Uncapacitated Pricing Optimization for Mobile Broadband Services

Fransiscus Rian Pratikto


The success of revenue management starting in the mid-1980s has been driving pricing decision to be more tactical and operational. Since then, statistics and operations research have been important tools in pricing and revenue optimization. This research seeks to determine optimal price for mobile broadband services of a particular service provider. The case study is mobile broadband services in Indonesian market. We made a plausible assumption that there is no capacity constraint. We used choice-based conjoint with hierarchical Bayes estimation method to derive individual part-worth utilities, based on which market simulation was run to obtain the price-response function. By combining this with information about market size, we came up with a number of data points representing the demand function. Instead of fitting the data points with some theoretical demand functions, we used monotonic cubic splines to interpolate the demand function. Accordingly, we did not use explicit demand functions in the optimization, but a numerical interpolation function to estimate demand for any particular price level. Using enumeration, we then came up with a recommended contribution-maximizing prices under one, two, and three fare-classes segmentation. We assumed a perfect segmentation where cannibalization and arbitrage were not present. Further, we discussed a generalized optimal segmentation problem under that assumption. We also investigated the impact of the changes in competitors’ service attributes on the optimal prices.


uncapacitated pricing optimization; choice-based conjoint; hierarchical Bayes; price-response function; monotonic cubic splines

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