Pretty Lavish
Turning intent into order value - at scale.
How Pretty Lavish transformed PDP recommendations from generic placeholders into a precision discovery engine - adding £32 per influenced order without a penny of incremental media spend.
→ See how fabric and silhouette matching surfaces adjacent styles on Pretty Lavish
RecoMelon installs in under a day. First influenced orders in 60min.
No code, no engineering, no catalogue prep.
Message our team on WhatsApp to see how RecoMelon fits your store. Or, run a free PDP audit - see your rec relevance score.
- The Situation
Pretty Lavish didn't have a traffic problem. They had a relevance problem.
As a premium occasionwear brand, Pretty Lavish operates in a category defined by high purchase intent and low tolerance for irrelevance. Shoppers arrive with specific requirements - occasion, silhouette, fabric, sleeve length - and make decisions fast.
Their paid acquisition was performing. Landing pages were optimised. But the moment a high-intent visitor reached a Product Detail Page, the experience broke down - replaced by a generic recommendation block that ignored fabric, silhouette and occasion context entirely.
"Most brands call this 'You Might Also Like.'
In reality, it's: We hope this is close enough.' Hope is not a strategy."
- The Category Dynamics
In occasionwear, nuance isn't preference - it's a dealbreaker.
Pretty Lavish sells into a category where purchase decisions hinge on attributes most recommendation engines treat as interchangeable.
A shopper viewing a satin bridesmaid gown has resolved her occasion, her colour family, her formality tier. What she needs are alternatives that respect that resolution - not products that share a price point or a past purchaser.
- →Fabric handle - satin vs crepe - is not stylistic preference; it photographs and wears differently across venue conditions
- →Sleeve presence is a function of venue dress code, not aesthetic taste
- →Occasion intent (bridesmaid vs. wedding guest) changes the entire purchasing logic
- →When visual continuity breaks, shopper confidence drops - and session depth collapses
"We'd tried three recommendation tools before. None of them understood the category."
- The Intervention
Visual alignment as the recommendation primitive.
RecoMelon re-architected discovery from the SKU level upward. Rather than relying on purchase history or manually maintained rules, every product in Pretty Lavish's catalogue was mapped visually - by what it actually looks like, not what it's labelled as.
This created a structural change in how the PDP functions. Instead of a dead end, the PDP became a continuation of the discovery journey - with 1,046,426 recommendations served across the period and 215,109 gallery impressions recorded.
- The Outcome
Results you can take to a board meeting.
These are not engagement metrics dressed up as revenue numbers. Each figure maps directly to order economics and discovery behaviour - the signals that compound into long-term commercial performance.
Executive interpretation
The £32 AOV delta per influenced order was generated without discounting, bundling, or incremental media spend. It came from alignment - from the PDP recommending products that respected the shopper's visual and occasion intent. This is margin architecture, not a UX enhancement.
- Shopper Intelligence
Dealbreaker Filters: what shoppers are actually telling you.
The Dealbreaker™ Filter data is one of the most underused signals in fashion merchandising. Every filter interaction is a shopper articulating a non-negotiable - not a preference, a requirement.
For Pretty Lavish, colour dominated as the primary decision driver, followed by pattern and silhouette. Red, orange and pink led on colour clicks - unsurprising for a brand with a strong occasionwear palette. Solid construction was the dominant pattern preference by a significant margin.
This data has a second-order value beyond the immediate session: it tells your buying and design teams what attributes are driving or blocking purchase intent across the catalogue.
- Strategic Takeaway
Relevance compounds.
Irrelevance erodes.
A 111.6% increase in recommendation clicks is not a UX win. It is evidence that shoppers - who had previously learned to ignore the module - re-engaged once it became visually coherent. That behavioural shift is the foundation everything else builds on.
And discovery, when it's coherent, compounds:
The uncomfortable question for any brand running paid acquisition into a PDP with generic recommendation logic: how much of your acquisition spend is being undermined by misalignment at the highest-intent moment in the funnel?
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Shopify brands use RecoMelon to fix bounce, grow AOV, and turn PDPs into high-converting discovery tools. Here’s how.
