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Monica Vinader Lifts Revenue with Geo-Personalized Ranking Powered by Constructor

5%
conversion lift
Industry
Jewelry
Headquarters
Holkham, Norfolk, United Kingdom
Solutions
Search, Autosuggest, Browse, Merchant Intelligence, Recommendations
5%
conversion lift
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Summary

Executive Summary

Read on to learn how Monica Vinader, the award-winning British luxury jewelry brand, elevated revenue and customer experience with Constructor:

  • Drove measurable business impact with a +2.6% lift in Purchase Conversion from their initial implementation
  • Boosted US performance with a 5% conversion lift through geo-personalized ranking, while keeping UK results steady
  • Expanded optimization to recommendations with a 30% recommendation engine conversion improvement

About Monica Vinader

Monica Vinader is an award-winning British luxury jewelry brand known for its contemporary, everyday designs and sustainability leadership.

Serving customers worldwide through its online store and physical boutiques, Monica Vinader is dedicated to creating personalized shopping experiences that reflect each customer’s unique style and preferences.

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monica-vinader-about@2x
monica-vinader-about@2x

Serving customers worldwide through its online store and physical boutiques, Monica Vinader is dedicated to creating personalized shopping experiences that reflect each customer’s unique style and preferences.

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The Challenge

Shopping Differences in Two Distinct Markets Created an Idea for Optimization

As a global brand, Monica Vinader has long understood that shoppers in the UK and US shop differently — from the types of jewelry they love to the price points they feel comfortable with.

However, their previous approach to ranking browse and search results used a single ranking model trained on combined UK and US data, with a heavier weighting on the UK, which represents the majority of overall traffic. This allowed the model to have more overall data to learn from — an important advantage when smaller regions have little data — but it missed important regional differences in shopping behaviors.

Mark Dougall, General Manager of Business Systems, explained:

"When we looked at our top-performing products, it was clear that what resonated in the UK wasn’t always the same in the US. We knew a single approach probably wasn’t letting the US perform to its full potential."

In particular, Monica Vinader saw signs that higher-value pieces were being pushed down in US results due to the influence of the larger UK dataset. As a significant secondary market with a clear appetite for higher price points, the US market was a clear opportunity to test a more tailored approach.

The Solution

Testing a Dedicated Geo-Personalized Ranking Strategy

Together with Constructor, Monica Vinader designed a new experiment to close this gap.

monica-vinader-wide@2x

Rather than just creating a single global ranking, Constructor trained its algorithm with data marked by the region it came from so it could learn how shoppers shop differently in every region, and could account for these differences in product rankings, even when knowing nothing else about a shopper.

This work was part of Monica Vinader’s Continuous Optimization Program — an ongoing experimentation program designed with Constructor to identify opportunities, test improvements, and measure real results. The goal is simple: use data and fresh insights to keep refining the shopping experience in ways that drive clear value for the business and its customers.

In the test: 

  • Control Group (A): Visitors saw results ranked by the combined model as before
  • Test Group (B): Visitors in the UK and US saw results ranked by their dedicated geo model, tailored to local demand and shopping behavior

Mark noted the simple insight that unlocked this change: “Earlier tests tried tweaking a single algorithm with weightings, but it wasn’t enough. The markets were just too different — they needed their own models. Running two algorithms on one index is pretty unique, but it made all the difference.”

The Results

Proven Revenue Lift and Stronger Conversion

Monica Vinader had already seen success from their original Constructor implementation, with a +2.6% lift in conversion.

Building on that foundation, the four-week test ran with a 50/50 traffic split, focusing on boosting revenue per browse user and driving more shoppers from browse to purchase.

+2.6%
lift in Purchase Conversion (initial implementation)
5%
conversion lift (Geo test)

The results in the US were exactly what the team had hoped for, with a 5% initial lift in conversion rate.

The uplift grew even stronger during Monica Vinader’s seasonal sale, when the new ranking model surfaced higher-value items that matched local preferences.

"It’s been a real winning story for us," Mark shared. "We achieved exactly what we set out to do, and the results were even stronger than we anticipated."

In the UK, the results stayed steady — just as expected — confirming that the new approach improved US performance without disrupting what worked well for the UK.

The Results

A True Test-and-Learn Partnership

Beyond the technical results, Monica Vinader values Constructor’s collaborative, experiment-driven approach.

"We’re very data-focused as a business — we don’t just take a partner’s word for it. Everything has to be proven and measured," said Mark. "Constructor not only brings the tech but also challenges us with new ideas, helps us analyze the results, and even tells us when something isn’t a win. That honesty builds real trust."

"We use Constructor as a benchmark for how we assess all our partners," he added. "They help us spot opportunities, design better experiments, and push us to get better — which is invaluable for a small team like ours."

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Moving Forward

What’s Next for Monica Vinader

With the new ranking approach now validated, Monica Vinader plans to roll it out more broadly and is already exploring new experiments with Constructor.

One recent experiment has resulted in a 30% improvement in their recommendation engine conversion rate. They also plan to test Constructor’s AI Shopping Agent to help bring even more personalized experiences to customers online and in-store.

"Constructor has really set the standard for partnership," Mark said. "We’re excited to keep experimenting and see what else we can unlock together."

By tailoring their ranking models to local markets, Monica Vinader proved that even small adjustments in how products are surfaced can drive measurable gains. Backed by Constructor’s collaborative, test-and-learn approach, they continue to raise the bar for what personalized, data-driven ecommerce looks like — for every shopper, in every market.

monica-vinader-next@2x
monica-vinader-next@2x

"Constructor has really set the standard for partnership," Mark said. "We’re excited to keep experimenting and see what else we can unlock together."

By tailoring their ranking models to local markets, Monica Vinader proved that even small adjustments in how products are surfaced can drive measurable gains. Backed by Constructor’s collaborative, test-and-learn approach, they continue to raise the bar for what personalized, data-driven ecommerce looks like — for every shopper, in every market.

Is there room for improvement with your current search vendor?​ Find out.

Reach out to a Constructor Product Discovery Specialist to learn how our testing helps surface gaps so you can make a data-driven assessment.

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