This is a technical seo case study about a premium fragrance brand that was sitting on a structurally broken Shopify store. The numbers were ugly, the fix was disciplined, and the foundation we left behind is now ready to compound across Google and AI answer engines. Here is exactly what happened, written so any ecommerce founder staring at a red audit report can see the path out.
Key Takeaways
- TryScent is a premium fragrance e-commerce brand on Shopify, EU made and IFRA compliant, with men's, women's, and unisex collections, more than 10,000 customers, and a 4.9 out of 5 average rating, but its SEO foundation was a mess.
- The store carried more than 3000 technical SEO errors across crawl, indexation, rendering, internal linking, and structured data, with Site Health reading near zero.
- Schema was missing across the product, collection, and review templates, so rich result eligibility and AI extraction were both compromised.
- The brand was effectively invisible in AI Overviews and ChatGPT for the queries fragrance buyers actually search.
- We ran a full technical audit, triaged every issue into a prioritized backlog, and shipped fixes week by week against a clear plan.
- Technical errors went from more than 3000 to zero, and Site Health climbed from near zero to around 80 percent.
- Schema is now live across product, collection, review, and FAQ templates, restoring rich result eligibility and giving AI engines machine readable signals to cite.
- We shaped an AI visibility focused content strategy with topic clusters and detailed outlines so the brand has a compounding answer surface to publish against.
- Branded clicks are lifting as the store becomes consistently discoverable across both traditional search and AI answer engines, the headline outcome of this technical seo case study.
What was the ecommerce brand struggling with?
If you run an ecommerce store, this scenario will feel familiar. You have a real product, real customers, and real proof, but your search performance does not reflect any of it. That was TryScent.
TryScent is a premium fragrance e-commerce brand that recreates iconic luxury scents at smarter prices, with formulations crafted in EU facilities under French quality standards, IFRA compliant ingredients, and the same concentration profiles as the designer originals they reference. The catalog spans men's, women's, and unisex collections. The brand had built a passionate base of more than 10,000 customers with a 4.9 out of 5 average rating across the catalog. Product market fit was not the problem.
The problem was the foundation underneath the store. TryScent had inherited a Shopify storefront with deep technical debt, the kind that accumulates quietly when a store grows fast and nobody is watching the crawl and indexation layer. From the outside the store looked fine. Under the hood, every signal that Google and the AI engines rely on to decide who to surface for a scent search was either missing, broken, or contradicting itself.
This is the trap a lot of ecommerce founders fall into. You assume that because sales are happening and customers are happy, the SEO must be roughly okay. Then you open a real audit and the picture is very different. That is the exact gap this technical seo case study is built to expose, because the brands that recognize themselves here are the ones who can still fix it before it costs them a full year of organic growth.
CrawlCrest, an AI SEO consultancy that helps brands get found in ChatGPT, Google AI Overviews, and Perplexity, came in to diagnose the store properly and rebuild the foundation from the ground up.
What was actually broken?
When we ran the diagnostics, the scope of the damage was clear. This was not a handful of warnings. It was a store-wide structural failure.
The headline number was more than 3000 technical SEO errors. They were spread across crawl, indexation, rendering, internal linking, and structured data, and every one of them was chipping away at the trust signals search engines and AI engines look for before they decide who to surface. When errors stack up like that, they do not just sit there quietly. They actively suppress how much of the store gets crawled, how cleanly it gets indexed, and how confidently any engine will cite it.
Site Health was reading near zero. That is about as bad as a technical score gets. It meant Googlebot was fighting friction on the way through the store, rendering signals were inconsistent, and the internal linking that should distribute authority to product and collection pages was not doing its job.
Schema was missing across the product, collection, and review templates. For an ecommerce brand, that is a direct hit. Without product and review structured data, rich result eligibility evaporates and AI extraction is compromised, because the engines have nothing clean and machine readable to pull from when they assemble an answer. AI Overviews and ChatGPT were treating TryScent as effectively invisible for the queries that matter most to a fragrance buyer.
Content was thin against the buyer intent landscape too. The awareness level topics that should anchor searches around scent families, designer inspirations, and fragrance how to guides were simply not there. So there was no compounding answer surface across Google or the AI answer engines, nothing for the engines to discover, trust, and cite over time.
If this is the picture on your own store, with a low health score, missing schema, and no visibility in AI answers, you can book a free audit and see exactly where your foundation is leaking before it gets more expensive to fix.
What did we change?
We treated this as a tight three month sprint focused on shipping a clean, AI ready foundation rather than chasing rankings on a broken site. You cannot rank a store that the crawler cannot move through, so the sequencing mattered. Here is the work, broken down by workstream.
We ran a full audit and built a triaged backlog
The first move in any serious technical seo case study is a complete audit, not a spot check. We ran a full technical audit across the store and triaged every single issue into a prioritized backlog, each with a clear owner and a clear path to fix. More than 3000 errors were mapped, severity scored, and queued for resolution. That gave the team a living plan where everyone knew what was shipping each week and exactly why it mattered for organic discovery in the fragrance category. Prioritization is what turns an intimidating 3000 error report into a calm, sequenced sprint.
We brought errors down from 3000 to zero
Then we worked the list. Indexation hygiene was tightened so the right pages were indexed and the wrong ones were not competing. Internal linking was rebuilt so authority flowed cleanly to the product and collection pages that needed it. Page speed and rendering signals were brought into the green. Crawl friction was removed so Googlebot could move through the store cleanly. After the cleanup pass, the same checks that had previously read near zero were reading around 80 percent. Every one of the 3000 plus errors was resolved.
We rolled out schema across the store
With the crawl and indexation layer clean, we rolled out structured data across the product, collection, review, and FAQ templates. This restored rich result eligibility across the priority surfaces and, just as importantly, gave AI Overviews and ChatGPT machine readable signals to draw from when they decide who to cite for a fragrance query. Schema is the layer that lets an engine understand your offer and your proof points, the price, the ratings, the answers, rather than guessing at them.
We shaped an AI visibility focused content strategy
Finally we worked with the team on a fresh content strategy aimed at AI visibility from the start. Blog topic clusters were mapped against the queries fragrance buyers actually ask, from scent family education to designer comparisons and how to choose guides. Detailed outlines were delivered so every future piece supports the commercial pages through citation friendly formatting and internal linking. The goal was simple. Give the brand a compounding answer surface that earns citations across Google and AI engines, not a pile of disconnected posts.
This is the same playbook CrawlCrest runs whenever a brand has good products but a broken or invisible foundation underneath.
What were the results?
The work delivered exactly what an ecommerce brand needs before any growth tactic can compound, a clean technical foundation. The results were unambiguous.
Technical SEO errors went from more than 3000 to zero. Site Health climbed from near zero to around 80 percent against the same checks. Schema is live across the templates that mediate AI extraction, so the store is now eligible for rich results and readable by AI engines. The content roadmap is in place to keep shipping against, which means the visibility work compounds instead of stalling.
Branded clicks are lifting as the store becomes consistently discoverable across Google and the AI answer engines. The brand now has a structurally sound base to compound against, with the foundation in place to power the next phase of growth. You can read the full breakdown in the TryScent case study, where every number in this technical seo case study is documented.
The honest framing matters here. We did not promise a traffic explosion in 90 days on a broken store. We promised a clean, AI ready foundation, and that is what shipped. Growth tactics layered on top of a sound foundation compound. The same tactics layered on top of 3000 errors just leak.
What can your ecommerce brand learn from this technical seo case study?
You do not need to be a fragrance brand for these lessons to apply. The pattern repeats across almost every ecommerce store that has grown faster than its technical hygiene.
First, sales success hides foundation problems. A store can convert well and still be structurally broken for search and AI. Customer love does not fix crawl errors. Second, the number of errors is less scary than it looks once they are triaged. A 3000 error report becomes a calm weekly sprint the moment it is prioritized by severity and impact. Third, schema is not optional anymore. Without product, collection, and review structured data, you are invisible to the AI answer engines that increasingly sit between buyers and your store. Fourth, content has to be built for citation, not just for keywords, so topic clusters and clean formatting are part of the technical foundation, not a separate project.
Most importantly, fix the foundation before you spend on growth. Layering ads or link building on top of a store with near zero health is how budgets get wasted. The same problem shows up in other shapes too. We have written about why brands get traffic but no leads and what to do when traffic dropped after AI, and our keyword cannibalization case study covers another version of the same issue, all tracing back to the same root cause, a foundation that engines and AI cannot trust.
If you are facing the same problem, a low health score, missing schema, and no presence in AI answers, you can get a free audit and get a clear, prioritized picture of what to fix first.
How does CrawlCrest help you fix technical SEO at scale?
CrawlCrest is an AI SEO consultancy that helps brands get found in ChatGPT, Google AI Overviews, and Perplexity, as well as in traditional Google search. We exist for exactly the situation in this technical seo case study, a store with real products and real customers sitting on a foundation that search engines and AI engines cannot trust.
Our process starts with a deep technical audit. We crawl your store the way Google does, surface every issue across crawl, indexation, rendering, internal linking, and structured data, then triage everything into a prioritized backlog so the work is sequenced and predictable instead of overwhelming. From there we resolve the errors, rebuild internal linking, bring speed and rendering into the green, and roll out schema across the templates that mediate both rich results and AI extraction.
Then we make sure the foundation actually earns visibility. A standalone AI visibility audit shows exactly where your brand is missing from AI answers, and from there we map content clusters to the questions your buyers ask, structure pages for citation, and position your brand to be surfaced and cited across AI answer engines, not just ranked on a results page. The result is a store that compounds, where every new piece of content and every new link lands on solid ground.
If your ecommerce store has a low health score, missing schema, or zero presence in AI answers, talk to CrawlCrest and we will run a free audit that shows you exactly where your foundation is leaking and what to fix first.
Final thoughts on this technical seo case study
TryScent had everything except a foundation. Great products, loyal customers, strong ratings, and a Shopify store quietly drowning in more than 3000 technical SEO errors with near zero health and no presence in AI answers. In three focused months that became zero errors, around 80 percent health, schema live across the store, and a content roadmap built for AI visibility.
The lesson at the heart of this technical seo case study is simple. Fix the foundation first, then let growth compound on top of it. If your store is in the same place, book your free audit and we will show you the path from a red report to a foundation that gets you found everywhere your buyers are searching.
