Transitioning to a genuinely data-driven model as a company isn’t just a technical move. It’s a complete cultural transformation that requires moving away from “we've always done it this way” and toward a culture of continuous experimentation.
In this article, based on a breakout session at D-Congress 2026 co-hosted with our partner Contentsquare, we explore what this transformation looks like in practice with real-world examples from two leading Nordic retailers.
The 4 Foundations of a Data-Driven Organization
Building a data-driven culture is a challenge for many businesses that requires alignment across several departments. Skipping a step in the process is a huge reason why so many data initiatives ultimately fail. There are four steps that are said to be the foundation of a data-driven organization. These are:
1. Break Down the Data Silos
A common hurdle in large organizations is that data often ends up in silos. Purchase data sits in one system, marketing data in another, and technical performance data in another. Ask any digital commerce professional whether they recognize this pattern, and most will likely say yes.
To gain a holistic view of the customer journey, teams need to see how different touchpoints interact, where customers drop off, and what happens before and after each engagement moment. It’s simple really, as data that cannot be connected cannot be acted on.
2. Run the Initiative Top Down and Across
For a data-driven approach to really take root, it must be supported from the top down and run across several departments. If a CEO does not advocate for data-backed decision-making, reports will often fall flat and fail to influence actual business strategy. When leadership actively advocates insight-driven decision-making, it changes the culture of the entire organization and empowers employees to see themselves as contributors rather than just followers.
3. Democratize the Data
When a single analyst is responsible for all reporting, the impact of it is easily lost. Lowering the barrier to data access changes this dynamic. When e-commerce managers, content specialists, digital merchandisers, UX designers, and product owners can each interpret data relevant to their own work, they take ownership of their metrics in a way that top-down reporting never achieves. Each person owns their delivery and can make informed adjustments without waiting for a centralized report.
4. Build a Two-Stream Workflow
Organizations that use data well don’t just collect it but create a rhythm around it. They tend to follow a “two-stream” approach, including
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A short-term stream for weekly, real-time decisions and quick adjustments
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A long-term stream for strategic thinking and meaningful site improvements
Each stream serves a different purpose and involves different people, which is the whole point. Mixing them will lose you clarity, as strategy and operations will get rushed. But when you follow both individually and use the data from each stream with intention, internal teams can instantly act faster and think smarter. Click here for a quick summary.
Case Study: Home Furnishing Nordic (HFN)
Home Furnishing Nordic (HFN), the Nordic brand behind major brands including Trademax, Chilli, and Furniturebox, has integrated data so deeply that it now drives the organization’s entire development roadmap. The company’s journey illustrates what happens when an organization fully commits to development from the top down.
Building a CRO Team
The most consequential decision HFN made was to establish a dedicated conversation rate optimization (CRO) team. The common goal across the organization was to prioritize all developments based on data-driven insights.
The CRO team brings together an analyst, a CRO specialist, and a head of CX, who work through a structured process together with an internal UX designer, analyzing site data for anomalies and drop-off points, forming hypotheses, building test variants, and measuring results in terms of revenue per session (RPS). Only “winning” tests that show genuine uplift progress to full development.
This matters because research says that most ideas turn out to be wrong. HFN's early hypothesis was that roughly 1 in 3 tests would be a winner. In practice, fewer than half of all tested changes yield positive results, meaning that a big portion of development effort in organizations that don't test is simply wasted.
An Increase in Conversation Rate
By strictly adhering to a data-backed strategy and measuring everything using Revenue Per Session (RPS), HFN has seen more than a 30% increase in its overall conversion rate over the past year. This number represents the cumulative impact of a disciplined, systematic process applied consistently over time, making the case for investment in data infrastructure far more powerful than any theoretical argument.
Case Study: Lindex
For Nordic fashion retailer Lindex, data has become the ultimate tool for resolving internal conflicts and responding to the rapid shifts not only in the fashion industry but also in channel optimization.
The Benefit of Using Behavioral Data
One of the most practically impactful shifts at Lindex has been the use of behavioral data (most specifically, visualizations of customer journeys, funnels, and heatmaps) to resolve internal disagreements.
The start page is an everlasting battleground for retail organizations. Marketing wants to promote a current campaign, purchasing teams for different business areas want their category to be fronted and centered, the e-commerce team has a view on what converts, etc. But without data, who wins the conversation comes down to seniority and persistence.
With behavioral data, on the other hand, the conversation changes. Showing a purchasing or marketing colleague visually exactly how customers move through a page, where they stop, what they ignore, and where they abandon, produces a different type of discussion. It becomes harder to argue for a placement that the data shows customers are simply not engaging with.
The Swimwear Hype: Responding to Real Demand
Fashion retail runs on long planning cycles with seasonal decisions made months in advance based on past patterns and industry norms.
At one point, Lindex was faced with the fact that all trends were pointing towards denim being the new big thing. But Lindex’s actual data told a completely different story. Swimwear had the highest “add to bag” rates, indicating customers were clearly engaging with swimwear more than denim.
The team had to make a choice: follow what the trend was saying and what everyone else was focusing on or follow the customer. Ultimately, they chose the customer, so they updated their start page, adjusted their communications, and shifted all focus to where demand was actually happening. This resulted in a huge sales uplift that going with the denim plan would have missed, while also saving time and increasing conversations.
Being responsive, looking at the data, following customer behavior, and acting on it is one of the most valuable capabilities in digital commerce. Lindex has since then opened up for the e-commerce team to act on data rather than trends.
The Two-Stream Model in Practice
Lindex has applied the two-stream model to its data workflows:
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The company has weekly sessions in place that bring together purchasing, paid media, and channel teams to review current performance data and make immediate adjustments. This makes it easier to respond to what is happening in the season right now.
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Longer-cycle working meetings are also part of business at Lindex, where developers, product owners, UX designers, and e-commerce managers bring together behavioral data and business priorities to form hypotheses and create a prioritized development backlog.
Separating these two rhythms prevents the company’s short-term issues from getting in the way of long-term priorities.
The Final Word: Culture is Everything
Both case studies in this article show the same underlying pattern: organizations that invest in data foundations, testing disciplines, and a measurement culture not only see significant improvements but also structural advantages that emerge over time.
The best results are the outcome of small, validated improvements made possible by an organization that has built the habit and infrastructure for continuous experimentation.
The retailers that will succeed the most going forward aren’t the ones with the largest budgets or the most advanced AI tools. They’re the ones who have made or will make the largest cultural change internally, building teams and processes that know how to learn from data, challenge their own assumptions, and act on what the evidence shows.
Key Takeaways
Building a data-driven organization rests on four core steps:
- Break down data silos
Ensure teams have a clear, shared view of how different touchpoints connect and influence each other. - Drive the initiative from the top
Secure strong executive sponsorship to embed a data-driven mindset across the organization. - Democratize access to data
Lower barriers to data access and empower teams to explore, interpret, and act on insights independently. - Adopt a two-stream workflow
Balance short-term actions with long-term initiatives to enable faster, more effective use of data.
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