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AssortmentCustomize

Financiado

Cerrado

Real time Assortment Customization for Products with Dynamic Content Theory and...

Real time Assortment Customization for Products with Dynamic Content Theory and an Application to Interactive TV In many industries, firms give consumers more choice options than they can choose, in a phenomenon that has been described as the paradox of choice. When products are created and updated very rapidly, the problem is worse e.g., co... ver más

31/03/2013

EUR

186K€

Presupuesto del proyecto: 186K€

Líder del proyecto

ERASMUS UNIVERSITEIT ROTTERDAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2013-03-31 No tenemos la información de la convocatoria

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Información adicional privada

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1 Participantes

EUR

0.00€ | Lider

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Líder del proyecto

ERASMUS UNIVERSITEIT ROTTERDAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 186K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto In many industries, firms give consumers more choice options than they can choose, in a phenomenon that has been described as the paradox of choice. When products are created and updated very rapidly, the problem is worse e.g., consumers often have hundreds of cable TV programs to choose from. This project proposes a method to optimally customize the assortment of products shown to consumers according to each consumer' s individual characteristics. The method first identifies product category membership for each product, and looks at consumer historical data to infer consumer’s style. Given product categories, consumer style, product price and cost, the method uses dynamic programming to identify the best shortlist of products to be offered to each consumer. Next, the consumer makes a decision (to choose or not a product from the list). The decision is then used to update the optimization algorithm. The method proposed in this project will be tested in one industry in particular (cable TV), but it will be general enough to be applicable to others. The method is based on cutting-edge optimization technology. This knowledge was developed at MIT and published by the applicant along with MIT senior professors in the best journal of the marketing field (Marketing Science). The applicant’s 2009 paper was the finalist of the prestigious John D.C.Little Award, and received Elsevier's Citations of Excellence award. It was published as the leading paper of its issue, with commentaries by leading scholars. The overall research strategy is to develop and solve the model, gather empirical priors from a field survey, run empirically-based simulations to obtain a proof-of-concept, and then empirically test the algorithm in the field. The implementation of the project includes the creation of a lab at the EUR with graduate students. The EUR will give full support including reduced teaching load, computing resources, mentoring, research budget and students.

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