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The accuracy of loyalty programs in small office home office and small and medium companies

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Resumo:Telecommunications companies have enabled societies to prosper socially and economically and be more interconnected. With the decreasing data storage and processing cost, businesses are now trying to extract actionable information from the available data. This is to improve and optimise their resource allocation and planning. Although topics of segmentation and loyalty programs are of central importance in the Telecommunications industry, research about alternative ways to increase the loyalty program is limited. This study aims to provide an overview of the Loyalty Program for SoHo and SME Markets, using data mining methods to identify, classify, and predict customers' purchases. For these segments, companies were assessed with data from 347 834 clients. TPOT SKM Confusion Matrix and several methods of correlation were used. To identify if the loyalty program was accurate and valuable for clients and to determine the current state of the art. Results indicate that loyalty programs are valuable to clients and have been widely adopted. However, the most significant feature could be used to improve brand loyalty. Findings suggest that some sectors and channel sales need to be revised. Hence, more efforts should be focused on truly valuable companies/sectors. Loyalty programs are not equally suitable for all companies. Data mining technologies can be beneficial to support Vodafone in designing a more efficient and valuable loyalty program with tailored strategies and rewards.
Autores principais:Ladeira, António Rui Mendes
Assunto:Telecommunications Loyalty Automated Machine Learning Business to Business SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 16 - Peace, justice and strong institutions SDG 17 - Partnerships for the goals
Ano:2023
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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