Cybernetis AI Helps Appliances Retailer Unlock Pricing Power

Challenges

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Before partnering with Cybernetis AI, a mid-sized small appliances retailer with operations across 12 metropolitan areas faced major hurdles in its pricing strategy. Annual revenue of about $120M was under pressure as discounts varied significantly by region and sales teams relied on intuition rather than structured analytics. The lack of a centralized, data-driven pricing framework led to inconsistent margins, missed opportunities for optimization, and declining competitiveness.

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Most of the company’s pricing and sales data was scattered across Excel sheets, disconnected CRM systems, and manual reports. Without automated analytical tools, decision-making cycles stretched over weeks, leaving the retailer unable to adapt prices quickly to customer demand, seasonal changes, or competitor actions. This limited visibility into customer willingness to pay and market segmentation made it difficult to tailor offers, protect profitability, and scale efficiently.

Project Highlights

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• 10-week implementation from kickoff to production-ready application
• Integrated 2M+ rows of pricing and sales data from ERP, POS, and CRM systems
• AI-driven segmentation of product categories and customer types
• Established price elasticity curves for every SKU and region served
• Rigorous testing framework, including A/B experiments, to validate results

Approach

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Over the course of 10 weeks, the Cybernetis AI team worked closely with the retailer’s pricing and sales leadership to deploy Cybernetis AI Pricing Optimization. By integrating more than 2 million rows of historical transaction and customer data, the system created a single, reliable source of truth for pricing analytics.

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Cybernetis AI applied machine learning to build elasticity models that identified optimal price points across SKUs and regions. The platform segmented customers by behavior and willingness to pay, enabling tailored offers and reducing reliance on intuition-driven discounts. Automated dashboards provided real-time monitoring of pricing performance and margin impact, while A/B tests validated the predicted outcomes.