A/B testing adaptations based on possibilistic reward methods for checkout processes: a numerical analysis

Martín Blanco, Miguel Carlos and Jiménez Martín, Antonio and Mateos Caballero, Alfonso (2020). A/B testing adaptations based on possibilistic reward methods for checkout processes: a numerical analysis. In: "9th International Conference on Operations Research and Enterprise Systems, ICORES 2020", 22-24 Feb 2020, La Valeta, Malta. ISBN 978-989-758-396-4. pp. 278-285. https://doi.org/10.5220/0009356902780285.

Description

Title: A/B testing adaptations based on possibilistic reward methods for checkout processes: a numerical analysis
Author/s:
  • Martín Blanco, Miguel Carlos
  • Jiménez Martín, Antonio
  • Mateos Caballero, Alfonso
Item Type: Presentation at Congress or Conference (Article)
Event Title: 9th International Conference on Operations Research and Enterprise Systems, ICORES 2020
Event Dates: 22-24 Feb 2020
Event Location: La Valeta, Malta
Title of Book: Proceedings of the 9th International Conference on Operations Research and Enterprise Systems, ICORES 2020
Date: 2020
ISBN: 978-989-758-396-4
Subjects:
Freetext Keywords: Multi-Armed bandit, Possibilistic reward, A/B testing, Checkout process, Numerical analyses
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

A/B Testing can be used in digital contexts to optimize the e-commerce purchasing process so as to reduce customer effort during online purchasing and assure that the largest possible number of customers place their order. In this paper we focus on the checkout process. Most of the companies are very interested in agilice this process in order to reduce the customer abandon rate during the purchase sequence and to increase the customer satisfaction. In this paper, we use an adaptation of A/B testing based on multi-armed bandit algorithms, which also includes the definition of alternative stopping criteria. In real contexts, where the family to which the reward distribution belongs is unknown, the possibilistic reward (PR) methods become a powerful alternative. In PR methods, the probability distribution of the expected rewards is approximately modeled and only the minimum and maximum reward bounds have to be known. A comparative numerical analysis based on the simulation of real checkout process scenarios is used to analyze the performance of the proposed A/B testing adaptations in non-Bernoulli environments. The conclusion is that the PR3 method can be efficiently used in such environments in combination with the PR3-based stopping criteria.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainMTM2017-86875-C3-3-RUnspecifiedUniversidad Politécnica de MadridToma de decisiones multicriterio y modelos de interdependencia para la gestión de riesgos. Seguridad en ATM

More information

Item ID: 66521
DC Identifier: http://oa.upm.es/66521/
OAI Identifier: oai:oa.upm.es:66521
DOI: 10.5220/0009356902780285
Official URL: https://www.scitepress.org/Link.aspx?doi=10.5220/0009356902780285
Deposited by: Memoria Investigacion
Deposited on: 06 Apr 2021 05:51
Last Modified: 06 Apr 2021 05:51
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