How John Beerens' Top Product Recommendation Placement Hit an 11% Click-to-Order CVR
JohnBeerens.com has been delivering beauty since 2004, built by Dutch hairstylist John Beerens into one of the best-known beauty retailers in the Netherlands. The store carries premium hair and beauty brands for two audiences that shop very differently: consumers buying for themselves, and the salon professionals and distributors who order through a separate B2B login.
The operation has scaled with that demand. The company moved into a 6,000 square meter facility in 2019, extended beyond the Dutch market into France and Germany, and was voted the most popular website in the Netherlands in the fashion and beauty category. Alongside next-day delivery on orders placed before 23:00, it runs live shopping streams and the JB Club loyalty program.
11.1%
Best Placement Click-to-Order CVR
9.9%
Store-Wide Click-to-Order CVR
The Discovery Problem at Catalog Scale
A premium multi-brand beauty catalog serving two audiences across three markets creates a product discovery problem that grows with every brand added. A consumer looking for a shampoo and a salon owner restocking the same line want different things from the same category page, and no team can hand-pick what surfaces for each of them in three languages.
That puts real weight on the recommendation blocks around the store, the modules that suggest what to look at next. Their job is to narrow a large catalog down to the few products a given shopper is most likely to buy.
The Solution
John Beerens runs Kimonix product recommendations across the store, with recommendation blocks at the points in a visit where a shopper is deciding what to look at next. Each block works from what that shopper has shown interest in rather than serving one fixed selection to everyone.
Kimonix reports on each block separately. Instead of a single store-wide recommendation figure, every placement carries its own click-to-order rate, which is what makes it possible to tell a block that is genuinely earning orders from one that is only collecting clicks.
Explore Kimonix product recommendations →
The Results
Across a 30-day window, clicks on Kimonix product recommendations converted to orders at a blended 9.9% across the store. The strongest single placement reached 11.1%.
Key Takeaways
11.1%
Best Placement Click-to-Order CVR
Across a 30-day window, clicks on Kimonix product recommendations converted to orders at a blended 9.9% across the store. The strongest single placement reached 11.1%.
- Click-to-order is the honest way to judge a recommendation placement: it measures what the placement itself controls rather than the traffic around it
- Per-placement reporting is what makes that judgment possible. A single store-wide recommendation figure hides how differently individual blocks perform
Frequently Asked Questions
What results did John Beerens get from Kimonix product recommendations?+
Over a 30-day window, clicks on Kimonix product recommendations converted to orders at 9.9% across the store, and the best-performing single placement reached an 11.1% click-to-order rate. Both figures measure the same thing: of the shoppers who clicked a recommended product, the share who went on to order.
How does Kimonix decide which products to recommend?+
Each block draws on what the individual shopper has shown interest in during their visit rather than showing every visitor the same fixed selection, and it can be weighted toward products the store has reason to push, such as items that are in stock and worth selling. On a catalog spanning many premium brands, that weighting is what keeps a recommendation relevant to the shopper and useful to the merchant at once.
Why do some product recommendation placements convert better than others?+
Two things feed into it. Where the block sits in the journey matters, because a shopper deep in a product page is at a different point of intent than one who has just landed. So does how the block is configured, meaning what it has been told to prioritize. The two are worth separating, because the fix differs: one is a placement decision, the other a configuration change.
How should a store measure whether its product recommendations are working?+
Judge them on click-to-order, then be careful what you compare that number against. There is no standard definition of the metric across vendors, and the attribution window alone can move it a long way, so the same figure quoted by two tools rarely means the same thing. The comparison that actually tells you something is between placements inside your own store, measured the same way.
Do product recommendations work for large multi-brand beauty catalogs?+
Yes, and catalog scale is the reason they matter rather than an obstacle. The more brands and variants a store carries, the less likely a shopper is to reach the right product by browsing alone, and the more a well-placed recommendation is doing. John Beerens has sold premium hair and beauty brands since 2004, to consumers and salon professionals across the Netherlands, France and Germany, and converts 9.9% of recommendation clicks into orders.