Unlocking Market Potential: Strategic Consumer Segmentation and Dynamic Pricing for Balancing Loyalty and Deal Seeking

Background: This paper examines the economic implications of market segmentation on consumer purchasing behavior with a particular emphasis on intertemporal pricing strategies in dynamic markets. Methods: In order to analyze optimal discount rates and the timing for price reductions for consumer segments, including loyal and deal-prone customers, a detailed mathematical model was developed. The model incorporates theories of consumer behavior and pricing elasticity to simulate market responses to price changes throughout a product’s lifecycle. Results: This research indicates that market segmentation enhances sales by targeting the distinct preferences of loyal consumers, who are less price-sensitive and who stabilize revenue streams, and deal-prone consumers, who respond to price reductions. Customizing pricing strategies for loyal consumers and deal-prone consumers increases sales volumes and optimizes profitability. Conclusions: This research improves our comprehension of market segmentation and dynamic pricing, providing a practical framework for businesses to create effective pricing strategies that can be promptly implemented. It emphasizes the significance of understanding consumer behavior and price sensitivity in the interest of revenue promotion. This study also emphasizes the social implications of equitable pricing practices, promoting the implementation of transparent and value-based strategies to promote market inclusivity and consumer trust.

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Publication Details

Journal
Mathematics
Published
2024-10-27
DOI
https://doi.org/10.3390/math12213364
Citations
5
Primary Topic
Consumer Market Behavior and Pricing
Type
article
Field-Weighted Citation Impact
2.19
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article

Unlocking Market Potential: Strategic Consumer Segmentation and Dynamic Pricing for Balancing Loyalty and Deal Seeking

Uriel Spiegel, Limor Dina Gonen, Tchai Tavor
5 citations
Mathematics
Consumer Market Behavior and Pricing
2.19
article

Unlocking Market Potential: Strategic Consumer Segmentation and Dynamic Pricing for Balancing Loyalty and Deal Seeking

Uriel Spiegel, Limor Dina Gonen, Tchai Tavor
article en
5 citations

Abstract

Background: This paper examines the economic implications of market segmentation on consumer purchasing behavior with a particular emphasis on intertemporal pricing strategies in dynamic markets. Methods: In order to analyze optimal discount rates and the timing for price reductions for consumer segments, including loyal and deal-prone customers, a detailed mathematical model was developed. The model incorporates theories of consumer behavior and pricing elasticity to simulate market responses to price changes throughout a product’s lifecycle. Results: This research indicates that market segmentation enhances sales by targeting the distinct preferences of loyal consumers, who are less price-sensitive and who stabilize revenue streams, and deal-prone consumers, who respond to price reductions. Customizing pricing strategies for loyal consumers and deal-prone consumers increases sales volumes and optimizes profitability. Conclusions: This research improves our comprehension of market segmentation and dynamic pricing, providing a practical framework for businesses to create effective pricing strategies that can be promptly implemented. It emphasizes the significance of understanding consumer behavior and price sensitivity in the interest of revenue promotion. This study also emphasizes the social implications of equitable pricing practices, promoting the implementation of transparent and value-based strategies to promote market inclusivity and consumer trust.

MathematicsVol. 12(21)
The Max Stern Yezreel Valley College (IL), Bar-Ilan University (IL), Ariel University (IL)
Openalex Percentile: Top 11%
Consumer Market Behavior and Pricing
2.19
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Unlocking Market Potential: Strategic Consumer Segmentation and Dynamic Pricing for Balancing Loyalty and Deal Seeking — Uriel Spiegel, Limor Dina Gonen, et al. · Mathematics (2024) | TGRS Research Map | TGRS