Reducing single-engine dependence for a fragrance retailer
Almost all of TryScent's AI visibility rested on one engine, and nobody had measured that. When ChatGPT's coverage of the brand later contracted, the store did not vanish from AI answers, because by then three other engines were citing it.
Measured in Ahrefs against a fixed January 2026 baseline. Total citation volume fell over the same period as ChatGPT's coverage of the brand contracted; the results section sets that out in full.
Problem
TryScent sells fragrance direct to consumers across men's, women's and unisex collections. Like most stores of its size it was running on a theme that had never been audited, and the technical debt had compounded quietly for as long as the store had existed.
A page-level audit found over three thousand technical errors across crawlability and site structure. Schema markup was missing across the store, which cost rich result eligibility and, more importantly, left AI crawlers inferring product attributes rather than reading them. URL slugs and product pages did not match in places, which interferes with how products get indexed at all. Domain authority was weak.
The finding that reframed the engagement came from splitting AI visibility by engine rather than reading it as one number. Ninety-eight percent of TryScent's AI citations came from ChatGPT alone. On paper the brand had AI visibility. In practice it had a single point of failure that nobody had looked at closely enough to notice.
Baseline
The January 2026 snapshot combined an Ahrefs pull with a page-level technical audit. Read together, the two halves explained each other: a store engines could not parse cleanly, and a citation base resting entirely on one of them.
Engine coverage tracked across ChatGPT, Perplexity, Gemini, Google AI Overviews, Google AI Mode, Copilot and Grok. Error count and schema coverage are audit findings rather than Ahrefs metrics.
Strategy
Two problems, one of them urgent and one of them structural. The store needed to be readable, and the brand needed to stop depending on a single engine for its entire presence in AI answers. Three moves.
A page-level audit converted into a prioritised cleanup roadmap. Hand an e-commerce team three thousand items and nothing gets done. Hand them ten in the right order and the next ninety become possible.
Schema across product pages so the store was eligible for rich results and legible to AI crawlers reading notes, categories and price as data rather than inferring them from page copy.
Content and formatting recommendations aimed specifically at the engines not already citing the brand. Visibility that rests on one engine is not visibility, it is exposure that happens to be pointing the right way.
We did not build a strategy around defending the ChatGPT position. Citation volume on any single engine moves for reasons entirely outside a brand's control, and organising the work around holding one number would have left the store more exposed rather than less.
Execution
Advisory, with TryScent's team implementing against the roadmap. Technical remediation ran first, because structured data and content recommendations land on nothing if the crawler cannot get through the store reliably.
Ran a page-level audit converting three thousand technical errors into a prioritised, sequenced cleanup roadmap.
Directed remediation across crawlability, site structure and product page architecture.
Fixed URL slug and product page mismatches interfering with how products were being indexed.
Specified the missing structured data across the store to restore rich result eligibility.
Made product attributes machine-readable so AI crawlers could read them rather than infer them from copy.
Delivered blog topics and outlines built for AI Overviews, AI Mode and LLM extraction.
Guided link acquisition toward strengthening a thin authority profile.
Results
The structural change is the one worth reading. Seven months ago the brand's presence in AI answers was effectively one engine's decision. Today four engines cite it, and two thirds of those citations come from engines that contributed almost nothing at the start.
Source: Ahrefs. July 2026 used as the last complete month; AI citation counts captured August 2026. Stated in full: total citation volume fell from 140 to 92 across the period, because ChatGPT citations for the brand contracted from 137 to 29 while the other engines grew. Organic traffic value also declined over the same window, and the store's organic footprint remains small in absolute terms. The engagement was scoped to technical health, structured data and citation breadth rather than traffic volume.
Lessons for e-commerce brands
Ninety-eight percent of TryScent's AI citations came from one engine, and no one had measured it because AI visibility was being read as a single number. One number hides the risk sitting inside it.
An audit that hands a small e-commerce team three thousand items produces paralysis, not progress. Sequencing the list into something a team can actually start on Monday is the deliverable. The list is just the input.
Structured data is how an engine reads notes, sizes, categories and price without guessing. Missing schema does not only cost rich results. It costs accuracy in the AI answers that describe your products to buyers.
ChatGPT citations fell while Perplexity and Copilot rose over the same months. Track engines separately or you will misattribute both your wins and your losses to whatever you happened to ship that quarter.
Book a FREE Call
Choose a time slot that works best for you. Thirty minutes to talk through your business, your goals and what you expect from a consultant.