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Outletcity Achieves 26% Profit Uplift & Optimizes End-of-Season Sell-Off

Learn how 7Learnings helped Outletcity increase their profitability while reducing the complexity and workload of setting prices
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Company Overview

Outletcity Metzingen, a pioneer in the European factory outlet market and a global fashion destination, is a premium off-price retailer for women’s, men’s, and children’s fashion. Since launching its online store in 2012, the company’s core strategy has focused on sophisticated off-pricing and liquidation, rather than traditional RRP-focused retail. Operating in the DACH region, Outletcity utilizes a closed-access, membership-required online storefront. Historically, the company used internal, rule-based pricing systems. Managing pricing across over 300 brands required more granular price adjustments and precise forecasting to manage high-velocity liquidation and hit business targets.

Outletcity achieves uplifts in profit, revenue, and sales

Challenges

  1. The retailer managed frequent, multi-level promotions including coupons, seasonal sales, and global events that were difficult to optimize manually. They could not accurately calculate the net margin impact of overlapping discount layers or model the cross-elasticity between promoted items and old season assortments.
  2. A primary challenge was hitting precise end-of-season volume targets without sacrificing too much profit margin. Markdown schedules relied on lagging indicators like historical sell-through (STR), leading to linear markdown ladders that often triggered deep discounts too late in the season to effectively steer inventory along an optimized recovery curve.
  3. Fragmented inventory (“broken” size runs) with missing core sizes led to suppressed sales and warehouse stagnation. This was managed manually to compensate for limitations in the existing tool. This manual effort highlighted the need for a solution that can balance “Oversellers” and broken runs to prevent inefficient inventory flow and lost profits.
  4. Outletcity needed a more granular approach to automate end-of-season sell-offs through deeper seasonal steering. The goal was to integrate specific liquidation-end optimizations that would more precisely clear aged inventory without relying on manual adjustments or broad discounting.

Solution

  • 7Learnings enabled Outletcity to pre-simulate the net-margin impact of complex, multi-level promotions (such as overlapping coupons and global events) to maintain profit alignment. By modeling cross-product elasticity, the system optimized pricing and avoided unnecessary cannibalization of full-price items.
  • The solution used elasticity models to steer inventory toward a set Liquidation End Stock Value. Automated steering replaced lagging indicators, identifying “Undersellers” early to trigger data-informed discounts. This optimized recovery for every item, hitting volume targets by the end-date and maximizing total recovery value.
  • Per Outletcity’s request, 7Learnings implemented a boost to suppressed sales of fragmented size runs by aggressively liquidating this “broken” inventory. The system grouped these items, normalized their demand forecasts, and applied more assertive price adjustments than those for full-size sets. This accelerated sell-off prevented warehouse stagnation and protected the margins of high-demand “Oversellers.”
  • The retailer utilized enhanced seasonal parameters within the tool to automate the clearance of older inventory. This granular steering for aged stock yielded a 145.8% profit uplift in that segment, successfully freeing up warehouse space while ensuring newer arrivals were managed according to their specific lifecycle needs.

Implementation Overview

The implementation started with defining KPIs and integrating transaction, stock, and attribute files into the 7Learnings engine. After customization, technical guardrails and predictive forecasting tailored to Outletcity’s off-price needs were established. A formal A/B test divided the assortment into Tool Support (TS) and No Tool Support (NTS) groups to assess the financial impact of AI-driven optimization versus the in-house solution. By the mid-point, liquidation steering and optimization rules were refined to improve slower-moving size runs. The validation phase showed a strong business case with significant projected net profit, enabling a rollout across the entire assortment.

7Learnings helped achieve:

26%

profit increase

11%

revenue increase

5%

sales before returns

Conclusion

The partnership between Outletcity Metzingen and 7Learnings validates the impact of AI-driven predictive pricing in navigating the inherent difficulties of high-volume off-price retail. The company successfully optimized the trade-off between protecting premium margins and achieving aggressive liquidation targets. The A/B test delivered a 26.4% profit uplift and a significant projected net business impact after accounting for tool costs. As Outletcity evaluates long-term effects and continues to fine-tune steering, the retailer is now also considering the broader impact of optimization for performance marketing and purchasing, eyeing additional avenues to improve efficiency.

Thanks to 7Learnings, we are finally on track for data-driven price automation and optimization!

Dr. Yola Engler
Director Pricing and Profit Management, Westwing
In the face of a growing and complex market, 7Learnings has helped us to significantly increase our margins while automating and streamlining our pricing process at the same time.
Jon Rogers
Vice President of Finance @ Displays2go

As an online retailer, it is very important to get your pricing right. 7Learnings did a great job in automating our pricing process while increasing profitability and sales significantly.

Oliver Roskopf
Chief Marketing Officer @ Vitafy

The solution developed by 7Learnings made price steering radically easy for our team, while increasing our profits and revenues significantly.

Timo Bethlehem
Managing Director @ meinemarkenmode.de

We benefited greatly from the pricing know-how and expertise provided by the data science team at 7Learnings. With 7Learnings’ predictive pricing solution, we achieved a fast ROI and completely automated our pricing process.

Jürgen Schuster
Founder & General Manager @ Raumschmiede
The 7Learnings solution has signifcantly increased our proftability and greatly simplifed the pricing process.
Dirk Wappler
CEO & Co-founder @ Apologistics

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