{AI search volume is still growing; Gemini and Perplexity results may show higher variance}
• _{Last-click attribution may miss earlier influence in complex B2B journeys}
• _{Some AI traffic is not yet fully trackable, especially with private browsing or embedded experiences}