An e-commerce account taken from 1.7x to 5.13x
How my team turned around an e-commerce store’s Meta Ads in 30 days: research first, small cold tests to find what worked, aggressive scaling of the winners, and cart offers that grew only as long as a shopper stayed away.
An e-commerce store spending heavily on Meta for a 1.7x return, with thin margins after costs.
Researched before spending, ran small cold tests, scaled only the winners, then used warm audiences and timed cart offers to bring shoppers back.
$528,537 in revenue from $102,984 of spend in 30 days, a 5.13x return and more than $250K of monthly profit.
Spending a lot to earn a little
At a 1.7x return, the store was buying revenue at a price that left little profit once product and fulfilment costs came out. Spending more would have grown sales and not much else.
The fix was not one clever ad. It was a process for finding what worked on small budgets, putting real money behind it, and recovering the shoppers who had already shown interest.
Research, test, scale, recover
Customers, competitors and the product’s strongest angles, before any budget was spent.
Four to eight ad sets, three to four variations each, run for three to five days.
Budgets on the winners raised by 2x to 10x, while the losers were switched off.
Retargeting the people who had visited, viewed or added to cart.
An offer that grows only as long as it needs to
| Days since leaving the cart | Offer shown |
|---|---|
| 0–7 days | Free shipping |
| 8–15 days | 10% off |
| 16–30 days | 20% off |
Most shoppers come back with the smallest incentive, so the deeper discounts are only paid to the people who need them.
Why it worked
Test small, scale hard
Short, cheap tests found the winners, and only then did the budget move.
Don’t discount everyone
Escalating cart offers kept margin on the shoppers who would have come back anyway.
Profit, not just revenue
The result that mattered was more than $250K a month of profit, not the revenue figure alone.
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