Download websites that use RichRelevance

About RichRelevance

A row of suggested products under a shopping cart looks like a small feature and is almost always a large contract. RichRelevance belongs to the generation of Personalisation tooling that retailers bought as a separate engine, fed with catalogue and session data and wired into the storefront by an integration team, rather than as a feature that arrived with the platform. Presence of the tag therefore says something about the scale of the retailer before it says anything about the retailer's taste in software.

RichRelevance has been selling that engine since 2006 under a Proprietary license, which places the product in the period when personalization carried its own budget line and its own procurement cycle. Brands from that period tend to have complicated afterlives. Ownership changes, product lines are folded into larger suites, and the original name survives in the markup long after the contract that paid for it was renegotiated or dropped. Our detection is a fingerprint match on the delivered script and not a check against a subscription list, and the difference is worth stating plainly.

As of October 2026 we record RichRelevance on 9 sites. A modest figure here is expected rather than surprising, because the buyer set was never wide and an engine of this kind was sold to large catalogues rather than to the long tail. Detection is pushed down further by tag managers, which hide the vendor behind a container script, and by server side rendering of recommendation blocks, which returns finished markup with nothing vendor specific left in it.

The researchers who want this list are usually building a displacement map. Vendors selling newer personalization and site search products want retailers already convinced that recommendations are worth paying for, since the budget exists and only the supplier is in question. Agencies want the same list to scope migration work. Analysts tracking consolidation in retail technology want to watch whether the detected footprint shrinks as contracts expire, which is only visible if the population is measured repeatedly rather than sampled.

None of that is answerable one domain at a time. Checking a retailer you already know about tells you what that retailer runs, while the export tells you the shape of the whole detected population and lets you filter it by ecommerce platform, by country and by whichever analytics and tag management stack sits alongside. Personalization engines rarely appear alone, and the neighbours in the stack are often more informative than the engine itself.