This paper addresses the basic issues with using classical Price Elasticity of Demand (PED) models for commercial applications, particularly due to their inability to apply effective constraints under dynamic market conditions. Classical models usually lead to theoretically optimal, but impractical price recommendations (e.g. unlimited price increases for inelastic goods). This study proposes the Holistic Pricing Approach (HPA), a multi-variable method that unifies data inputs from multiple sources into a single recommendation engine that helps overcome the classical model shortcomings.

The HPA method employs a data fusion system linking three unique data sources: internal economics (e.g., product cost, target gross margins), competitive intelligence (competitor’s prices) and macroeconomic factors (e.g., inflation). These inputs are standardized with an interoperability layer to drive a four-step algorithmic heuristic. This includes a margin anchor price that is subject to adjustments by “competitive boundary checks” and “volume guardrails” to avoid excessive demand erosion.

The effectiveness of the HPA was validated through a theoretical simulation with truncation that was compared to a classic elasticity model. The results showed that the traditional approach maximized margin at the expense of significant volume (20% lost), while HPA successfully balanced preserving margins and market share (5% volume loss). Furthermore, the total profit dollar amount was greater for the HPA strategy, which confirms that the HPA methodology drives increasing economic value.

This study demonstrates that to protect revenue integrity, pricing must be approached as an interoperable ecosystem of constraints rather than a single dimensional elasticity calculation. This approach offers a roadmap for business leaders who, in the face of inflation, need to strike the right balance between increasing prices and preserving market share.

Holistic Pricing, Demand Elasticity, Data Fusion, Data Interoperability, Pricing Analytics

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