Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming

In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts

Pricing the Cloud: An Auction Approach

Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research. One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints. Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice

Cloud Technology Selection: A structured framework for decision making

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis study aims to get organizations to improve their decision making during the selection of cloud technology process. As the technology evolves alongside an ever-increasing abundance in market offer, it may be challenging to choose the desirable service that encompasses several business approaches. For the purpose of this study to be attained, the reader must first comprehend the definition of Cloud Technology: it is the delivery of IT resources over the Internet, being applications, software, storage, among other services. Furthermore, understanding the current main technologies/architectures and their capabilities/limitations will play an important role in designing and developing the prospected solution. A thoroughly research will be produced to better define the criteria used in the process. Despite the fact that technology is able to be tailored up to a certain level for the organization needs, a higher level of participation will encourage vendors and architecture designers to develop a better knowledge on the companies’ desires, thus delivering more appropriate features to their unique needs

Prevalencia de la segmentación de mercado como técnica óptima para el posicionamiento empresarial. Revisión sistemática

El estudio tuvo como objetivo revisar la literatura existente y actualizar nuevas posturas sobre segmentación de mercados como técnica para el posicionamiento empresarial. La investigación se desarrolló mediante una revisión sistemática con un enfoque cualitativo. Asimismo, para cumplir este fin se tuvo que emplear artículos indexados de fuentes confiables como Scopus y Doaj, que cuenten con la identificación de objetivos digitales (DOI) y fueron representados en inglés, siendo un aporte significativo de información teórica por parte de los artículos científicos indexados. Después del proceso de análisis de inclusión y exclusión, se hallaron 69 artículos con las características necesarias para aplicarse en el estudio, seguidamente, mediante un análisis minucioso se seleccionaron 35 artículos referentes a la variable, verificando que compartan coincidencias con los enfoques de estudio, que fueron psicográfico, demográfico, conductual y geográfico. Se concluyó que, la estrategia de segmentación debe ser totalmente precisa para que una empresa se oriente eficazmente a su grupo deseado y, si las empresas tienen un segmento bien descrito y dominado, ésta obtendrá una posición de mercado más sólida