Dividing products by “type” of stock - own stock/fulfilment/dropshipping - labeling products and calculating the result at the label level. In 90% of cases, items from own stock sell better and more efficiently, so it is worth directing larger budgets there.
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Sale/low stock - labeling the products you want to “push” out of stock and, based on GA4/GAds data, pulling out those products that had the highest effectiveness and then placing them in other campaigns, with a dedicated budget.
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Categories + margin segment - split the account into campaigns based on category and margin segment to match ROAS to a specific margin range. If there are enough conversions on a campaign, then re-segment it so that there are no big differences in the ROAS generated, e.g. 400% and 2000% (the idea is that there should be no campaigns that have few conversions, because a PMAX campaign has nothing to “learn” from).
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Key series - separating from some brand, e.g. Adidas, a key series on sales, which we care most about, and setting aside a separate campaign and budget for it - here an important aspect is also the division into products that are already performing and those that need to be activated, to increase exposure.

TOP Products for reach - separating out, for example, 10% of TOP products from each category and emitting ads for cold traffic, such as Facebook carousel. Thanks to the fact that we have multiple categories in the carousel, we can reach potential audiences with greater efficiency, because we have a mix of categories, e.g. table, chair, dresser - thus we can catch attention by showing a larger range of the portfolio - not just a category, but symmetrically, another point >>
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TOP products from the category/brand - remarketing - a combination of selection of products from the category/brand with a remarketing list, e.g. brand = Adidas, we select all users who have been on the selected brand, for the period of the last x days and have not made a purchase. We show them the brand ad, but in the ad we have only TOP sellers of Adidas.
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TOP Sellers, afterburning sales - increase the exposure of the best products, but with additional conditions, e.g. we take x TOP products, but limit them MAX COS or MIN ROAS so that if we reach an unacceptable level of the ratio of expenses to revenue then the product falls out of the campaign. e.g. Adidas pants current COS 10%, acceptable COS 15% - so we have 5% “air” on COS, which means we can further invest in the product to increase sales.

TOP products from the brand in the recommendation frames on the eCommerce platform - for example, a product LP of the selected brand within which there is a frame/carousel for which we prepare a feed, in which there are TOP 10 - 20 products that sell best
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Exclusion of products, with a small number of variants (mainly for the fashion industry) - if the product does not have a size S, M or L, it is placed in a separate campaign, but you can adjust the methodology to your store
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Exclude products that spend too much money and don't sell, e.g., the product costs 100 PLN , its margin is 10%, it spent 200 PLN , it didn't sell once, it incurred a cost that will have to be made up, and to do it at the product level you would have to sell 20 units, so you need to take some other strategy to make the product start selling, not just spend.
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For stores with offline sales - broadcast a local campaign with TOP products and an invitation to the point of sale, bumping it up with, for example, a special additional promotion when you visit the store
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PREORDERS - singling out products that will appear in the store and linking them to a remarketing group matched to the product in the preorder, e.g. a new product series linked to people who have been on the selected brand/category and have not made a purchase in the last 30 days.

Entering a new advertising channel , e.g. Bing Ads - selecting x products, where x is matched with the amount of budget, e.g. PLN 1000 - you can select TOP 100 products and start “unleashing” the new advertising channel with products that are TOP from the perspective of sales in GA4 or ecommerce engine data.