Merchant Center & Feed Rebuild
Disapprovals cleared at source, GTIN, MPN and attribute gaps closed, and titles rewritten to lead with searched terms. Custom labels for margin, stock cover and seasonality.
Google catches Austin demand that was created somewhere else — a can tasted at a run club, a name read off a shelf. Our job is to be the cleanest listing in that moment.
Delivered remotely for brands across Austin and Texas.
Google does not create the Austin functional-beverage customer. It intercepts one who was created somewhere else: a sample handed out at a Zilker run club, a can spotted in a Central Market cold case, a formulator's name repeated on a podcast. The decision happens off-platform and arrives in the search box afterwards, which makes the account a distribution job rather than a persuasion job — and on Shopify the distribution runs through Merchant Center long before it runs through the campaign builder.
Feeds in this category break in ways a generic account never sees. Co-packed private-label SKUs arrive with no GTIN because nobody assigned one. A single product exists as six flavours, three sizes and two multi-packs, which becomes eighteen near-identical items bidding on the same query unless product types and custom labels are built deliberately. And supplement and functional-food listings quietly accumulate policy flags on ingredient and claim language that sit unread in the diagnostics tab for months while the brand blames its bidding strategy.
Then structure. Branded search for an Austin brand on shelf is inflated by distribution — people who already bought you at a natural grocery are typing the name to reorder — so leaving brand inside a shared campaign produces a return figure that measures your retail footprint rather than your advertising. We isolate it, judge non-brand on its own line, and mine the query set every week for the dose, ingredient and comparison phrasing that actually ends in a purchase.
The co-packers, formulators and brokers clustered around this metro made it cheap for you to launch — and just as cheap for the four brands bidding against you on the same non-brand terms with a near-identical formula. Nobody wins that auction on bid strategy. It gets won on feed depth: ingredient and dose written into the title where a buyer types them, format and dietary attributes populated so you are eligible for the narrow queries at all, and stock cover carried in a custom label so budget stops accelerating into a flavour your co-packer will not run again this quarter. The cost side has a local wrinkle too. From roughly May through September, cold packs, insulated liners and shortened transit come out of the same contribution margin your return target was derived from, so a target set in February is quietly unprofitable in July at identical revenue. And the March and October event weeks send branded volume sharply up without moving non-brand demand at all, which is how an averaged quarterly readout convinces a founder the account improved when really the calendar did.
The same standard of work we run for every client — applied to a Austin brand’s realities.
Full service detailDisapprovals cleared at source, GTIN, MPN and attribute gaps closed, and titles rewritten to lead with searched terms. Custom labels for margin, stock cover and seasonality.
Branded demand isolated into its own campaign, budget and target so non-brand performance becomes visible. Competitor conquesting runs as a separate line, judged separately.
Asset groups split by margin band and product type instead of one catch-all, with listing-group bids, product exclusions and brand-term controls applied wherever PMax still allows them.
A weekly pass over search terms and PMax category reports, with a maintained shared negative library so budget stops leaking into research, DIY and job-seeker queries.
Merchant promotions, sale price annotations, shipping and returns policy setup, product ratings, and local inventory ads where you have stores worth feeding.
Targets set from margin per product group rather than a platform default, moved in controlled increments, with a written reason attached to every bid and budget change.
We do not work off a rate card. Every Austin engagement starts with a fixed statement of work — named deliverables, named dates, one number — written after we have looked at your store, not before. If a smaller first step would serve you better, we will say so.
Get this scopedMerchant Center diagnostics, attribute coverage, campaign overlap and wasted spend scored against ninety days of search terms. You get the findings whether you hire us or not.
Titles, attributes, product types and custom labels rebuilt before any campaign work. A perfectly structured account on a bad feed still shows the wrong products to the wrong queries.
Brand, non-brand, Shopping, PMax and Search rebuilt with hard budget boundaries and a shared negative library, so each line answers a different commercial question.
tROAS targets derived from contribution margin by product group and applied gradually, so the account keeps its learning instead of resetting it every Monday.
Weekly query mining, monthly feed reviews, then expansion into the categories the search data says you can profitably win. Nothing scales before the query set is clean.
Anonymised under NDA. Figures pulled from the client’s own analytics.
~$6M/yr DTC, 900+ SKUs across size and colour variants, US · Shopify Plus
Returns ran at 31% and refund cost consumed the entire paid media margin. One size chart image served 40 different fits, and 62% of add-to-carts started on a collection page that never showed variant availability. The named constraint: no new product photography budget, so every fix had to come out of the existing asset library and the review corpus.
~$9M/yr, 210 SKUs, US + AU · Shopify Plus (migrated from BigCommerce)
Meta ROAS had slid from 3.6x to 1.9x in a year and the team had spent twelve months buying new creative to fix it. The real cause was measurement: the BigCommerce checkout dropped 22% of purchase events and the Conversions API had never been installed, so both ad platforms were optimising on incomplete data. The named constraint: peak season was 14 weeks out, and the replatform had to be live and stable well before Black Friday traffic arrived.
“Six thousand products and a Shopping feed nobody had touched since it was first generated — a third of it was disapproved and we had no idea. They rebuilt the feed off our real product data, fixed the GTIN and size attributes, and split brand off from non-brand so I could finally see what we were actually paying to acquire. They also cut the broad 'baby clothes' terms that were eating a quarter of the budget on people who were nowhere near buying. Spend is roughly flat and non-brand search revenue has close to doubled.”
Thirty minutes with the strategist who would actually run your account. We screen-share your store, read your data live, and tell you the three highest-value things we can see from the outside.
Prefer to write it out? [email protected] gets a real reply the same business day, Mon-Fri, 9am-6pm MT.