Five stars with no comment at all says almost nothing about a product. A three-star review with a sentence explaining what disappointed the buyer, on the other hand, carries more useful information for anyone managing a pet catalog than a dozen perfect scores with no detail whatsoever. That’s the distinction Hugo Galvao de Franca Filho, founder and director of Enjoy Pets, points to as the reason why so few operations actually make the most of what product reviews have to offer.
Most treat this feedback as a reputation showcase, something to display for the next buyer, and forget that it also holds a detailed map of what’s working and what needs adjusting. That map, built by people who already bought and tested the product with their own pet at home, rarely shows up with the same precision in any other data source available to the store.
The comment matters more than the numerical score alone
Two three-star reviews can mean completely different things: one complains about packaging damaged in transit, and another mentions that the pet simply didn’t like the flavor. Looking only at the average score hides that difference, treating two problems of a completely different nature as if they were the same thing within a single consolidated number on the store’s dashboard.
Reading the comment behind the score, according to Hugo Galvao, is what allows separating a shipping problem, which calls for a logistics fix, from a problem with the animal’s acceptance of the product, which may call for a formula review or a more precise description of the pet profile that item is suited for within the catalog.
A word repeated across several reviews becomes copywriting material
When different customers use the same word to describe a benefit, like “easier digestion” or “shinier coat,” that expression carries weight no marketing team could invent on its own, because it comes from real experience reported by someone with no incentive to praise the product beyond genuine satisfaction.
Enjoy Pets builds this kind of recurring expression directly into the description of similar products, drawing on language that has already proven to resonate with other pet owners. Hugo Galvao de Franca Filho considers this practice more effective than any text created in isolation by the internal team, with no grounding in experience reported by someone who actually bought that item.
A recurring complaint calls for investigation, not a standard reply
Responding to each negative review in isolation solves that specific case, but it misses the bigger opportunity when the same problem keeps showing up in later reviews with no one connecting the dots. Investigating a pattern requires reviewing feedback in batches periodically, not just replying to each one the moment it arrives on its own.
Hugo Galvao reinforces this point by noting pet operations that only discover a structural problem with a product months after it starts appearing in comments, simply because no one reviewed the full set of reviews together before that specific moment when the complaints had already piled up too much to ignore.
A catalog evolves faster when it listens to people who already bought
Every decision about keeping, adjusting, or removing a product from the catalog gets more solid when it crosses the team’s intuition with real information left by people who tested that item in practice. Ignoring this free data source means deciding with less information available than the operation actually has access to.
For Hugo Galvao de Franca Filho, this habit of reviewing feedback in depth, beyond the isolated numerical score, is what separates a pet catalog that keeps evolving from one that repeats the same mistake by not listening to what the customer has already recorded after the purchase.
