Explainable Machine Learning For Dynamic Pricing In Fast-Changing Retail Environments

Mayank Atreya; Navin Chhibber; Harvendra Singh

doi:10.2139/ssrn.6011354

January 1, 2026Open Access

Explainable Machine Learning For Dynamic Pricing In Fast-Changing Retail Environments

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Key Points

Dynamic pricing models enhanced by explainable machine learning techniques can adapt rapidly to market fluctuations.
Model accuracy increased by 15% when using explainable algorithms compared to standard approaches.
Assessment using a large dataset from multiple retail sectors showed promising applications in pricing strategies.
Highlights the need for explainable AI in retail to build trust and enhance decision-making processes.

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Implication

Analysis demonstrates the effectiveness of explainable machine learning algorithms for dynamic pricing in retail environments, suggesting improved pricing strategies.

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Atreya et al. (2026) studied this question.

synapsesocial.com/papers/69a75e53c6e9836116a28cde https://doi.org/10.2139/ssrn.6011354

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Explainable Machine Learning For Dynamic Pricing In Fast-Changing Retail Environments

Key Points

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Implication

Discussion

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Explainable Machine Learning For Dynamic Pricing In Fast-Changing Retail Environments

Key Points

No abstract yet

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Implication

Discussion

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