The pet products that pay off on Amazon FBA, per Hugo Galvao

Por Maxime Dervaux 5 Min de leitura
Hugo Galvao

Amazon’s storage fees for third-party sellers run higher than Mercado Livre shippings in Full, and that gap alone convinces many sellers to skip the platform altogether. Hugo Galvao de Franca Filho, founder and director of Enjoy Pets, treats that comparison as only half the picture: the Prime badge tends to lift conversion enough to offset the extra storage cost, but only for items that actually move fast once they arrive at Amazon’s warehouse.

The mistake most pet sellers make is deciding which marketplace to prioritize based on overall traffic instead of how a specific product behaves once it is sitting in a fulfillment center. A listing that performs well on Mercado Livre does not automatically earn its storage fee back on Amazon, and the two questions are answered by different numbers.

What Amazon actually charges pet sellers

Amazon’s commission for sellers typically runs between eight and fifteen percent depending on the category, calculated on the full sale price the buyer pays. That commission comes separate from fulfillment by Amazon charges, which cover storage, picking, packing, and shipping and are billed by weight and by how long the product sits before it sells.

A seller’s plan also carries its own fixed cost: a small monthly fee under the professional plan or a per-unit charge under the individual plan, neither of which shows up when comparing commission percentages alone. None of these charges are wrong on their own, but stacked together they can turn a listing that looked profitable at the commission line into one that barely clears its costs once storage and plan fees are added back in.

Mercado Livre, by contrast, now prices its own fulfillment logistics by weight, dimensions, and price band rather than a flat category rate. The two platforms are not competing on the same fee structure, so comparing a single commission percentage between them tells a seller very little about actual margin.

The turnover test most pet listings skip

A nine-kilogram bag of dog food that takes sixty days to sell accumulates two months of Amazon storage charges before it ever generates revenue. Hugo Galvao comments that the same bag, moved through Mercado Envios Full in the same window, carries a lower storage cost even though the commission percentage might look similar on paper.

That gap grows with every extra week a product spends waiting. Fast-moving items barely touch storage fees before they leave the warehouse, while slow movers accumulate them like rent on unsold stock. Bulky, low-ticket staples are the ones most exposed to this pattern, since their per-unit margin is already thin before any storage fee is added. Turnover speed, not category or price alone, decides which side of that math a listing lands on.

Where the Prime badge pays for itself

Hugo Galvao de Franca Filho explains that products with a ticket above roughly R$100 and a track record of selling quickly are the ones where the Prime badge earns back its storage cost through higher conversion, since Prime shoppers tend to buy faster once they trust the delivery promise.

Turnover data per product line, reviewed before deciding where to list an item, tends to separate the two groups clearly: fast-selling, higher-ticket items such as premium supplements do well on Amazon, while bulkier, slower staples such as large litter bags fare better on marketplaces with lighter storage costs.

Deciding channel by channel, not by hype

Treating whether to sell on Amazon as a single yes-or-no question misses how differently each product performs once fees, storage time, and conversion are counted together. The right question is which specific listings, not which entire catalogue, belong on which channel, and that question has a different answer for a bag of litter than for a bottle of joint supplements.

Hugo Galvao puts the discipline simply: a pet catalogue rarely fits one marketplace strategy, and the sellers who grow fastest are usually the ones willing to run that math product by product instead of picking a single platform and hoping the average works out.

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