Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Detalhes bibliográficos
Principais autores:	Hossain, Shoumik, Fahmiduzzaman, Quazi, Payel, Nehrin Siddique, Hossain, Mohammad Shahriar, Hossain, Nabil
Outros Autores:	Mostakim, Moin
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2022
Assuntos:	Customer segmentation Long Short-Term Memory (LSTM) Networks Deep learning Multi-layer perceptron K-nearest Neighbor (KNN) algorithm Machine learning Computer algorithms.
Acesso em linha:	http://hdl.handle.net/10361/15986

id	10361-15986
record_format	dspace
spelling	10361-159862022-01-26T10:10:23Z Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry Hossain, Shoumik Fahmiduzzaman, Quazi Payel, Nehrin Siddique Hossain, Mohammad Shahriar Hossain, Nabil Mostakim, Moin Majumdar, Mahbub Alam Department of Computer Science and Engineering, Brac University Customer segmentation Long Short-Term Memory (LSTM) Networks Deep learning Multi-layer perceptron K-nearest Neighbor (KNN) algorithm Machine learning Computer algorithms. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-47). In competitive markets, it is costly to attract new customers in the business as they already have a wide customer base; that being the case, businesses spend a healthy budget to bring back customers who have once been with them. Despite the business investing heavily in trying to retain their customers, the re-engagement of the customers is not satisfactory as many businesses use intuition, experience, and traditional methods for marketing literature. Moreover, there is a global pandemic (COVID-19) that is hampering businesses everywhere. While the majority of the businesses are operating on a loss, a few small businesses are already being shut down. Thus, there is a need to increase the pro tability of the businesses and retain their customer base. To increase customer turnover and re-engagement, this paper focuses on the implementation of intelligent business practices using machine learning and neural networks. The paper focuses on analyzing the customer behavior based on their purchase behavior and transactional values for customer retention. The classi cation is done to identify the customers who are pro table to the business and customers who are likely to churn due to various reasons. In this paper, we proposed a Multi-layer perceptron (MLP) to segment the customers according to RFM methodology and customer pro tability index. The result of MLP was compared with K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) as the later models have been widely used. Additionally, Bidirectional Long Short-Term Memory (LSTM) models have been implemented for primary customer classi cation and sales prediction. The prediction model is an attempt to reduce - nancial loss on marketing campaigns for re-engagement of customers in the business. Shoumik Hossain Quazi Fahmiduzzaman Nehrin Siddique Payel Mohammad Shahriar Hossain Nabil Hossain B. Computer Science 2022-01-24T06:09:25Z 2022-01-24T06:09:25Z 2021 2021-01 Thesis ID 17101322 ID 17101307 ID 17101508 ID 17101239 ID 16301134 http://hdl.handle.net/10361/15986 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Customer segmentation Long Short-Term Memory (LSTM) Networks Deep learning Multi-layer perceptron K-nearest Neighbor (KNN) algorithm Machine learning Computer algorithms.
spellingShingle	Customer segmentation Long Short-Term Memory (LSTM) Networks Deep learning Multi-layer perceptron K-nearest Neighbor (KNN) algorithm Machine learning Computer algorithms. Hossain, Shoumik Fahmiduzzaman, Quazi Payel, Nehrin Siddique Hossain, Mohammad Shahriar Hossain, Nabil Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Mostakim, Moin
author_facet	Mostakim, Moin Hossain, Shoumik Fahmiduzzaman, Quazi Payel, Nehrin Siddique Hossain, Mohammad Shahriar Hossain, Nabil
format	Thesis
author	Hossain, Shoumik Fahmiduzzaman, Quazi Payel, Nehrin Siddique Hossain, Mohammad Shahriar Hossain, Nabil
author_sort	Hossain, Shoumik
title	Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
title_short	Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
title_full	Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
title_fullStr	Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
title_full_unstemmed	Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
title_sort	utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
publisher	Brac University
publishDate	2022
url	http://hdl.handle.net/10361/15986
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_version_	1814307646901059584

Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry

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