Consumer Choice Modeling in Retail Pricing Strategies
Summary
Consumer choice modeling in retail pricing strategies revolves around understanding how price and non-price factors drive individual purchase decisions and substitution patterns. Grounded in random utility theory, discrete choice frameworks estimate the probability that a consumer selects one product over alternatives as a function of price, promotions, product attributes and personal heterogeneity. Advances in scanner and point-of-sale data have enabled the calibration of nested logit, mixed-logit and Bayesian semiparametric models that capture both immediate and lagged price effects, stockpiling behaviour, reference-price formation and cross-product substitution. These models inform assortment planning, dynamic price optimisation and promotional design, allowing retailers to tailor offers to consumer segments, improve inventory management and enhance profitability. The integration of behavioural insights—such as loss aversion and fairness perceptions—alongside machine learning techniques has further refined forecasts of demand elasticity and cannibalisation, yielding globally applicable tools for evidence-based pricing in competitive markets.
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Consumer Choice Modeling in Retail Pricing Strategies publication trend
The graph below shows the total number of articles in consumer choice modeling in retail pricing strategies across all publications each year (not limited to Nature Index journals).
Technical terms
Discrete choice model: A framework predicting selection among alternatives based on estimated utilities derived from attributes and consumer heterogeneity.
Nested logit model: A discrete choice variant that accommodates hierarchical substitution patterns by grouping similar options into nests.
Price cannibalisation: The reduction in sales of non-promoted items caused by the promotion of another product within the same category.
Dynamic pricing: A strategy that adjusts prices over time in response to demand fluctuations, inventory levels and competitive actions.
Semiparametric Bayesian model: A hybrid modelling technique combining parametric and nonparametric elements, estimated in a Bayesian framework to flexibly capture complex relationships.
References
- On consumer choice patterns and the net impact of feature promotions. International Journal of Research in Marketing (2018).
- Causal Quantification of Cannibalization During Promotional Sales in Grocery Retail. IEEE Access (2021).
- Dynamic pricing using flexible heterogeneous sales response models. OR Spectrum (2024).
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