An enterprise data platform, AI data analysis, and democratized data access yield new business insights and better, faster decisions.
An enterprise data platform, AI data analysis, and democratized data access yield new business insights and better, faster decisions.
Data everywhere: The need for a centralized platform
Emil Garrote had a data problem.
There was plenty of it: customer data, transactional sales data, marketing campaign data, ecommerce data, and data on inventory and revenue. But it was scattered across the company on disparate platforms in departmental silos. Data describing the same sale or prospect might differ from platform to platform.
“We had no single source of truth,” acknowledges Garrote, Business Director at Scandinavian women’s-wear retailer Gina Tricot. “That fragmentation made it difficult to trust the accuracy of the data we had and make solid, data-driven business decisions. We were relying on gut feelings far too often.”
And whether you’re determining how many snake-print bell-bottom jeans to stock in each of 145 brick-and-mortar stores, how to allocate a multimillion-dollar ad spend across multiple marketing channels, or which products are selling best on your website, miscalculations can be costly, and you want more than a gut feeling to go on.
We’d always been very satisfied with BigQuery’s performance, and we knew Looker would make it easier for non-technical users to generate actionable business intelligence.
Emil Garrote
Business Director, Gina Tricot
Gina Tricot had been successfully funneling ecommerce data from Google Analytics into BigQuery and accessing it through Looker Studio for several years. Garrote had been especially impressed with Looker’s built-in semantic layer, which shields users from the underlying complexity of the BigQuery database. So he decided it was time to take the next logical step and convert BigQuery into a true enterprise data warehouse.
“We’d always been very satisfied with BigQuery’s performance,” he relates, “and we knew Looker would make it easier for non-technical users to work with data and generate actionable business intelligence.” He also likes the potential that the conversational analytics and machine learning built into BigQuery holds for Gina Tricot.
Speaking the same “data language” yields measurable results
Garrote and his team began linking more and more platforms to BigQuery. Business insights broadened and deepened with each new addition.
With our enterprise data warehouse in BigQuery, we’re all drawing conclusions from a unified set of data, which has expanded our universe of business insights exponentially.
Emil Garrote
Business Director, Gina Tricot
For example, Gina Tricot marketers can now combine data from the customer relationship management platform with data from Google Analytics and various marketing channels, enabling them to get a comprehensive, end-to-end understanding of the customer journey. Moving data in the opposite direction, marketers can combine and activate the data in BigQuery to build audiences and orchestrate more successful targeted omnichannel campaigns.
Though the project is ongoing, it has already borne fruit. Imposing a uniform taxonomy across data from all marketing channels, then combining campaign performance and sales data with Gina Tricot’s own attribution models, contributed to around 1.0 billion SEK increase in year-over-year revenue from 2024 to 2025.
Garrote is now planning the rollout of Google Meridian, a mixed marketing model tool that will enable the company to refine its ad spend even further and set clear KPIs based on a single shared dataset that provides a 360-degree customer view.
In addition, regression modeling powered by BigQuery’s built-in machine learning allows the Gina Tricot team to analyze and predict sales trends and improve inventory forecasting, which affects everything from how much stock to order to how many warehouse staff to hire. Together with Forefront Consulting and an internal cross-functional team, Garrote is now exploring new use cases — including budgeting and revenue forecasting — for the AI and ML models in Google Cloud.
“With our enterprise data warehouse in BigQuery, we’re all finally speaking the same data language and drawing conclusions from a unified set of data,” Garrote adds, “and that has expanded our universe of business insights exponentially.”
Democratizing data with Looker fosters a data-driven culture
Indeed, Looker’s easy-to-use drag-and-drop report editor, templates, and dashboards have leveled the data playing field. Non-technical team members can slice-and-dice data themselves rather than relying on data experts, who are freed up for higher-value work such as complex data analysis and onboarding more users. Looker’s automated reporting and scheduled notifications have furthered data democratization, enabling swift, data-driven decision-making companywide.
“The fashion industry moves fast, and Looker enables us to keep pace,” Garrote continues. “Using the mobile app for Looker, managers in each of our markets can assess historical and real-time sales performance and decide what items to restock while visiting the store in question. That just wasn’t possible before.” And he expects conversational analytics, which allows users to query a database using natural language prompts, to accelerate decision-making even more.
An added benefit of Looker is that it makes querying BigQuery more cost-effective by caching the results from previous queries, thereby eliminating the cost of queries that have already been made. The company has channeled that cost savings, in part, into additional Looker licenses that give even more people access to the tool.
Having a unified data warehouse, an easy-to-use reporting tool, and AI-powered predictive analyses has yielded a new data-first culture at Gina Tricot. We’ve seen a double-digit improvement in return on ad spend and a huge increase in revenue as a result.
Emil Garrote
Business Director, Gina Tricot
One unexpected perk of BigQuery and Looker: They’ve made it easier for Gina Tricot to attract and onboard top-quality talent because they’re so widely used and well-liked among fashion industry data engineers and analysts.
And Garrote expects the benefits of BigQuery and Looker to continue to accrue as he rolls it out to more and more departments. Its success in sales, marketing, and inventory management has generated excitement throughout the company, and he plans to provide buyers, customer service reps, and the ecommerce and finance teams access to Looker in the coming months.
“Having a unified data warehouse, an easy-to-use reporting tool, and AI-powered predictive analyses has yielded a new data-first culture at Gina Tricot,” he concludes. “We’ve seen a double-digit increase in return on ad spend as well as huge increase in revenue as a result — and we’re just getting started.”
Founded in 1997, women’s fashion retailer Gina Tricot has 145 stores across Scandinavia and more than 1,500 employees worldwide. Its website serves customers in 30 European countries.
Industry: Retail
Location: Sweden
Products: BigQuery, Google Analytics, Google Meridian, Looker, Looker Studio
