Elevated IKEA store performance through shared, data-driven KPIs

Decrease in validation time
38,4%
Every improvement must undergo a validation process before publication. By releasing the first iteration, we cut this time by 38.4%.
Iterations of improvement input form for store employees boosted submissions by 181%.
Every published improvement is backed by proven KPIs. By personalising the whole experience and motivating store employees to share, we drove implementation of improvements by 27%.
increase in idea improvement
181%
rise in implemented ideas
+27%
Hover over the results to learn more 👀

At IKEA, I worked on an internal B2E tool called Vetlanda - a product that enables store co-workers to submit proven improvements that optimise work processes in franchise stores, drive measurable KPIs, and reduce friction in employee workflows.

MY ROLE
I played a key role in shaping Vetlanda by co-creating the product roadmap with stakeholders and the product team in order to establish a strategic direction and leading the full design lifecycle from research and IA to high-fidelity prototypes, ensuring a seamless and consistent user experience with IKEA’s design system.
Our team's mission was to transform the tool from a mandatory task into a valuable experience that empowers store employees, making it feel purposeful rather than just another tool that needs to be used in their already packed workday.
Problem statement

Employees didn’t engage with the tool due to lack of time, ownership and a complex usage.

Upon joining the team, Vetlanda faced plenty of challenges on a technical and strategic level. Part of the product was happening in Excel sheets, UI wasn't aligned to IKEA’s design system, we were undergoing a backend platform migration... Stakeholders were requesting ambitious features such as AI, personalisation, gamification - without clear data to guide decisions. These constraints pushed us to think strategically and creatively, identifying viable solutions to not only stabilise the product but also set it up for meaningful impact.
End users of the product

Department leads in stores across the world



IKEA stores and their franchisees operate across nearly every continent, with more than 50 stores worldwide. Within these stores, several department leads play a critical role, and these individuals are the primary users of our product.

Given the cultural differences across countries, it was essential to ensure that all improvements are accessible, efficient and adapted to the regional context.

CORE FLOWS OF the PRODUCT
Validation flow

From a fragmented Excel sheet to an integrated in-product workflow

The validation flow begins when a store co-worker submits an improvement to Vetlanda. Three types of users are involved in this process:

‣ Editors
, who review language and accuracy and facilitate communication between co-workers and other validation actors.

‣ Best Practice Leads, who own the process and are responsible for business outcomes, including reviewing data sensitivity, accuracy and alignment with IKEA concept.

‣ Subject Matter Experts, who evaluate improvements from the perspective of their specific domain.

All three roles documented their findings and feedback to others in a single shared Excel spreadsheet, which became increasingly unstructured and difficult to manage over time.
To develop the first iterations, we conducted multiple user interviews and shadowing sessions with all three roles using the existing Excel based process. This research helped us identify five key pain points for each user group. Using these insights, I facilitated a workshop with the full team to align on the findings, surface potential technical constraints, and agree on a path forward.

Stakeholders were part of every stage of the process as they are one of the validation actors as well.
Results of first iterations

Validation time was reduced by 38.4% through the use of Power BI analytics and close collaboration with validation stakeholders.

The end-to-end process, from product discovery through implementation, took approximately seven months.

While validation time was significantly reduced, research revealed a key remaining bottleneck at the start of the validation flow: communication between editors and submitters.

A key improvement to the flow was enabling submission assignment and introducing a centralised logbook overview, shown below.

A key improvement to the flow was:

Enabling submission validation,
Establishing a logbook overview.

Both are shown in the prototype on the left.

Data showed that 70% of submitted improvements were returned to the submitter, with most responses taking over a week.

The most common reasons for this were incomplete form fields and a lack of clarity about what the form was asking for.
To identify opportunities to reduce friction and accelerate delivery, we assessed the first stage of the validation process and the step before: filling out the submission form.
We redesigned the Help Center with a structured sidebar and post-use feedback prompts, resulting in clearer navigation, higher content discoverability, and actionable insights for ongoing improvements.


Submission flow

The answer to further reduce validation time? Submission flow.

Increase in submissions

In one year, we achieved a 181% increase in submitting high quality improvements.

Improving the submission form had a positive ripple effect across the platform. It raised the quality and clarity of published improvements, which built greater trust among store employees.

As confidence grew, more employees adopted existing improvements and felt motivated to submit their own.

AI enhancements of the flow

Our product vision's future for the submission form is AI-assisted submission flow.

The product should help users capture KPIs with minimal manual effort, while ensuring they are complete, valid, and meaningful. AI becomes a copilot inside the submission form:

Extracts relevant information from uploaded PDFs,
Validates entered KPIs,
Highlights gaps and missing fields,
Guides users toward best-practice KPI sets

The goal from a design perspective is frictionless entry and intelligent guidance.
AI could perform three key functions when processing the uploaded PDF:

Extracts relevant data from the document and organises it into the correct form fields;

Evaluates content for accuracy and consistency,

Suggests writing improvements or clarifications where needed to enhance quality and readability.


By integrating AI to extract data from PDFs and improve user instructions and wording, we can significantly reduce the time co-workers need to complete the submission form.
Implement flow

We managed to get people to share, but what about copying improvements?

By refining the submission form and validation process, we enabled store employees to share ideas with ease and empowered validation actors to move faster: ensuring that only clear, actionable, and high-quality improvements reached the platform.

With high-quality improvements secured, the next priority is to drive impact by making them easy to adopt and beneficial for employees: turning Vetlanda into a primary tool for shared learning and raising business profit.

To support this vision, we conducted deep user research and behavioural analysis. Insights from quantitative data revealed a simple truth: store employees come to Vetlanda with a clear purpose—to find improvements that can elevate results of their own department.

This insight is now guiding our strategy, with personalisation at its core, enabling relevant improvements to surface faster and driving meaningful adoption across the organisation.
MVP of the flow. Because KPI evaluation requires several months of observational data, this flow incorporates email-based coordination with co-workers as an solution.

In 84% of the time, store employees want to find ways to improve store performance only within their particular review area.

The key was to offer a more personalised experience:

‣ Onboarding flow for selecting preferred review topics
.
Our goal was to display the most relevant improvements by leveraging both users’ search queries and the specific expertise of their department to improve discoverability.

‣ User profile.
Introduced a user preferences step, allowing adjustments to email notifications, KPI tracking, and review topics. This step not only improves personalisation today but will also enable future features such as bookmarking, joining store groups, and calculating KPIs at the store level.

‣ Improved search function.
Enhanced the search functionality by supporting multiple languages and improving result relevance through ranking based on whether the search term appears in the improvement title, subtitle or body text.
We implemented a simple but effective onboarding flow that became the turning point in significantly increasing the amount and quality of information collected about coworkers’ preferences.


To support wider adoption, we introduced lightweight ways to track how improvements are implemented across stores. By adding a simple prompt "Have you implemented a Good Example?" on the improvement page, we began capturing who implemented each improvement and how often, without increasing user effort.

This gave us initial visibility into real-world adoption and laid the groundwork for measuring impact. While early data collection was manual, it validated the approach and informed the design of a scalable implementation flow.
By storing feedback of KPI implementations from other stores, we established a data foundation for measuring real business impact.

This enabled us to continue with AI-driven KPI calculations that will automatically quantify and display the profit generated by each Good Example across all countries.

Making impact visible at scale increased confidence and adoption, contributing to a 27% rise in average implementations.

Learnings
Product roadmap co-creation
I learned a great deal about co-creating a product roadmap and organising work into epics and stories in Jira. It was an important lesson in collaborating with stakeholders, as working closely with them was a key part of the process.
Inconsistencies between the design system components and dev library
This project taught me how to design effectively within technical constraints. As many design system components were not yet available in the Vue library, I learned to balance ideal UX solutions with what was realistically buildable, making thoughtful trade-offs between usability, consistency, and development effort.
Collaboration with stakeholders
Designing for co-workers while aligning with stakeholders from highly specialised parts of the business is complex. It requires time and consistent collaboration to build trust, align perspectives, and establish a shared product vision.

But this is not the end...

Beyond the solutions presented here, the project included important dimensions such as cross-functional collaboration, stakeholder alignment, and overcoming team-level challenges. I’m happy to share more about these behind-the-scenes learnings :)
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