REMA 1000: Accelerating Business Intelligence Adoption for 4,000 Users

How to facilitate the adoption of an analytics platform across an international retail group?

Business Intelligence tools have become essential for driving operational performance.

But their success directly depends on their ability to be used daily by business teams.

To support the deployment of its GII (Golden Insights Intelligence) analytics platform from Symphony Retail AI, REMA 1000 chose K-NOW to integrate user support directly into the application.

The objective: accelerate platform adoption, facilitate access to information, and empower users.

The Context

REMA 1000 is one of the leading food retail chains in Northern Europe.

Based in Oslo, the group operates several thousand stores and relies heavily on data to manage its operations.

As part of the deployment of GII, its Business Intelligence platform developed by Symphony Retail AI, REMA 1000 aims to provide its teams with a modern analytical environment capable of facilitating decision-making at all levels of the organization.

The challenge is not only technological.

It also involves ensuring rapid adoption of new analytical practices by several thousand users.

The Challenges

Support the platform launch

REMA 1000 wanted to use K-NOW to:

• introduce the new application to users
• highlight new features
• communicate platform updates
• collect user feedback directly within the application

Simplify the adoption of analytical tools

The rich functionality of a Business Intelligence tool can lead to adoption difficulties, especially for occasional users.

The goal was to make data access simpler and more intuitive.

Prepare for future deployments

The project also aimed to enable the REMA 1000 teams to discover the possibilities offered by K-NOW and quickly gain the autonomy needed to create their own content.

The implemented solution

Support directly integrated into GII

The deployed system includes:

• a user onboarding journey
• contextual announcements
• feedback mechanisms
• help accessible directly within the application

Users thus benefit from assistance available when they need it, without leaving their work environment.

International deployment

Content was made available in:

• English
• Norwegian

to effectively support the various populations concerned.

Author Training

A first training session allowed the REMA 1000 teams to discover the authoring mode and start producing their own K-NOW content.

Observed Results

High Team Satisfaction

User feedback highlights several immediate benefits:

• contextual help directly within GII is considered particularly useful and time-saving
• creating new content is perceived as simple and fast
• updates are easy to perform
• publications can be scheduled according to go-live dates
• the training provided enabled rapid upskilling of the teams.

Enhanced Autonomy

The REMA 1000 teams quickly identified K-NOW's ability to:

• facilitate the onboarding of new users
• accelerate the production of help content
• progressively reduce support requests
• support platform evolutions over time.

Effective Collaboration

The project also benefited from:

• excellent quality of storyboards prepared by REMA 1000
• fluid communication between all stakeholders
• rapid technical integration on GII thanks to experience gained from similar projects.

Key Figures

‍Project Testimonials

"On the spot helps inside GII really useful and time saving."

"Easy to create new learnings and publish quickly."

"Easy and quick to update."

"Delivered on schedule."

Key Takeaways

The REMA 1000 experience demonstrates that the success of a Business Intelligence project does not depend solely on the quality of dashboards or available data.

Value creation primarily comes through user adoption.

By integrating support directly into GII, REMA 1000 facilitates user upskilling, accelerates the adoption of new analytical practices, and sustainably prepares for future deployments.

K-NOW thus becomes a driver for adoption, performance, and autonomy, serving the group's data strategy.

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