Enhancing Profitability with an Improved Price Sensitivity Model

Introduction: Enhancing Pricing Strategy with Customer Insight

Pricing plays a critical role in profitability, yet many companies rely on simple decision models rooted in expert rules and only basic customer data. Such approaches are functional, but they rarely provide the nuance needed to fully unlock profit potential across diverse product groups.

Our client faced exactly this situation: a straightforward, rule-based decision process that did not adequately capture how different customers react to price changes.

Before: Simple Rule-Based Decision Model

The initial setup was built around a basic decision model using expert rules and limited client information. The process focused on a small set of core variables and ignored richer behavioral or contextual data, which led to:

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  Reduced ability to tailor prices to individual customer price sensitivity.
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  Missed opportunities to optimize margin for specific products and segments.
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  An inflexible strategy that struggled to keep up with changing market conditions.

Our Approach: Advanced, Explainable Price Sensitivity Modeling

To move beyond this plateau, we designed an automatic price sensitivity process that recommends an optimal price for each product, using advanced machine learning methods and explainable AI techniques.

The enhanced model incorporates:

• Advanced machine learning algorithms (including gradient boosting methods such as XGBoost) to capture non‑linear relationships between price, product attributes and customer characteristics.
• Explainable AI (XAI) tools to make complex models transparent and trustworthy for business stakeholders, including:

Thanks to these methods, the pricing team could not only see what price the model recommends, but also why a given recommendation is optimal in a specific context.