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Petrol Industries Achieves 61% Profit Uplift across Online Marketplaces

Learn how Petrol Industries leveraged 7Learnings and Channelengine to optimize prices on multiple marketplaces
Holen Sie sich die Fallstudie

Überblick über das Unternehmen

Petrol Industries is a leading European apparel and denim brand operating a high-volume multi-channel model across major online marketplaces, including Zalando and Bol.com, and its own webshop. The primary objective of partnering with 7Learnings was to transition from rule-based pricing to dynamic, elasticity-informed optimization. By doing so, the brand aimed to automate its data pipeline, protect profitability during clearance cycles, and maintain strict compliance across online platforms. Following this, Petrol Industries plans to roll out 7Learnings globally across all its online marketplaces and webshop.

Petrol Industries logo with red oval and black text.

Petrol Industries sees double digit uplifts in profit, margin, and revenue

Herausforderungen

  1. Petrol Industries relied on rule-based pricing and manual data that failed to calculate product-level profitability. This approach could not account for marketplace variables such as return rates, shipping, marketing, or tiered marketplace commission rates, resulting in profit losses.
  2. Prior to automating data pipelines, the data required for 7Learnings‘ machine learning models had to be manually sourced which delayed and restricted pricing adaptation across marketplaces and countries. This made it difficult to manage online promotions that needed to turn a profit while still protecting wholesale partner relationships and brand equity.
  3. Platforms like Zalando enforce complex, strict pricing rules and structural guidelines. Manually managing, adapting, and staying compliant with these platform-specific rules across multiple geographical regions creates operational overhead and risk of violation.
  4. Clearance periods relied on broad, reactive price-slashing („race to the bottom“). This unoptimized discounting destroyed product margin on slow-moving inventory without effectively increasing demand on marketplaces.

Lösung

  • The 7Learnings engine predicts profit on a product-by-product level by mathematically factoring in demand, shipping costs, high returns, and tiered marketplace commission brackets. The algorithm is configured to avoid pricing points within unprofitable marketplace commission tiers, ensuring every transaction maximizes net profit.
  • By developing a direct connection between 7Learnings and ChannelEngine, Petrol Industries automated its marketplace data pipelines, ingesting live marketplace data into 7Learnings. This removed manual work and maximized marketplace profitability with disciplined omnichannel pricing.
  • 7Learnings integrated customized front-end rule modules, allowing Petrol Industries to input platform-specific constraints (e.g., Zalando guidelines). The predictive pricing engine automatically filters out any price points that violate these constraints and selects the optimal price from the remaining compliant options.
  • The 7Learnings algorithm aggressively protected margins by keeping prices higher on inelastic items where customers were willing to pay, while executing targeted, margin-optimized discounts only where demand elasticity guaranteed a profitable volume uplift.

7Learnings hat dazu beigetragen:

+61%

profit increase

+37%

margin increase

+18%

revenue increase

Partnership Spotlight: 7Learnings & ChannelEngine

A cornerstone of this implementation’s success was the deep, collaborative partnership between 7Learnings and ChannelEngine, Petrol Industries’ marketplace integrator that connects brands and retailers to more than 1,300 global marketplaces, social commerce platforms, and emerging agentic commerce channels.

  • The Integration: To solve the pain point of manually gathering data from multiple sources into 7Learnings, the two platforms designed an automated, direct data pipeline integration.
  • How it works: The connection allows 7Learnings to automatically pull transaction history, active price periods, product attributes, stock levels and channel data directly from ChannelEngine’s environment. The 7Learnings engine models daily demand and calculates price-elasticity for each SKU to generate profit-optimal prices. These optimized prices can then be sent back to ChannelEngine, syncing them live on all channels like Zalando and Bol.com instantly.
  • Strategic Value: ChannelEngine connects and automates your multi-channel sales flow; 7Learnings transforms that live transaction and stock data into AI-predicted, profit-optimal prices for every product, every channel. This automated loop eliminates operational friction, reduces pricing errors, and drives maximum profitability. Every marketplace, priced to win.

Schlussfolgerung

The partnership modernized Petrol Industries’ marketplace infrastructure by replacing rule-based pricing and manual data handoffs with a predictive, machine learning-driven solution. By factoring in transactional costs such as returns and commissions into product-level elasticity models, the 7Learnings solution protected gross margins and steered the brand away from unprofitable price matching. Facilitated by the ChannelEngine integration, this approach enabled Petrol Industries to achieve a remarkable 61.1% profit uplift and a 36.8% margin increase. Petrol Industries is now prepared for a global rollout across all active marketplaces with 7Learnings.

Black and white Petrol brand logo inside an oval.
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

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