Blog

March 2026

How to do AI search brand sentiment analysis

AI mention sentiment analysis is the process of monitoring and classifying how AI search platforms ChatGPT, Perplexity, and Google AI Overviews describe a brand as positive, negative, or neutral within their generated responses. It combines natural language processing, machine learning, and deep learning to evaluate the specific language AI models use when referencing a brand, then translates those findings into actionable intelligence for brand reputation management.

March 2026

Branded vs Non-Branded Query Tracking in AI Search

Branded vs non-branded query tracking in AI search is the practice of separately monitoring how AI engines (ChatGPT, Perplexity, Google AI Overviews) reference your brand by name versus include it in category-level answers. These are two fundamentally different intelligence functions reputation monitoring and competitive positioning that require distinct query sets, KPIs, monitoring cadences, and platform-specific strategies.

March 2026

Mentions vs Citations vs Recommendations in AI

AI platforms disagree on which brands to recommend for 61.9% of queries. Only 17% of queries produce the same brand recommendations across ChatGPT, Google AI Overview, and Google AI Mode. That gap isn't noise. It's a structural problem baked into how each platform generates responses and it renders any monitoring system that treats all AI appearances as equivalent "brand visibility" fundamentally unreliable.

March 2026

How to Fix Incorrect AI Brand Information

An AI brand hallucination occurs when tools like ChatGPT, Gemini, or Perplexity generate confidently stated but factually incorrect information about a company wrong pricing, fabricated features, outdated policies, or false origin stories and present it as verified fact. There is no disclaimer, no "we're not sure about this," and no way for the reader to tell the difference between accurate brand information and something the model invented.

March 2026

AI Search Query Discovery: Finding What Real Users Ask AI About Your Brand

AI search query discovery is the process of identifying, monitoring, and analyzing the natural-language questions users ask AI assistants about your brand, products, and category. Unlike traditional keyword research, which tracks short queries typed into search engines, AI query discovery addresses the ~70% of AI prompts that have no equivalent in traditional search conversational, multi-turn questions averaging 23 words that occur in platforms like ChatGPT, Perplexity, and Google AI Overviews.

March 2026

AI Search Traffic Attribution: Connecting AI Mentions to Website Visits and Conversions

AI-referred traffic converts at 5–23x the rate of traditional organic search. But most of it is invisible in your analytics. ChatGPT strips referrer data from paid accounts, Google AI Overviews pass no distinct referral signal at all, and the branded searches AI generates get credited to Google organic. Your highest-converting acquisition channel is hiding in plain sight misclassified as "direct" or "(none)" in GA4.

March 2026

How to Discover Buying Intent Queries in AI Search

Buying intent queries in AI search are conversational prompts entered into ChatGPT, Perplexity, and Google AI Overviews that signal a user is actively evaluating, comparing, or ready to purchase a product or service. These queries convert at 14.2% versus 2.8% for traditional Google organic a 5x advantage documented by RankScience making them the highest-converting traffic source in digital marketing. Tracking and optimizing for these prompts is now a revenue-critical function because AI search is capturing an increasing share of purchase research, and standard analytics tools undercount its influence by roughly 10x.

March 2026

How Does ChatGPT Search Work?

ChatGPT Search works by combining a fine-tuned GPT-4o language model with Microsoft Bing's web index to retrieve, evaluate, and synthesize real-time information into conversational answers with inline citations. But here's what most explainers won't tell you: according to a Semrush analysis of 80 million ChatGPT queries, only about 46% of queries actually trigger a live web search. The other 54% are answered entirely from the model's training data no web retrieval at all.

March 2026

How Does ChatGPT Recommend Products?

ChatGPT recommends products through a multi-layered system that is fundamentally different from Google. It combines pre-search reasoning, real-time retrieval from Bing's search index (not Google's), structured merchant feed data, and entity authority cross-referencing across independent sources all within an ad-free, conversational interface. Instead of ranking pages by backlinks, ChatGPT evaluates whether a product is a well-described, verifiable entity that can be triangulated across editorial listicles, Reddit discussions, review platforms, and brand-owned content. The result is non-deterministic: two users asking the same question can receive different product recommendations based on location, conversation history, and real-time retrieval variability.