Michael McBride
Founder & CEO, Atelier Commerce
Michael McBride
Founder & CEO, Atelier Commerce
This was not one SEO program applied twice. It was technical SEO treated as audience architecture: two storefronts, two vocabularies, two measurement systems, and one discipline holding them together. The same science, said correctly to each audience, and nothing said to either one that the work could not support.
This was not one SEO program applied twice. It was technical SEO treated as audience architecture: two storefronts, two vocabularies, two measurement systems, and one discipline holding them together. The same science, said correctly to each audience, and nothing said to either one that the work could not support.
The Challenge
The dual-storefront model created problems that a single-store program never has to solve.
The first was language. A meta title that converts a consumer actively repels a physician. "Best Hair Growth Serum" earns the click from someone comparing options on their phone. To a dermatologist evaluating what to stock and recommend, that same phrasing reads as marketing, not medicine. The professional audience responds to clinical framing: peptide complex, in-office protocol, science-backed, measurable results. Every title tag, every meta description, and every H1 had to be written twice, in two registers, for two readers who would never see the other version. The second was duplication. Because both stores sell overlapping products built from the same source content, the catalogs were riddled with duplicate title tags, duplicate meta descriptions, and duplicate H1s across product, collection, and blog pages. Search engines cannot rank what they cannot tell apart. On the consumer store, duplicate metadata blurred the line between a blog article and a collection page. On the professional store, duplicate H1s and titles ran across multiple rounds of products, diluting the very pages meant to signal clinical authority. Left alone, the two stores would have competed with themselves and, in places, with each other. The third was measurement. There was no clean way to read either store's organic performance. Tracking was incomplete, Search Console did not exist for either property, and the data that did flow in could not be trusted to separate consumer behavior from professional behavior. You cannot optimize an audience you cannot see. The fourth was the new front of discovery. Consumers increasingly start with an AI assistant rather than a search box. A storefront that cannot be read cleanly by an agent is invisible in that channel, no matter how well it ranks in traditional results. The consumer store needed to be legible to machines as well as people.
The Challenge
The dual-storefront model created problems that a single-store program never has to solve.
The first was language. A meta title that converts a consumer actively repels a physician. "Best Hair Growth Serum" earns the click from someone comparing options on their phone. To a dermatologist evaluating what to stock and recommend, that same phrasing reads as marketing, not medicine. The professional audience responds to clinical framing: peptide complex, in-office protocol, science-backed, measurable results. Every title tag, every meta description, and every H1 had to be written twice, in two registers, for two readers who would never see the other version. The second was duplication. Because both stores sell overlapping products built from the same source content, the catalogs were riddled with duplicate title tags, duplicate meta descriptions, and duplicate H1s across product, collection, and blog pages. Search engines cannot rank what they cannot tell apart. On the consumer store, duplicate metadata blurred the line between a blog article and a collection page. On the professional store, duplicate H1s and titles ran across multiple rounds of products, diluting the very pages meant to signal clinical authority. Left alone, the two stores would have competed with themselves and, in places, with each other. The third was measurement. There was no clean way to read either store's organic performance. Tracking was incomplete, Search Console did not exist for either property, and the data that did flow in could not be trusted to separate consumer behavior from professional behavior. You cannot optimize an audience you cannot see. The fourth was the new front of discovery. Consumers increasingly start with an AI assistant rather than a search box. A storefront that cannot be read cleanly by an agent is invisible in that channel, no matter how well it ranks in traditional results. The consumer store needed to be legible to machines as well as people.
The System
We ran two SEO campaigns in parallel, one per store, and refused to copy answers between them.
On the consumer store, we worked through successive rounds of duplicate meta descriptions and duplicate title tags, introducing page-type differentiators so that a "Blog" page and a "Collection" page could no longer be confused for one another in search. We standardized a reusable meta protocol, a single formula of product title, keyword, and brand name, and applied it consistently across products, collections, content, and blog pages. The result is a catalog where every page states plainly what it is and who it is for. We then published an llms.txt file so AI agents and shopping assistants can read the store directly, find products, and understand how to transact, turning the consumer storefront into something that answers engines can quote rather than skip. In the professional store, the same problems demanded different answers. We rewrote duplicate H1s and title tags across multiple rounds, then rewrote meta titles and descriptions in medically credentialed language built for physicians, dermatologists, and clinic providers. Where the consumer store says serum, the professional store says protocol. Where one promises visible results, the other documents clinical outcomes. This is the audience architecture made concrete: the same product, deliberately described in two vocabularies, so that each store earns the right audience instead of the wrong click. Across both stores, we cleaned up the foundation. We remediated 404 errors that surfaced in the Search Console so authority stopped leaking through broken paths. We built Google Search Console from scratch for both properties, giving each store its own honest view of impressions, clicks, and queries. We deployed a Littledata script to feed clean, reliable purchase and behavior data into GA4, and added Microsoft Clarity for heatmapping and session recording so we could watch how each audience actually moved, rather than guess. Then we turned to conversion, and built systems that work without supervision. On the professional store, we optimized the login redirect so that a tagged B2B customer lands on the shop page ready to reorder, not on an orders page that adds a step. We restructured the Laser Cap product page to cross-sell the KeraFactor solution, pairing the device with the serum that extends its results. We stood up a Recharge subscription portal so professional reorders run on their own cadence. In the consumer store, we put Shopify Flow to work on fraud detection, screening risky orders automatically before they cost the business. We built the content surfaces of the 858M+ impressions of 2025 deserved, dedicated blog and press landing pages designed to capture attention from coverage and convert it, rather than let it bounce. We migrated SMS from Attentive to Klaviyo to unify messaging with the email program, and built HubSpot lead-nurture workflows tuned to the longer, more deliberate B2B buying cycle. A migration from Stamped to Reviews.io and Influence.io is underway, rebuilding loyalty and rewards so social proof and repeat purchase compound rather than scatter.
The System
We ran two SEO campaigns in parallel, one per store, and refused to copy answers between them.
On the consumer store, we worked through successive rounds of duplicate meta descriptions and duplicate title tags, introducing page-type differentiators so that a "Blog" page and a "Collection" page could no longer be confused for one another in search. We standardized a reusable meta protocol, a single formula of product title, keyword, and brand name, and applied it consistently across products, collections, content, and blog pages. The result is a catalog where every page states plainly what it is and who it is for. We then published an llms.txt file so AI agents and shopping assistants can read the store directly, find products, and understand how to transact, turning the consumer storefront into something that answers engines can quote rather than skip. In the professional store, the same problems demanded different answers. We rewrote duplicate H1s and title tags across multiple rounds, then rewrote meta titles and descriptions in medically credentialed language built for physicians, dermatologists, and clinic providers. Where the consumer store says serum, the professional store says protocol. Where one promises visible results, the other documents clinical outcomes. This is the audience architecture made concrete: the same product, deliberately described in two vocabularies, so that each store earns the right audience instead of the wrong click. Across both stores, we cleaned up the foundation. We remediated 404 errors that surfaced in the Search Console so authority stopped leaking through broken paths. We built Google Search Console from scratch for both properties, giving each store its own honest view of impressions, clicks, and queries. We deployed a Littledata script to feed clean, reliable purchase and behavior data into GA4, and added Microsoft Clarity for heatmapping and session recording so we could watch how each audience actually moved, rather than guess. Then we turned to conversion, and built systems that work without supervision. On the professional store, we optimized the login redirect so that a tagged B2B customer lands on the shop page ready to reorder, not on an orders page that adds a step. We restructured the Laser Cap product page to cross-sell the KeraFactor solution, pairing the device with the serum that extends its results. We stood up a Recharge subscription portal so professional reorders run on their own cadence. In the consumer store, we put Shopify Flow to work on fraud detection, screening risky orders automatically before they cost the business. We built the content surfaces of the 858M+ impressions of 2025 deserved, dedicated blog and press landing pages designed to capture attention from coverage and convert it, rather than let it bounce. We migrated SMS from Attentive to Klaviyo to unify messaging with the email program, and built HubSpot lead-nurture workflows tuned to the longer, more deliberate B2B buying cycle. A migration from Stamped to Reviews.io and Influence.io is underway, rebuilding loyalty and rewards so social proof and repeat purchase compound rather than scatter.
Latest projects
Social Media Marketing
Alicia Adams Alpaca: Where nine in ten organic social visitors are meeting the brand for the very first time
Alicia Adams Alpaca runs organic social as top-of-funnel discovery, not bottom-funnel conversion. Nearly 90% of visitors it sends are first-timers, the brand's highest new-visitor rate of any channel, arriving curious after seeing a styled throw or editorial content, ready to look closer.
Social Media Marketing
Alicia Adams Alpaca: Where nine in ten organic social visitors are meeting the brand for the very first time
Alicia Adams Alpaca runs organic social as top-of-funnel discovery, not bottom-funnel conversion. Nearly 90% of visitors it sends are first-timers, the brand's highest new-visitor rate of any channel, arriving curious after seeing a styled throw or editorial content, ready to look closer.
Moonglow Jewelry: SEO & AEO, One Data Layer, Three Surfaces
We cleaned up a sprawling Shopify Plus catalog and enriched the product data behind it, structured for search engines and answer engines alike. The result: the blue link, the star-rating rich result, a seat in the AI Overview. Same data. Built to be found. Built to be cited.
Moonglow Jewelry: SEO & AEO, One Data Layer, Three Surfaces
We cleaned up a sprawling Shopify Plus catalog and enriched the product data behind it, structured for search engines and answer engines alike. The result: the blue link, the star-rating rich result, a seat in the AI Overview. Same data. Built to be found. Built to be cited.
Latest projects
Social Media Marketing
Alicia Adams Alpaca: Where nine in ten organic social visitors are meeting the brand for the very first time
Alicia Adams Alpaca runs organic social as top-of-funnel discovery, not bottom-funnel conversion. Nearly 90% of visitors it sends are first-timers, the brand's highest new-visitor rate of any channel, arriving curious after seeing a styled throw or editorial content, ready to look closer.
Moonglow Jewelry: SEO & AEO, One Data Layer, Three Surfaces
We cleaned up a sprawling Shopify Plus catalog and enriched the product data behind it, structured for search engines and answer engines alike. The result: the blue link, the star-rating rich result, a seat in the AI Overview. Same data. Built to be found. Built to be cited.
