Download websites that use SiteVibes

About SiteVibes

Star ratings, shopper photographs and the little counter saying how many people bought an item this week are rendered by somebody, and it is rarely the shop platform itself. Social proof arrived as a bolt-on layer, sold separately, dropped into a product template as a script and configured through somebody else's dashboard. SiteVibes works that way in the Analytics space, covering reviews, user generated content, loyalty and referral mechanics from a single JavaScript embed.

The layer is easy to buy and easy to swap, which shapes what a detection means. A merchant running one has decided that conversion on the product page is worth paying for, and has usually bought other tools on the same reasoning, from search to recommendations to email flows. A retailer with no such layer at all is a different kind of prospect. Presence or absence of a widget vendor sorts storefronts by how actively they are being optimised, which is a more useful axis than size.

As of October 2026 we detect SiteVibes on 24 sites, sold under a Proprietary licence since 2015. Widget counts move faster than platform counts, because replacing a review provider is a template edit rather than a migration, and contracts in that market are short. A figure like this describes a moment, and the date attached to each record is doing as much work as the number itself.

Detection here is also partial by nature. Scripts of this kind are frequently loaded through a tag manager, deferred until interaction, or injected by the commerce platform's own app framework, and every one of those paths can hide the vendor from a crawl that reads only the delivered markup. Where the embed is direct we see it clearly. Where it is wrapped, we may not, so the published count is better understood as a lower bound than as a census.

The reason to want a list rather than a lookup is that the interesting question is always relative. Which storefronts run this layer, on which commerce platforms, in which countries, and what else sits beside it. Answering that from single lookups means guessing which domains to check, which quietly builds the answer into the sample before the work starts. An export removes the guess and lets the filtering happen after the data is in hand, which is the difference between a study and an anecdote.