Meta retargeting pools: split by recency or one big 180-day audience?

For a $60 to $120 product with a 3 to 4 week consideration window, I'd split the audience. A single 180-day pool gets dominated by people who glanced at one category page six months ago, and they soak up frequency while the hot cart abandoners get a diluted share. Splitting into 0-7, 8-30 and 31-180 days lets the newest group see the sharp offer and the older group get a cheaper, broader message.

The one place I'd give ground is low traffic. If a store only gets a few thousand visitors a month, the small pools may be too thin to deliver, and in that case one combined pool is probably the safer setup. What minimum pool size did you use before splitting?

Overlap is what I'd measure first. A 0-7 day visitor also sits in the 0-30 and 0-180 pools, so each pool claims the same sale and per-pool ROAS looks better than it really is. The fair way to judge the split is total conversions at the same spend, compared against a single combined pool.

Agreed on comparing total conversions. The quickest check is to add up the purchases each retargeting ad set reports for a month and set that next to the orders in your store backend for the same dates. In a split setup the platform total usually runs well above the real count, and that gap is the double-claimed sales. If a client sees 140 Meta purchases against 95 orders, they'll ask about it, so have that reconciliation ready before you call the split a win.

What actually fixes it is exclusions, not just the date windows. Exclude the 0-7 and 8-30 day audiences from the 31-180 day pool, so the cheap, broad message only reaches people who haven't been in the sharper groups. Without that, the older pool keeps serving the same people, and frequency climbs on exactly the audience you wanted to keep cheap.

Double-counting isn't the whole story. Meta's default attribution includes 1-day view-through, so someone who merely saw a retargeting ad and then bought direct still gets credited to Meta. That inflates the platform number even when your exclusions are clean. If you filter to click-only purchases, you'll see how much of the gap is real overlap.

Agreed, and that's what keeps the reconciliation honest. Your store backend can't see views at all, so view-through purchases will always show up as a gap, even when your exclusions are perfect. For retargeting that's often the biggest slice, since a lot of those people were coming back anyway. Pull click-through purchases first and compare those to backend orders, then treat the view-through remainder as its own estimate instead of folding it into the split's score. And don't assume the default attribution window is the same on every account anymore. Check the setting on the ad set itself.

Lock the attribution setting before the test starts, and keep it identical for the split and the combined pool. Judge only on click-through purchases, and check them against backend orders. A split changes how often people see the ads, and that alone can change how much view-through credit each arm collects. If the split's view share goes up, its platform ROAS looks better even when real sales didn't move. Fixing the window means you're comparing audience structure and not reporting settings.

Date basis matters here too. Meta can credit a conversion to the day someone interacted with the ad, while your backend counts it on the order date, so the two totals drift apart in the days after a test ends. Wait out your longest click window before you read the click-through numbers, and pull store orders on the same day basis Meta uses. If you don't, the arm that ran through the end of the month can look better or worse just because of when its data was pulled.

I wouldn't match the backend to Meta's day basis. I'm not sure Meta dates conversions the way that's described, and a platform reporting setting is a shaky thing to anchor to anyway. Keep the backend as the source of truth and pull both arms on the same cutoff date, once the longest attribution window has closed.

Agreed on the cutoff. The backend side needs one more adjustment: net out cancellations and refunds. Meta counts the purchase when the thank-you page loads, not when the money settles, so a $90 order refunded two weeks later still sits in the platform number. On a $60 to $120 product with a normal return rate, that can be a few percent of conversions, which is about the size of the differences people are arguing over.

Pull refunds and cancellations for the same cutoff, subtract them from the backend count, and then compare the arms. If the gap shrinks a lot, the split wasn't inflating anything. It was just counting sales that later went back.

Netting refunds makes sense, but how much it matters comes down to how fast returns come back in your store. If most refunds land within a week or two, a cutoff just past the attribution window already captures them. If returns trickle in for a month, the last refunds will be missing from one arm and not the other, so the comparison is still off.

It's also worth checking whether refund rates differ by arm. Impulse buyers from the 0-7 day group may return more often than people in the broad pool, and that could make the split look better on gross orders and worse on net.