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 captures demand that already exists — so in a Sydney account the feed, the structure and the query data do the work, not the creative.
Delivered remotely for brands across Sydney and Australia.
Someone has already decided they want linen sheets or a rash vest, and they have typed it. The job is to be there with an accurate price, an accurate delivery promise and a product page that closes. That makes Merchant Center feed quality the first and largest piece of work in any Australian account. Prices have to be submitted GST-inclusive and match the landing page exactly, GTINs and brand fields have to be right, and shipping and delivery-time settings have to reflect your actual carrier performance by zone — because Google surfaces those delivery estimates in Shopping, and an optimistic one gets punished twice, first at checkout and then in your return rate.
Structure comes second and it is mostly about honesty in reporting. Brand search in a market this size can look like a triumphant account when it is really a tax on demand you already earned through Instagram, PR or a stockist. We separate brand, non-brand and competitor activity into distinct campaigns with their own budgets and their own targets, so the non-brand number — the one that says whether Google is actually growing the business — is visible every week rather than hidden inside a blended ROAS.
Then Performance Max, which in Australian accounts is frequently doing something less impressive than its reporting suggests. We build it with the asset groups and listing groups split by contribution margin and weeks of cover rather than left as one bucket, we keep brand terms out of it where the account allows, and we mine search terms and Shopping queries weekly to feed negatives and to find the non-brand demand worth building a dedicated campaign around. Bidding targets are set against contribution margin after freight and BNPL fees, not against a revenue number that looks good and loses money on every Darwin delivery.
Australian search demand does not follow the US retail clock, and an account run on northern-hemisphere assumptions leaves money on the table twice a year. Click Frenzy in November, Afterpay Day, Black Friday as an imported but now serious event, and then Boxing Day — which for many local brands is a bigger single day than Black Friday — stack into a ten-week period where CPCs climb and budgets need to be planned rather than defended. On the other side, category demand runs in reverse: swim, outdoor, cooling and travel build from September, and winter categories peak while the US is in high summer. Two further local specifics change the feed work. Free-shipping thresholds and delivery estimates need to be represented accurately in Merchant Center, because a national free-shipping annotation that does not actually apply to WA is both a disapproval risk and a refund generator. And price competitiveness against Amazon.com.au and eBay listings of your own product is worth monitoring in the Merchant Center price reports, since it quietly determines whether your Shopping impressions convert at all.
The same standard of work we run for every client — applied to a Sydney 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 Sydney 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.