There is no such thing as the Shopify app stack

Ask ten agencies what a healthy Shopify store runs and you get one list: a reviews app, an email tool, maybe subscriptions. Then you look at the actual installs across a few thousand stores and that single list falls apart. What a store runs depends almost entirely on what it sells, and the gaps between categories are wide enough to change how you pitch.

We read this off the monitored panel: 60,000+ Shopify stores we watch closely, a microscope on the 7M+ store universe we index. This month the panel is deep enough in three verticals to compare their app stacks side by side.

The "standard" Shopify stack is a beauty stack. Everywhere else, half of it is missing.

Reviews are table stakes, until they aren't

Social proof is the one app category everyone assumes is universal. It nearly is in beauty, where 97% of monitored stores run a reviews app. In apparel it is still the norm at 89%. In food and beverage it drops to 61%: four in ten stores selling snacks, coffee, and sauces are collecting no structured reviews at all.

That 61% is the number to keep. A food and beverage brand with no reviews app is not a laggard you need to convince that reviews matter. It is a store one integration away from proof it does not have, in a category where repeat purchase lives or dies on trust. That is a first-meeting talking point with a fix attached.

Email splits the serious from the hobbyist

Owned marketing is where the verticals separate hardest. Beauty runs email or SMS at 81%. Apparel sits at 49%, almost exactly half. Food and beverage trails at 22%.

Read the apparel number as your opening. Half of monitored apparel stores are running paid acquisition into a store with no owned channel to catch the buyers who do not convert on the first visit. Every dollar of traffic they buy leaks. An agency that leads with retention math, not a redesign, is speaking to a problem the owner can already feel in their ad spend.

Subscriptions is a beauty word

Then there is subscriptions, which most decks treat as a lever any store can pull. In practice it is one vertical's game. Beauty runs it at 35%. Apparel is at 1%. Food and beverage is at 0%.

That is not a gap to sell into. It is a filter. Pitching a subscription build to an apparel or food brand is pitching against the grain of the entire category, and the install data says the category already voted. Save it for beauty, replenishment, and the food brands with a genuine consumable, and spend the pitch elsewhere for everyone else. Knowing where a lever does not apply is worth as much as knowing where it does.

Why the stacks diverge

None of this is random. The stack tracks the economics of the category. Beauty sells small, repeatable, high-margin products, so reviews, owned marketing, and subscriptions all pay for themselves quickly, and the stack fills in. Apparel repeats too, but sizing and returns dominate the problem, so email earns its place while subscriptions rarely do. Food and beverage runs on thin margins and heavy shipping, which is exactly the shape that makes an owner hesitate before adding another monthly app fee, and it shows: the category runs the leanest stack of the three.

For an agency, that is the useful frame. When an app is missing, ask first whether the category economics explain it. If they do, the absence is normal and not a pitch. If they do not, you have found a store behaving unlike its peers, and that is either a store punching above its weight or one with a real, nameable gap. Either way it is worth the call.

What to do with this

The useful version of this pulse is not the averages. It is that "the app stack" is the wrong unit. There is a beauty stack, an apparel stack, and a food and beverage stack, and they want different first conversations.

You do not have to pull install data by hand to run that check. The free store check reads any store's app stack and public signals in seconds, and the product turns the same reads into a weekly list of stores whose stack shows a gap you close. The point of watching 60,000+ stores is not the averages. It is being able to tell one store, in one category, exactly what it is missing.