The Impact of User Behavior on AI Search Results

AI Search and users have a symbiotic relationship: users get instant answers, and the AI gets smarter with every query. Most articles talk about how AI is reshaping search behavior; but what about the other way around?

User behavior isn’t just a byproduct of AI search, but one of its most powerful inputs. Every query typed, link clicked, and page abandoned feeds signals back into the system, influencing how Large Language Models (LLMs) refine results, which sources get prioritized, and what future users see. 

Today’s collective search habits actively shape tomorrow’s AI search. For brands and marketers, understanding this feedback loop is the secret sauce to unlocking visibility, trust, and influence in our AI search world. 

What Is User Behavior in AI Search?  

In the context of AI search, user behavior is the trail of digital breadcrumbs we leave every time we interact with an AI search engine (like ChatGPT, Gemini, or Perplexity) or AI assistant (like Alexa or a customer service bot). It’s not just what we type; it’s how we behave. Did you click the first link or scroll down? Did you rephrase your question because the answer missed the mark? Did you switch to voice or image search instead of text?

Each of these micro-actions helps AI better understand intent, improve future results, and personalize your search experience.

How User Behavior Trains AI

Every click, refinement, or skipped result doesn’t just show what users want, it teaches AI systems how to think about relevance. In traditional search, these signals help rank web pages. In AI search, they actively shape how LLMs interpret queries, prioritize information, and decide whether or not to cite external sources.

Here are a few of the most influential behaviors at play:

• Click-throughs: Choosing one result over another signals relevance.
• Dwell time: Longer engagement suggests satisfaction with content.
• Search refinements: Rephrasing a query (“cheap laptop” → “best laptop under $500”) teaches AI more natural intent patterns and phrasing.
• Abandonment: Leaving results without clicking often tells AI nothing was helpful, but in LLMs, it can mean the opposite. If a user reads the synthesized answer and moves on, the model interprets that as success, learning that a standalone response (without citations) was enough.
• Search modality: Using voice commands, visual search (like Google Lens), or typed queries signals shifting user preferences and forces AI to adapt across modalities.

Example: The rise of voice assistants like Siri and Alexa changed how we search. Instead of typing “weather NYC,” we now ask, “What’s the weather like in New York City today?” That behavioral shift forced search engines, and now LLMs, to master natural language understanding.

The takeaway: User behavior doesn’t just shape what AI shows, it teaches AI how to think. Every action we take trains the model to deliver more contextually relevant, human-like results.

The Feedback Loop Between User Behavior & AI

AI search engines like ChatGPT and Gemini are the best multitaskers. In addition to serving up results, they take every interaction as a learning opportunity. Each click, scroll, and abandonment is a signal either validating or challenging the LLM’s understanding of what’s relevant.

In response, the AI search engine refines its results and may even re-train the model based on those signals. This creates a feedback loop: users teach the AI, and the AI reshapes how users search. 

How AI Adjusts to Behavior

AI responds to user behavior in two main contexts: traditional search engines, which use AI to refine the search experience, and generative AI search tools. The mechanics may differ, but the principle is the same: systems adapt based on how people interact with results. 

Traditional Search (Google, YouTube, Bing, Amazon, etc.)

• Personalized rankings: Algorithms weigh signals like click-through rate, dwell time, and bounce rates to refine which results surface first.
• Contextual signals: Location, device type, and search history shape SERPs on an individual level.
• Content recommendations: YouTube and TikTok adjust feeds based on watch time, likes, and replays, surfacing what users show the strongest engagement with.
• Aggregate learning: Billions of queries across users influence how ranking systems evolve over time (e.g., better understanding natural language).

Generative AI Search (ChatGPT, Gemini, Perplexity, etc.)

• Session-level adaptation: Models adjust responses mid-conversation, learning from clarifications (“make this shorter,” “focus on cost”) to refine outputs in real time.
• Account-based personalization: Gemini pulls from your Google ecosystem (Calendar, Gmail, Maps), while ChatGPT offers custom instructions and memory to tailor responses over multiple sessions.
• Feedback loops: Upvotes, downvotes, and regenerations don’t just fine-tune results for you; they inform collective retraining and model updates at scale.
• Aggregate learning: Just like search engines, LLMs learn from patterns across millions of prompts, shaping how they interpret and prioritize answers in future responses.

Example: In Google Search, a click signals relevance, pushing that page higher in rankings for future users. In ChatGPT, a clarification like “explain it in simpler terms” immediately changes the style of response. Repeated feedback like this across thousands of users gradually nudges the model to default toward clearer phrasing. 

Testing the Feedback Loop: Gaming Headset Queries

Okay, now we’re going to run our own little experiment with the help of Goodie. Take this parent topic: gaming headset. In the Goodie screenshot below, you can see this is a high-volume term (3,400+ queries) that spans across the major LLMs. It’s also product-driven, which makes it a perfect test case for how AI search results shift based on user behavior.

Three things stand out here: 

1. Model distribution: The bulk of “gaming headset” queries happen on ChatGPT and other large models, but Gemini and Perplexity still hold noticeable market share (making them important players in this space).
2. High demand: 3,432 prompts were logged for “gaming headset” across AI tools. Lots of people are asking about this topic.
3. Low visibility potential (15, marked “Poor”): Even though demand is high, it’s hard for brands to get surfaced in AI results. Most answers are generic and inconsistent with citations, which means actual brands often get left out of the AI loop.

To put this in regular human-speak: lots of users are asking AI about gaming headsets, but the chances of a brand actually being named in those answers are pretty low right now.

So what does this look like in practice? Let’s run three prompts across different LLMs:

• Baseline: “What is the best gaming headset?”
• Refinement #1: “What is the best wireless gaming headset under $200?”
• Refinement #2: “Summarize the best headsets for competitive FPS players.”

We tested these across ChatGPT and Perplexity because they represent two distinct user experiences: ChatGPT’s conversational, context-driven interface versus Perplexity’s citation-first, research-style approach. Using both gives a fuller picture of how user behavior shapes AI results across different search ecosystems.

To keep things consistent, we used each tool’s “incognito” mode so no personal search history influenced the results. Here’s what happened:

ChatGPT Results

Baseline Prompt: “What is the best gaming headset?”

• Answer type: Broad overview → what features to look for, then a general product roundup (Astro A50, SteelSeries Arctis Nova, HyperX Cloud III).
• Sources cited: Mentions from big review sites (RTINGS, SoundGuys, PC Gamer).
• Takeaway: A “buyer’s guide” style answer: balanced, general, no clear prioritization.

Refinement #1: “Best wireless gaming headset under $200”

• Answer type: Much more practical → “Top Recommendations Under $200” list
• Products elevated: SteelSeries Arctis Nova 7, HyperX Cloud Alpha Wireless, SteelSeries Nova 5, plus mid-market picks like Kraken V3, Barracuda X, Logitech G733.
• Structure shift: ChatGPT introduced:
  
  • Expert tradeoffs (latency, mic, comfort).
  • A decision matrix (“Best all-rounder,” “Longest battery life,” etc.).
  • A “my pick” conclusion (SteelSeries Nova 7).
• Takeaway: Refinement pushed ChatGPT to filter aggressively by budget and features, dropping high-end models and surfacing mainstream, mid-market options.

Now, one more for good measure: 

Refinement #2: “Best headsets for competitive FPS players”

• Answer type: Pivoted to performance-first gear
• Products elevated: Logitech G Pro X, SteelSeries Nova Pro, Astro A50, HyperX Cloud Alpha.
• Focus areas: Low latency, directional clarity, mic quality, comfort for long sessions.
• Tone shift: Less about price, more about competitive advantage; “wired still king for latency,” “don’t be swayed by RGB.”
• Takeaway: Refinement reshaped the entire recommendation logic, from consumer comfort to pro-level performance.

Perplexity Results

Baseline Prompt: “What is the best gaming headset?”

• Answer type: Direct, list-driven recommendations. Organized by categories like All-around Gaming Headsets and Audiophile Picks.
• Products surfaced: HyperX Cloud Alpha, Razer BlackShark V2 X, Audeze Maxwell, SteelSeries Arctis Nova Pro.
• Sources cited: Heavily sourced: RTINGS, HiFiGuides, PC Gamer, SoundGuys, and more. Each product recommendation tied back to an external authority.
• Takeaway: While ChatGPT acts like a guide that walks you through how to decide, Perplexity behaves more like a research assistant; giving you sourced, ready-to-click answers immediately.

Refinement #1: “Best wireless gaming headset under $200”

• Answer type: Narrowed to budget-friendly wireless models with direct pricing information.
• Products elevated: SteelSeries Arctis Nova 7 Wireless, Razer BlackShark V2 Pro Wireless, Logitech G522/G733, Corsair Void Wireless, Turtle Beach Stealth 600.
• Structure shift: Perplexity emphasized credibility by anchoring each suggestion to where it was reviewed or sold (SoundGuys, Best Buy, PC Gamer).
• Takeaway: The refinement pushed Perplexity to filter by cost and features but in a citation-first style: less about personalization, more about authoritative validation.

Refinement #2: “Best headsets for competitive FPS players”

• Answer type: Narrowed to budget-friendly wireless models with direct pricing information.
• Products elevated: SteelSeries Arctis Nova 7 Wireless, Razer BlackShark V2 Pro Wireless, Logitech G522/G733, Corsair Void Wireless, Turtle Beach Stealth 600.
• Structure shift: Perplexity emphasized credibility by anchoring each suggestion to where it was reviewed or sold (SoundGuys, Best Buy, PC Gamer).

What Stands Out

ChatGPT adapts conversationally, shaping its answers through decision tables, pros and cons, and even “my pick”-style recommendations. Perplexity, on the other hand, adapts structurally, leaning on authoritative lists, citations, and clear sourcing to build credibility.

Both tools significantly shifted their product recommendations once the query was refined, proving that user behavior doesn’t just tweak AI outputs but reshapes them entirely, and each platform does so in its own way.

Why This Matters for Brands & Marketers

This experiment illustrates the feedback loop in action. As soon as a user refines their query, AI search engines don’t just tweak the answer; they reframe the entire decision-making logic.

For brands, this has three big implications:

1. Visibility isn’t guaranteed, even in high-demand spaces: Goodie’s data shows that “gaming headset” queries are booming (3,400+ prompts), yet visibility potential is marked “Poor.”
   
   • Translation: even if your product is the right fit, AI won’t reliably surface you unless your content matches the way users are searching.
2. User refinements decide who wins: In our tests, broad queries gave broad answers (everyone from Astro to SteelSeries to HyperX). But as soon as constraints were added (budget, wireless, or competitive play) the field shifted. Brands that had content structured around those niches (e.g., “best wireless under $200”) got pulled in, while others dropped out.
3. Different AI platforms reward different content styles: ChatGPT rewards decision-making support: structured pros and cons, comparisons, and clear takeaways. Perplexity, however, rewards authority and citations: being mentioned by expert reviewers, retailers, or third-party sources.
   
   • Translation: brands need to optimize for both if they want consistent exposure across the AI ecosystem.

The bottom line: AI search isn’t a “one answer fits all.” The way users phrase and refine queries directly determines which brands are visible, which products are recommended, and which voices are trusted. For marketers, that means investing in structured, scannable, citation-worthy content (both on and off-page), and thinking beyond traditional SEO.

And, we have proof that it works: one of our own clients, SteelSeries, consistently shows up in these topic variations (from broad headset queries to niche prompts about budget-friendly wireless gear). That visibility isn’t an accident; it’s the result of building content that aligns with both user behavior and the way AI engines prioritize information.

The winners in AI search are the brands that not only rank but also actively align with the behavioral patterns shaping results.

Where User Behavior Shapes AI in Practice

With LLMs, the impact of user behavior is everywhere. To a user, these tools are just spitting out answers left and right, but they do much more than that. With every conversation, LLMs are adapting, refining, and evolving based on how people interact with them. A few clear examples:

Prompt Refinements

Every time a user says, “Can you make that shorter?” or “Focus on budget-friendly options,” the model adjusts its answer in real time. Those refinements not only improve the session for that user, but also contribute to aggregate learning about how future answers should be framed.

Feedback Loops (Votes & Regenerations)

Upvotes, downvotes, and “regenerate” clicks are all behavioral signals. They tell the model whether an answer hit the mark, and at scale, they help guide model updates. Thousands of downvotes on vague or inaccurate answers push developers to retrain models toward clearer, more trustworthy outputs.

Memory & Personalization

ChatGPT, Gemini, and others are increasingly leaning into memory. If you consistently ask for structured outlines, the model adapts to provide that format. This mirrors how ChatGPT has learned to respond to my personal style, refining based on repeated behavior. For brands, this means that user interactions shape not just single answers, but the default “voice” of a model over time.

Source Shaping

In Perplexity or Gemini, the user clicks on cited links feed back into the system. If everyone consistently clicks through to a certain review site or brand page, those sources gain weight in future recommendations. User behavior decides which voices get amplified.

Why this matters for marketers: User behavior isn’t just a one-off output. These outputs train the entire ecosystem of LLMs. That means the way people interact with AI search is just as important as the way brands optimize their content. If your content isn’t structured, cited, and useful enough to earn clicks, refinements, or positive feedback, you’ll get filtered out of the loop.

Future of AI Search & User Behavior

This feedback loop is only accelerating, folks. More people are opting to use LLMs as their default search engine, and every click, refinement, and feedback button shapes the future of AI search. Here’s what is on the horizon:

Evolving Interaction Styles

With Google, we type a query, but AI search is going in a multimodal direction. People are asking questions using natural language, yes, but they’re also uploading images, speaking commands, and pulling in multiple sources and references while requesting responses in a specific format (“Show me this in a chart”).

LLMs like ChatGPT, Gemini, Perplexity, and many other AI tools are increasingly training to understand these multimodal behaviors, and the way users adopt them will set the direction for future search interfaces. For example: 

• Perplexity lets users shift seamlessly from a general query into shopping research. You can ask about “the best wireless gaming headset under $200,” and the platform not only summarizes reviews, but also links directly to product pages, price comparisons, and retailer sites. It blurs the line between search engine, research tool, and shopping assistant.
• ChatGPT, on the other hand, functions more like a traditional messenger. The interface is conversational first: you refine your query through back-and-forth prompts (“make it shorter,” “compare these options”) without being nudged toward transactions or product listings. It mirrors the experience of chatting with a colleague rather than browsing a search results page.

These subtle differences in interaction style matter because they influence what kinds of behavior signals get fed back. Perplexity tracks which sources users click and reinforce; ChatGPT tracks how prompts are refined and adjusted. Over time, these choices will shape two very different futures for how people discover brands. Consider this the AI arms race to see who can provide the most helpful format for users, like Google versus Yahoo! back in the day. 

From SERPs to Chats: How AI Is Redefining Search Flow

This evolution in interaction styles has led to a bigger shift: LLMs aren’t just assisting search anymore but are acting as the search engine itself. LLMs aren’t being treated like the sidekick anymore; they are the search engine. Instead of users scanning SERPs, many people’s first step is opting for their LLM of choice to begin their research. This small shift is completely changing the dynamics of how visibility works: 

• Search flow is collapsing. In traditional search, users type a query, scan the blue links, click through, bounce, and perhaps refine their search. In an LLM, that process is broken down into essentially a single exchange. You ask, it answers. If you refine, it rewrites. The “hunt and peck” browsing behavior we’re used to is being replaced by a guided, conversational funnel.
• Refinements become the new query chain. In Google, refinements mean new searches. In ChatGPT, they’re part of the same thread. Asking “best gaming headset” → “make it under $200” → “focus on PS5 competitive players” doesn’t create three separate searches; it builds one evolving conversation that teaches the model how to interpret intent.
• Different platforms emphasize different behaviors. In Perplexity, refinements often push users toward action: product pages, price comparisons, shopping research. Think of it as AI-driven top-of-funnel and mid-funnel in the same place. But in ChatGPT, refinements remain purely conversational. It’s more like messaging a knowledgeable colleague with an emphasis on information quality.
• For brands, this means visibility is conditional. On SERPs, if you rank in the top three, you’re almost always seen. In LLMs, you may only appear in the baseline query, but drop out when refinements happen. If you’re not optimized for the niche prompts users evolve toward, you disappear from the conversation.

Example in action: Our headset experiment illustrated this perfectly. In ChatGPT, the broad prompt “best gaming headset” surfaced Astro, HyperX, and SteelSeries. But once we refined it to “best wireless under $200,” Astro disappeared, and SteelSeries gained visibility. The refinement itself decided who stayed in the conversation, and who got cut out.

Why this matters: AI isn’t about winning one keyword. It’s about building content ecosystems that can flex with user refinements: broad enough for general queries, specific enough for budget, feature, or use-case prompts. Prepping for that conversational search flow will outlast a focus on optimizing for static SERPs alone.

Feedback at Scale Becomes the New Algorithm

Google built its empire on PageRank, rewarding sites with backlinks. Authority wasn’t just what you said about yourself; it was who linked to you, how often, and from where. In the world of LLMs, that same role is being played by behavioral feedback.

• Micro-feedback in real time. Just to reiterate one more time here, every time a user clicks “regenerate”, thumbs up or down, or refines their prompt, that’s data. For the user, the conversation immediately improves. But aggregated across millions of users, that's a new ranking system, baby; telling models which kinds of answers “win”. 
• Aggregate signals drive training. When thousands of people downvote vague answers in ChatGPT, the model eventually gets retrained to provide more sourced, specific content. When Perplexity users consistently click on certain review sites, those sites get reinforced as “trusted voices.” This is the new backlink; instead of links between sites, it’s engagement between humans and AI outputs.
• Content quality is no longer optional. For a while, mediocre content could still win on the SERP if it was keyword-optimized and well-linked. That simply isn’t enough for LLMs. If users don’t click your citations, stay on your page, or give positive feedback, your brand is risking falling out of that reinforcement loop.
• Platforms are weighting this differently. 
  
  • ChatGPT: heavily influenced by in-thread refinements (“explain it shorter,” “add pros and cons”), plus explicit upvotes or downvotes.
  • Perplexity: more sensitive to click-through behavior on cited links, rewarding the sites users treat as worth exploring.
  • Gemini: pulls from Google’s broader ecosystem (click history, logged-in user data), so behavioral feedback is layered with personal context.

Search marketers used to optimize for algorithms. Now they have to optimize for user satisfaction signals at scale. If your content doesn’t get clicked, cited, or reinforced, you’re invisible, no matter how good your traditional SEO is. Winning in AI search will mean:

• Crafting content that earns engagement signals (clear, trustworthy, scannable).
• Building authority that gets you cited in third-party reviews (so you surface in Perplexity).
• Providing the structure and clarity that earns positive refinements (so ChatGPT “learns” your content works).

Regular SERPs aren’t enough for today’s users. If SEO is about optimizing algorithms, Answer Engine Optimization (AEO) is about optimizing for humans, because they’re our new algorithm. Every feedback signal shapes what LLMs surface tomorrow. 

For us brands and marketers, this means two things: 

1. You can’t treat AI search as static. Baseline visibility doesn’t guarantee staying power. Refinements and feedback loops constantly reshape the playing field.
2. You have to meet users where they’re headed. Whether that’s conversational threads in ChatGPT, citation-heavy answers in Perplexity, or multimodal prompts across Gemini, the winners will be the brands whose content adapts to both the broad queries and the behavioral pivots that follow.

From Algorithms to Actions: The Power of User Behavior

The Goodie team sees this first-hand. In our headset experiment, SteelSeries gained visibility not because of luck, but because its content matched the behaviors users were actually expressing: budget constraints, wireless needs, and competitive play. That’s the future of AI search: brand visibility will belong to those who anticipate and align with the way people interact with these tools.

The takeaway is simple: user behavior isn’t just about influencing AI search, it is AI search. If you want to be visible tomorrow, you need to optimize not only for what people ask, but also for how they behave once the answer shows up.