Maximizing the Expected Revenue: The Use of Machine Learning Models for the Case of a Soccer Ball Company

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dc.contributor.advisorCarrasco-Navarro, Rocío
dc.contributor.authorGarcía-Meléndez, Fátima A.
dc.date.accessioned2023-04-24T21:14:59Z
dc.date.available2023-04-24T21:14:59Z
dc.date.issued2022-12
dc.descriptionEvery company begins with a fundamental questions and re-asks this questions several times throughout the company´s life, what is the product that will create enough value for it´s customers so that enough money can be charged to make a profit and keep on creating more value? So basically, a company´s strategy begins with a great product design and a price tag that customers are willing to pay that will maximize revenue. In past years, pricing has been so unattended because usually the responsibility tends to fall under different areas of the company and due to it´s complexity, task associated with setting prices are often not on top of the incumbency list. Ergo, prices are not varied enough for different product items, market segments and purchased occasion, impacting the demand, sales and perceived value of the product and brand. Therefore, this study aimed to determine how can machine learning models help create value and maximize revenue by determining the best product and price for a soccer ball company in Mexico. As a result, this research was able to determine that there are 3 different customer segments and that each of them values different characteristics of the soccer ball. Also, that a random forest model was the best model to calculate the purchase probabilities compared to a naive bayes model, a general linear model with logit link and a support vector machine model. Given those probabilities, the expected revenue was calculated for all the different product profiles, or combinations of the ball, and concluded that a price discriminated model with 3 balls; 1 targeted for each customer segment, can increase the expected revenue from an approximate of $166 to $1,572 dollars, proving that machine learning models and information-based decision making processes should be a must for every companyes_MX
dc.identifier.citationGarcía-Meléndez, F. A. (2022). Maximizing the Expected Revenue: The Use of Machine Learning Models for the Case of a Soccer Ball Company. Trabajo de obtención de grado, Maestría en Ciencia de Datos. Tlaquepaque, Jalisco: ITESO.es_MX
dc.identifier.urihttps://hdl.handle.net/11117/8930
dc.language.isoenges_MX
dc.publisherITESOes_MX
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdfes_MX
dc.subjectPricinges_MX
dc.subjectMaximizationes_MX
dc.subjectValuees_MX
dc.subjectProduct Designes_MX
dc.subjectMachine Learninges_MX
dc.subjectRandom Forestes_MX
dc.subjectExpected Revenuees_MX
dc.titleMaximizing the Expected Revenue: The Use of Machine Learning Models for the Case of a Soccer Ball Companyes_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_MX

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