How Finnish Design Shop Drove Tangible Results with Contextual Personalization
They identified personalization and merchandising as growth opportunities. After comparing different service providers in the domain, they selected Constructor’s solutions, opting to use A/B testing to validate their effectiveness.
Finnish Design Shop integrated Constructor’s Search, Browse, and Recommendations features into its online store. Customers’ perceived page load speeds and search engine visibility were finely optimized. As a result, Finnish Design Shop saw measurable lifts in conversion rate, revenue per visitor (RPV), and add-to-cart behavior within a few weeks of A/B testing.
The team credited Constructor’s data-driven Proof Schedule and, later, the Continuous Optimization Program (COP) with helping them build trust in the data, react more quickly to shopper intent, and establish a scalable foundation for long-term global growth.
They identified personalization and merchandising as growth opportunities. After comparing different service providers in the domain, they selected Constructor’s solutions, opting to use A/B testing to validate their effectiveness.
Finnish Design Shop integrated Constructor’s Search, Browse, and Recommendations features into its online store. Customers’ perceived page load speeds and search engine visibility were finely optimized. As a result, Finnish Design Shop saw measurable lifts in conversion rate, revenue per visitor (RPV), and add-to-cart behavior within a few weeks of A/B testing.
The team credited Constructor’s data-driven Proof Schedule and, later, the Continuous Optimization Program (COP) with helping them build trust in the data, react more quickly to shopper intent, and establish a scalable foundation for long-term global growth.
The Finnish Design Shop team had identified merchandising, localization, and scalability as key areas and processes they wanted to improve. They needed a new technical solution that could handle their existing challenges more efficiently, particularly in supporting multiple languages, country-based requirements and regulations, and other international retail-related complexities that were previously difficult and time-consuming to manage.
They simultaneously had a business initiative aimed at improving their conversion rates and creating more seamless and personalized shopping experiences for their users. They had identified dynamic, contextual personalization as a required capability for the new platform that would help them achieve these goals and transform their previously static site experience into a dynamic, highly personalized product discovery journey that adapts to user behavior and trends, while ensuring the site is optimized for their business needs and KPIs.
The Finnish Design Shop team had identified merchandising, localization, and scalability as key areas and processes they wanted to improve. They needed a new technical solution that could handle their existing challenges more efficiently, particularly in supporting multiple languages, country-based requirements and regulations, and other international retail-related complexities that were previously difficult and time-consuming to manage.
They simultaneously had a business initiative aimed at improving their conversion rates and creating more seamless and personalized shopping experiences for their users. They had identified dynamic, contextual personalization as a required capability for the new platform that would help them achieve these goals and transform their previously static site experience into a dynamic, highly personalized product discovery journey that adapts to user behavior and trends, while ensuring the site is optimized for their business needs and KPIs.
For Finnish Design Shop, it was the Proof Schedule that sealed the deal.
During this data-driven, low-risk pilot, data scientists from both teams analyzed Finnish Design Shop’s search logs, merchandising gaps, and real shopper behavior. This revealed promising potential in ranking optimization and uncovered missed revenue opportunities.
Once Constructor’s algorithms took behavior and contextual awareness into consideration, rankings reflected both precise and attractive results, greatly improving the chances users will convert on an item.
For Finnish Design Shop, it was the Proof Schedule that sealed the deal.
During this data-driven, low-risk pilot, data scientists from both teams analyzed Finnish Design Shop’s search logs, merchandising gaps, and real shopper behavior. This revealed promising potential in ranking optimization and uncovered missed revenue opportunities.
Once Constructor’s algorithms took behavior and contextual awareness into consideration, rankings reflected both precise and attractive results, greatly improving the chances users will convert on an item.
In selected highly competitive regions, Finnish Design Shop achieved measurable increases in browse revenue (+26%) and add-to-cart-to-purchase rate (+45%). The furniture and home decor brand secured a 13% revenue uplift on their Finnish storefront, thanks to a new Search reranker model that was trained with their regional user behavioral data for Finland.
Alongside these regional optimizations, Finnish Design Shop has continued to advance its personalization strategy, experimenting across the entire shopping journey.
Overall, the Constructor solution unlocked the ability to experiment quickly — i.e., to A/B test merchandising changes, react to trending products, and optimize for regional intent.
In selected highly competitive regions, Finnish Design Shop achieved measurable increases in browse revenue (+26%) and add-to-cart-to-purchase rate (+45%). The furniture and home decor brand secured a 13% revenue uplift on their Finnish storefront, thanks to a new Search reranker model that was trained with their regional user behavioral data for Finland.
Alongside these regional optimizations, Finnish Design Shop has continued to advance its personalization strategy, experimenting across the entire shopping journey.
Overall, the Constructor solution unlocked the ability to experiment quickly — i.e., to A/B test merchandising changes, react to trending products, and optimize for regional intent.
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