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 is where Chicago demand already exists — a maintenance buyer typing a stamped part number, a homeowner searching a grill model in March. Our job is to be the cleanest listing in that moment.
Delivered remotely for brands across Chicago and Illinois.
Google does not create the customer. It intercepts one who has already decided, which in this market means two very different searchers using the same box. A facilities buyer in Cicero types a part number off a failed component and wants a page that confirms compatibility. A household in Naperville types a category term in the second week of March because the snow finally stopped. Both are demand you capture rather than demand you manufacture, and the account has to be built to catch each without letting one pay for the other.
For a Chicago catalogue that starts in Merchant Center, not in the campaign builder. Deep manufacturing and housewares catalogues are exactly where feeds fall over: part numbers carried in the title as an internal SKU string, GTIN and MPN gaps on private-label lines, product types inherited from a line sheet rather than from how anyone shops, and quiet disapprovals on food, supplement and alcohol claims that nobody has opened the diagnostics tab to see. Fixing the feed changes which queries you are eligible for before a bid moves.
Then structure, and the structure question here is usually brand. A Chicago manufacturer with thirty years of trade equity is frequently bidding against its own distributors and against marketplace resellers of its own SKUs, so branded search looks magnificent and hides whether non-brand works at all. We separate them, give each its own budget and its own target derived from contribution margin after freight, and mine search terms weekly — because in an industrial catalogue the difference between a buying query and a spec-sheet-reading query is one word, and only the query report will tell you which one you bought.
Three Chicago realities shape a Google account here. First, the highest-intent queries in this metro are alphanumeric: part numbers, cross-references, superseded SKUs and material grades, searched by maintenance and procurement buyers around the Elk Grove Village and Bedford Park industrial belt. Those queries are won in the feed and in Search with tight exact-match coverage, not by Performance Max guessing. Second, demand here has a hard calendar. Grill, patio, cooler and outdoor spend has to be building in February for a March ramp; snow removal, ice melt and cold-weather gear invert on the same axis and can spike inside forty-eight hours of a lake-effect forecast, which is an argument for budget headroom rather than a flat monthly cap. Third, the geography is an asset almost nobody uses: a warehouse in the Chicago area reaches a large share of the US population on two-day ground, so shipping speed and free-shipping annotations in Merchant Center are doing real competitive work against a coastal seller, and bidding should reflect that the profitable zones are not evenly spread.
The same standard of work we run for every client — applied to a Chicago 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 Chicago 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.