Recently, 7Learnings, Google Cloud, and KPS AG gathered at Google’s Hamburg headquarters for an exclusive executive session. The goal was ambitious: to discuss how retail leaders are moving beyond static, manual rules toward continuous, data-driven optimization.

The focal point of the afternoon was a deep dive into GALERIA, one of Germany’s largest department store chains. With over 2 million SKUs and 83 retail locations, GALERIA’s transformation offers a blueprint for how traditional retailers can outpace agile online competitors.

Impressions from the event

The challenge: Breaking free from legacy constraints

The discussion, expertly moderated by Paul Anderie (KPS AG), began by addressing the “manual trap.” Like many established retailers, GALERIA previously relied on pricing processes that were simply too slow and inflexible for today’s landscape.

Panelists Eva Lukai (Google Cloud), Thomas Zielke (KPS AG), and Bert Middendorp (GALERIA) highlighted the specific pain points that triggered this transformation:

  • Pricing responsiveness: Managing millions of SKUs manually meant that performance data often went cold before prices could be adjusted.
  • Channel inconsistency: Discrepancies between product categories and channels were hurting conversion rates and brand perception.
  • The scale problem: The sheer volume of the assortment required a solution that could scale across everything from fashion to lifestyle accessories.

The solution: A future-proof AI foundation

To solve this, GALERIA partnered with KPS to implement 7Learnings’ AI-powered pricing solution. The strategy was built on three technical pillars:

  1. Predictive engine: Using machine learning to automate decisions based on internal data, competitor benchmarks, and demand signals.
  2. Simulation & forecasting: The ability to “test” prices before they go live, forecasting revenue, profit, and volume to ensure they align with high-level business goals.
  3. Modern cloud infrastructure: The entire solution sits on Google Cloud Platform, utilizing BigQuery for high-speed data warehousing and precise forecasting.

Remarkable results

The highlight of the session was the reveal of the project’s impact. By shifting from manual rules to real-time, data-driven pricing, GALERIA didn’t just see incremental gains—they saw a total performance shift:

  • +230% Revenue Uplift (compared to control groups).
  • +50% Margin Improvement.
  • 2-Month ROI, proving that AI implementation can deliver value almost immediately.

As our Co-Founder & CEO Felix Hoffmann noted, moving to predictive pricing represents a fundamental change in how a retail organization operates.

For a deep dive into the full implementation process, read our case study with Galeria here.