Consumer-based factors in applying dynamic pricing in a digital context in South Africa

Abstract

This study examined dynamic pricing, an academic field that analyses and determines the most advantageous selling prices for items or services. The study found consumers are opposed to paying transactions, scarcity, urgency, and specific categories of goods and services fees. They are willing to pay more for personalised offers, fair pricing, transparent pricing strategies, and customer treatment. The study confirms the results of the Ariely experiment, suggesting that customer-based factors influence willingness to pay for a product or service. The study suggests that dynamic pricing can effectively increase revenues and company valuations for online retailers, when implemented fairly and justifiably. Consumers are tolerant of price variations where fluctuating demand is expected, given the price differential is not excessive. Firms in industries with limited resources and fluctuating demand are more able to implement dynamic pricing. Online retailers must allocate resources and determine suitable artificial intelligence (AI) solutions to address negative customer reactions. AI and machine learning (ML) are pivotal in forecasting and facilitating tailored experiences in big data analytics. Adopting AI and ML is key for dynamic pricing, especially regarding price sensitivity and irrationality. Recommendations for sellers include developing consistent, transparent, and simple policies that align with consumer expectations. Companies should carefully consider price discrimination and increase transparency to offset negative effects. Future research should consider Consumer factors, pricing optimisation for lower-income consumers, and longitudinal studies. The study's limitations include insufficient data for younger generations, potential biases due to generational opinions, and a bias favouring individuals with higher socio- economic levels.