Scaling Personalization Across 12 European Markets: A Decade of E-Commerce Growth with Miinto
Apparel, Fashion
The Results
Martin & Servera validated Retail Media as a scalable revenue stream using Algonomy’s Recommend™ and DSW, without investing in a costly point solution.
From September 2025 through April 2026, the program delivered strong outcomes across all key metrics.
Placements closer to the point of purchase, such as the search results page, consistently delivered the highest engagement and conversion rates.
The Client
Miinto is one of Europe’s largest fashion marketplaces, connecting shoppers with boutiques and brands across 12 markets, from the Nordics to Southern Europe. At that scale, personalization isn’t a feature decision. It’s an infrastructure one.
For almost a decade, Miinto has run Algonomy’s personalization suite across its full market footprint, using Recommend™, Discover, and Engage to cover every meaningful moment in the shopper journey, from product recommendations to discovery and homepage content.
Twelve markets. Three products. One consistent personalization layer.
The Challenge
Different languages, different shopper behaviors, different catalog structures. Most personalization tools break down under that complexity. The usual fix is to customize market by market, which creates inconsistency and makes it hard to improve anything at scale.
Miinto’s approach, built on Algonomy’s personalization suite, takes a different path.
Rather than managing each market separately, Miinto runs a single, unified personalization stack across all twelve. A unified stack across all markets means the same logic, data infrastructure, and optimization levers, regardless of geography. What gets refined in one market strengthens the others. What works in Stockholm gets tested in Warsaw.
The Solution
1 Discover: Turning Exploration Into Revenue
For shoppers who arrive without a specific destination, Discover surfaces personalized product feeds that turn browsing into buying.
Personalized Accessories PLP based on the customer’s affinity towards sunglasses.
Revenue attribution from Discover, the share of total sales traceable to a discovery interaction, reached 33.65% in Belgium and 31.37% in Norway in a single year in 2025.
Across the mid-tier European markets, including Poland, the Netherlands, France, Italy, and Germany, attribution consistently sat between 22% and 27% that same year.
Even in earlier-stage deployments in the UK, Discover accounted for 17.46% of revenue.
The consistency across markets at different stages of maturity is what the almost-decade of optimization delivers. Discovery isn’t a feature that works for Miinto’s best market. It works for all of them.
2 Recommend™: Closing the Loop at the Conversion Moment
Where Discover captures exploratory shoppers, Recommend™ works at the product and cart levels, surfacing relevant suggestions when a shopper is closest to making a purchase.
Relevant product recommendations at PDP, powered by Recommend™.
Revenue attribution from Recommend™ reached 5.76% in Sweden and 5.39% in Norway in 2025, with most European markets contributing in the 3–4% range that same year.
These numbers reflect what a well-tuned recommendation engine should deliver at this stage of the journey: incremental revenue from shoppers already in purchase mode, adding to baskets that are already forming.
Relevant product recommendations at the Add-to-cart page, powered by Recommend™.
Almost a Decade of Compounding
Ongoing optimization is built into how Miinto and Algonomy operate together.
Strategy refinements on recommendation placements, multivariate testing on discovery configurations, and continuous tuning across markets. The partnership is structured around improvement, not maintenance.
Almost a decade of that work shows in the 2025 results. And that’s what the next chapter is built on.
“We just wanted personalization that actually worked for our customers across every market, without the complexity. Almost a decade with Algonomy has given us the infrastructure, the data, and the partnership to make it real. Today, we have a program that scales across twelve markets, delivers consistent results, and keeps getting stronger.”
Find™
Recommend
Order right
Engage™
Merchandise Analytics
One of the key challenges faced by the client was accommodating factors such as weather, promotions, events, inventory, etc. in sales forecasting, resulting in reduced OTB (open-to-buy).
Decisions on identifying the right products and determining optimal markdown was done based on limited understanding, leading to margin erosion.
Stockouts in some stores and overstocking in others was a common occurrence due to missing single view of inventory across stores, leading to potential loss of sales.
