Commercial Trades & DIY Retailer Layer-Grosshandel Achieves 15% Profit & 28% Revenue Uplifts

Learn how Layer-Grosshandel reduced pricing errors & improved cross-channel coordination

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Company Overview

Layer-Grosshandel is a leading multi-channel specialized retailer and wholesaler of tools, machine tools, and professional workwear. Serving B2B clients (across trade/crafts, agriculture, and industry) and B2C DIY shoppers through physical branches and an online shop, Layer-Grosshandel allows all customer segments to use both channels flexibly.

The primary objective of partnering with 7Learnings was to transition from static cost-plus methods and rule-based repricing to an automated, elasticity-driven system. By doing so, Layer-Grosshandel aimed to improve pricing efficiency across its 58,000+ SKUs, capture unrealized demand, and enable target-based corporate steering.

Layer-Grosshandel automates pricing across catalog of over 58,000 products

Challenges

Managing a vast product catalog of over 58,000 SKUs manually made price adjustments time-consuming and error-prone. With an assortment of this size, it was difficult for the business to maintain a clear overview and strategic control over its overarching pricing execution.
The business historically relied on traditional cost-plus pricing, leaving prices largely static over time. This approach insufficiently accounted for competitor movements and customers’ willingness to pay, resulting in suboptimal pricing, missed margin potential, and exposure to online price transparency.
Prior to implementing 7Learnings, the company lacked an advanced pricing engine. While they had used a basic repricing tool for about a year to adjust prices based solely on competitor data and predefined rules, the tool could not account for price elasticity. As a result, the system forced unnecessary, margin-depleting competitor matching on many low elasticity products.
Switching between revenue and margin goals required slow manual recalculations instead of efficient, automated adjustments. The lack of an agile control mechanism meant that pricing catalog alignment with business targets was not achieved.

Solution

7Learnings automated core price generation across selected key categories of Layer-Grosshandel’s 58,000+ product assortment. This eliminated time-consuming manual updates, minimized pricing mistakes, and provided management with a centralized overview of their pricing architecture.
The platform replaced static cost-plus methods with a dynamic framework that accounts for shifting market conditions and customer willingness to pay. This allowed Layer-Grosshandel to unlock previously unexploited margin potential while navigating the price transparency of the digital market.
Moving away from rule-based repricing tools, the 7Learnings engine introduced sophisticated price elasticity modeling. Rather than performing direct, blanket competitor matching, the system determines true product elasticity, ensuring prices are adjusted to match competitors only when absolutely necessary.
The platform empowered management to steer corporate pricing strategy efficiently. By simply setting a high-level target, such as profit optimization or revenue growth, the algorithm automatically determines the optimal prices across the assortment to meet the defined business objective.

Implementation Overview

The predictive pricing implementation centered on an A/B test in a predefined product section to establish a new price baseline for each SKU. A structured A/B test and phased implementation enabled Layer to safely automate manual pricing workflows and validate a predictive, target-driven strategy across its portfolio.

Bonus: Long-term predictions, therefore, allow for higher margins and increased revenue through KPI steering optimization, as well as a manual effort reduction.

7Learnings helped achieve:

+15%

profit increase

+28%

revenue increase

Conclusion

The partnership modernized Layer-Grosshandel’s retail infrastructure by replacing manual complexities with a dynamic, predictive pricing engine. 7Learnings successfully automated daily operations, eliminated costly pricing errors, and improved cross-channel coordination. By incorporating market dynamics into predictive modeling and enabling management to steer pricing in line with strategic targets, the A/B test results confirmed double-digit growth across both the top and bottom lines.

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

Dr. Yola Engler

Director Pricing and Profit Management, Westwing

7Learnings replaced broad discounts with surgical, elasticity-driven pricing across our digital marketplaces. Driving a +61% profit uplift while automating our data pipelines via ChannelEngine has been a huge benefit for our global commercial strategy.

Leon op 't Hoog

Marketplace Manager, Petrol Industries

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

The 7Learnings solution has signifcantly increased our proftability and greatly simplifed the pricing process.

Dirk Wappler

CEO & Co-founder @ Apologistics

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