Abstract:

This study sought to produce the information needed to define a dynamic pricing strategy and contextualized offer that matches e-consumers’ profiles, thereby raising the number of reservations made through online distribution channels. A database of 11 million e-consumers of an online distribution channel was used. Hierarchical and non-hierarchical segmentation methods were applied to identify and validate the optimal number of market segments. Profiles of these segments were developed based on e-consumers’ characteristics and the probability that these individuals would reserve a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into the predefined segments.

This empirical study’s results illustrate how the intelligent use of information by online distribution channels can be improved through optimal dynamic pricing strategies and contextualized offers fitting the profile of each new e-consumer. The findings suggest that appropriate market segmentation policies can be implemented by online reservation systems. This approach offers benefits to service suppliers because it can generate a higher probability of reservations and more profits than fixed pricing can.