How Meidanis Identified a 7% Contribution-Margin Uplift Opportunity with AI-Driven Pricing
Meidanis, a sizeable Greek distributor of electrical equipment and consumables, wanted to understand where prices should increase or decrease across a broad product portfolio — and whether a more differentiated approach could improve contribution margin while protecting — and, where possible, increasing — revenue and volume.
The challenge
Meidanis operates through a nationwide network of more than 30 stores. The business is predominantly B2B, serving professional customers, while also including a smaller retail component.
Its scale, broad product portfolio, regional footprint, structured discount policies by partner type, and additional deal-level flexibility create substantial pricing complexity.
Historically, pricing had been driven largely by a cost-plus logic, including responses to supplier price increases. Management believed that some products or customers were probably underpriced while others might be overpriced, but did not have a reliable way to identify where prices should increase or decrease, by how much, and which commercial factors mattered most.
The objective was therefore not simply to raise prices. It was to identify a differentiated pricing structure that could improve contribution margin while protecting — and where possible increasing — revenue and volume.
The approach
We applied FutureUP's Strategic Price Uplift Scan approach using data from 2023–2025:
A focused AI-driven price optimization analysis of existing sales data, without requiring software deployment.
The analysis considered pricing behavior across products, commercial policies, customer segments, payment terms, and other relevant business and market factors, and estimated differentiated price-response relationships.
What the analysis found
The results identified an estimated 7% contribution-margin uplift opportunity, with revenue and volume growth under the suggested optimal pricing mix.
More importantly, the recommendation was not a uniform price change but a differentiated one, with targeted increases and decreases by product, customer segment, commercial policy, and other relevant conditions.
The analysis also generated alternative scenarios. This allowed management to evaluate what would happen under different levels of execution — including more conservative pricing strategies, partial implementation, or continued use of simpler approaches such as cost-plus pricing.
Validation on unseen 2026 data
Several months later, newly accumulated 2026 sales data provided a much stronger test of the model. The business environment had become more challenging, with supplier cost increases, inflationary pressure, and broader geopolitical and market uncertainty.
Despite these changing conditions, the previously estimated price-response patterns tracked observed behavior closely, with predictive accuracy remaining close to 90%.
Observed vs. estimated price-response patterns on unseen 2026 data
This provided an important validation: the model had not merely fitted historical data well. It had captured a real underlying price signal that remained valid when tested against genuinely unseen sales data in a more difficult market environment.
The case also reinforced the value of differentiated optimization. Compared with simpler pricing approaches, the modeled profit opportunity from the optimized pricing scheme was approximately 1.7 times higher.
The broader lesson
Meaningful pricing upside does not necessarily require an enterprise-wide pricing transformation.
A focused AI-driven analysis can identify where the opportunity exists, quantify its potential, test alternative strategies, and provide a validated basis for better pricing decisions.
Check out FutureUP's Strategic Price Uplift Scan:
AI-driven pricing insights to unlock hidden revenue & margin
Using your data. Minimal involvement. No implementation needed.
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