Demand estimation and bundle price optimization : a data-driven approach
The bundling of multi-products at a fixed price has become a popular marketing strategy and attracted many researchers’ attention. This dissertation investigates the bundle pricing problem with discrete choice models. Two demand estimation methods, Random-Coefficients Logit Model and Marginal Dis...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/77161 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The bundling of multi-products at a fixed price has become a popular marketing strategy and
attracted many researchers’ attention. This dissertation investigates the bundle pricing problem
with discrete choice models. Two demand estimation methods, Random-Coefficients Logit Model
and Marginal Distribution Model, are carefully studied and implemented into a real data set in
the fast food industry to exhibit their prediction ability. To solve the bundle pricing problem, we
employ a framework called “Marginal Estimations + Price Optimization” developed by Yan et al., which is based on Marginal Distribution Model. The bundle price optimization is demonstrated
by implementing this framework into the aforementioned data set. Besides, a demand forecasting
method based on choice models is proposed and used to predict the market shares of new bundles
in the context of bundle design. |
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