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I have audited enough Australian ecommerce accounts to know that the problem is almost never the bidding strategy. It is the product data going in, and the checkout sitting behind the click. This is written for an online retailer who is spending real money on Shopping or Performance Max, can see the impressions and the clicks, and cannot see a profitable return. Before you change a single bid, audit the product feed and the checkout, because paying more to send people into a broken checkout only buys you more abandonment. Two faults explain most of the waste I find, and both sit outside the ads interface. The first is a product feed that misrepresents what you sell, so Google shows the wrong product, or refuses to show it, or shows it with the wrong price. The second is a checkout that asks too much of the buyer at the last step. Neither shows up as an obvious error in your campaign report. Check that your feed matches your live site on price and stock, that your conversion tracking records the actual order value, and that a test purchase on a phone completes without friction. If any of those three is broken, no bidding strategy can rescue the account. Shopping campaigns are built directly from your product data rather than from keywords. Your feed is the ad copy, the targeting and the landing page selection all at once. Performance Max runs across all Google inventory from one campaign. It can perform well, and it removes much of the product level control you have in Shopping, which is why a clean feed and accurate conversion values matter even more inside it. Search campaigns still carry brand and high intent buying terms, and Demand Gen handles discovery. For a small store, brand search and a Shopping or Performance Max campaign is usually enough to start. The scale of the shopping surface is enormous. Google's post on its generative AI search features describes the Shopping Graph, "which has more than 35 billion product listings". Your listing either matches what a shopper searched for or it does not appear at all. Put the brand, the product name, the size, the colour and the identifying number in the title, in that order. Use the real GTIN where one exists, and map to the most specific product category available rather than a catch-all. Main images should show the product on a plain background without added text. Price and availability in the feed have to match the site at the moment of the click, because mismatches cause disapprovals and lost trust at the same time. Missing required attributes, duplicate product identifiers, and titles stuffed with keywords all reduce eligible impressions without generating an error you would notice. A weekly feed health check catches most of it. Cart abandonment is the norm rather than the exception. The Baymard Institute records 70.22% as the "average documented online shopping cart abandonment rate". In a market that competitive, conversion rate is the lever with the most room to move. Unexpected costs, forced account creation, a payment method they do not trust, a checkout that is slow or broken on a phone, and doubt about delivery time or returns. The largest single cause is measured: Shopify, citing the Baymard Institute, reports that "the leading reason US shoppers abandoned an online purchase during checkout was extra costs such as shipping, tax, or fees (39%)". Show the total delivered price early, including shipping, rather than revealing it at the final step. State delivery timeframes in plain days, show returns terms without making people hunt, and put reviews where they are seen before the buy button rather than after it. It trains customers to wait for the next sale and eats your margin. Fixing shipping clarity and returns confidence lifts conversion without giving away profit. The market average gives you context for where you start: Shopify reports that "the average global storewide conversion rate hovers at around 1.58%", so a store sitting near that figure has a checkout and offer problem to fix rather than an ads problem. A large retailer can afford to run campaigns the data cannot yet justify, because its volume produces a signal. A store spending a few thousand dollars a month has to keep the campaign simple enough to read the result, and to fund the feed work that makes the spend worthwhile. Move once you have reliable conversion tracking, several hundred conversions a month, and a feed you trust. Before that, smart bidding optimises towards whatever your tracking tells it, and if the tracking is wrong, it learns the wrong lesson quickly. Return on ad spend ignores what the order actually made you. Subtract cost of goods, shipping subsidy, payment fees, the expected return rate for that product category, and then decide whether the customer is likely to buy again. Two stores can both report a return of four, and one of them is losing money on every sale. Work it backwards from margin. If your gross margin is 40%, you break even at a return of 2.5 on ad spend before you count returns, fees or your own time. That number, not a benchmark from someone else's account, is your floor. Work out your real return on ad spend with the actual figures rather than the platform's own reporting, and have your ecommerce tracking set up right in GA4 so the order values match what your accounts show. Add spend to the products with margin and repeat purchase behind them, and to the campaigns already converting profitably. Scale in steps and check the delivered cost per order after each increase, because rising spend on a fixed audience usually means rising cost per acquisition. Hold back the products with a high return rate, the sizes and colours that generate support tickets, and any campaign whose conversions depend on a discount that you cannot repeat. You can see how this played out for a retail client, where the fix was feed and checkout work rather than a bigger budget. One campaign for every product means one budget for products with completely different margins. Split by margin and by best seller versus the rest, and you can decide where the money goes instead of letting the platform decide. Repeat customers flatter your reported return because they were going to buy anyway. Track new customer acquisition separately, and accept that the honest cost of a new customer is higher than the blended number suggests. Online retail keeps expanding, with Shopify reporting that "In 2025, ecommerce will account for 20.5% of worldwide retail sales", which means more competitors bidding on the same shoppers, not fewer. Before your next budget increase, check the feed and complete a test purchase on a phone. Both checks take an afternoon, and they decide whether extra spend reaches a shopper or a dead end. Let the margin decide, not a percentage of revenue. Work out what a new customer is worth after returns and repeat purchases, then set the budget at what you can afford to spend to acquire one. Both can work, but the feed matters more than the campaign type. A messy feed will underperform in either, and a clean feed usually makes Performance Max considerably easier to judge. The global average storewide conversion rate sits around 1.58%, so a low figure is common, not automatically a fault in your ads. Look at checkout costs and shipping before you change bidding. There is no universal number, because it depends entirely on your margin. A 4x return on a 60% margin product is a very different business to 4x on a 15% margin one. Yes for Shopping and Performance Max. It is where your product data lives, and the quality of that feed directly controls how often your products are eligible to appear. Feed fixes and checkout improvements often show up within a few weeks because they affect whether your products are shown at all. Bidding changes need longer, and you should let them run before judging. Are you doing SEO or Ads? Stop wasting money on ads that are not working. Get advertising that is intentional and measured properly.Why Most Ecommerce Ad Spend Leaks
The two leaks that explain most waste
What to check before touching bids
How Google Ads For Ecommerce Is Structured
Shopping campaigns and the product feed
Performance Max and what it changed
Search and Demand Gen as support
Your Product Feed Is Your Ad
Titles, GTINs and category mapping
Images, pricing and availability errors
The feed problems that quietly cut impressions
The Checkout Problem Behind The Ads
What abandonment data tells you
The five reasons shoppers walk away
Fixing Your Conversion Rate First
Cost, clarity and trust on the product page
Shipping and returns before discounting
Budget, Bidding And Where To Start
Why small stores should not copy big store budgets
When to move from manual to smart bidding
Measuring Profit, Not Just ROAS
Margin, returns and repeat purchase in the maths
The ROAS target that is actually break even
Scaling Without Wrecking Your Margin
Where to add spend first
Segments to hold back
Common Australian Ecommerce Ad Mistakes
Running one campaign for the whole catalogue
Ignoring new customer acquisition costs
Google Ads for Ecommerce FAQ
How much should an Australian ecommerce store spend on Google Ads?
Should I run Shopping ads or Performance Max?
Why is my conversion rate so low?
What is a good ROAS for ecommerce?
Do I need a product feed in Google Ads?
How long before I see results?
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