Incorporating Cannibalization into Pricing Optimization Using Choice Data: An Application to the Pricing of Mobile Broadband Services

Fransiscus Rian Pratikto


Price differentiation may not be as effective in increasing profitability due to imperfect segmentation, arbitrage, and, cannibalization. Cannibalization takes place when customer with higher willingness-to-pay buys lower-priced product. This research proposes an approach to incorporating cannibalization into pricing optimization using choice data. From choice data, individual level utilities are estimated using hierarchical Bayes and individual choice is predicted using randomized first choice simulation. Individual choices are then aggregated to obtain the demand function. The novelty of this research is in the way cannibalization is incorporated into the pricing optimization. Instead of integrating cannibalization into the demand function or representing it as a separate component in the optimization formulation, in this research, cannibalizing products are incorporated into the simulation scenario as competing products, based on which the demand functions used in the optimization are derived. This approach is more direct and realistic than those in the previous research. The approach was implemented in a case study of mobile broadband services in Indonesian price-sensitive market. The result shows that two-fare-class price differentiation incoporated with product differentiation increases total contribution of about 60% compared to single-fare-class policy. Furthermore, it is also shown from our case study that starting from a three-fare-class policy, through iterations, our approach suggests that policy with two-fare-class results in a not significantly different total contribution.


cannibalization; pricing optimization; choice data; hierarchical Bayes; randomized first choice

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