A Frame Work for Customer Relationship Management in Nigerian Banks Using Data Analytics

Olaniyan, Olatayo; Ojukwu, Emeka; Ogude, Cyril

doi:10.46792/fuoyejet.v4i2.402

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…This makes it a desirable area for different research work. Olaniyan et al [49] addressed the current problem of achieving customer satisfaction being faced by the Nigerian Banking industry. According to the authors, qualitative research was performed to identify the purpose of customer satisfaction.…”

Section: Business-specific Crm Applicationsmentioning

confidence: 99%

Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review

et al. 2021

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Customization of products or services is a strategy that the business sector has embraced to build a better relationship with the customers to cater to their individual needs and thus providing them a fulfilling experience. This whole process is known as customer relationship management (CRM). In this context, we extensively surveyed 138 papers published between 1996 and 2021 in the area of analytical CRM. Although this study consisted of papers from different business sectors, a fair share of focus was directed to the telecommunication industry and generalized CRM techniques usages. Different science and engineering-based data repositories were studied to ascertain significant studies published in scientific journals, conferences, and articles. The research works on CRM were considered and separated into IT and non-IT-based techniques to study the methods used in different business sectors. The main target behind implementing CRM is for the better revenue growth of the company. Different IT and non-IT-based techniques are used in the analytical CRM area to achieve this target, and researchers have been actively involved in this domain. The purpose of the research was to show the impact of IT-based techniques in the business world. A detailed future course of research in this area was discussed.

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Section: Business-specific Crm Applicationsmentioning

confidence: 99%

Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review

et al. 2021

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“…Today, banking organizations around the world are facing different types of challenges that include but not limited to global competition for deposits, loans, underwriting fees, increasing customer demands, shrinking profit margins and the need to keep up with the new technologies (1). In this situation, banking corporations about the world suddenly started to realize the importance of customer relationship management to acquire competitive edge over its rivals.…”

Section: Introductionmentioning

confidence: 99%

Customer Relationship Management in Banking in the UK Industry: Case of Lloyds Bank

Saravanan²,

et al. 2022

ECS Trans.

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The purpose of this research was to focus on how Lloyds Bank, one of the leading and largest banks of the UK, is using CRM to their advantage. After collecting secondary data, several different methods implemented by the organization were found to implement and strengthen its CRM activities. The findings show the organization is providing training to its employees so that they can become more skillful and competent while dealing with consumers. Lloyd Bank is also tracking buying behavior of its consumers and demands of its consumers based on different demographic factors. These are helping the organization to customize or improve its products and services. Besides, it is also using social media marketing along with traditional marketing methods, such as newsletters to let the consumers know about new product launches. It is also providing discount coupons to its consumers to motivate their buying behavior. Lloyds Bank is also working to increase customer loyalty by providing VIP services to its consumers. It is also continuously improving the way consumers love to communicate so that customer engagement remains uninterrupted. Besides, as a bold move, rather than focusing on all the consumers, Lloyds Bank is only focusing on most-profitable and responsible consumers.

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“…These factors are difficult to predict in advance, therefore makes future exchange rate prediction to become uncertain. Available published literature shows that numerous methods of forecasting exchange rate have been proposed by various scholars on exchange rate prediction but none of them have been shown to be superior to the other (Han and Kamber, 2006;Sewell and Shawe, 2012;Shen,Chao and Zhao, 2015;Olaniyan, Ojukwu and Ogude, 2019;Omolewa, et.al. 2021).…”

Section: Introductionmentioning

confidence: 99%

Currency Exchange Forecasting Using Sample Mean Estimator and Multiple Linear Regression Machine Learning Models

2021

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In recent time, there is an increasing growth in the amount of trading taking place in the currency exchange market. However, effective analysis and simulation tools for performing accurate prediction of these exchange rates are lacking. To alleviate this challenge, this work presents an hybrid machine learning and prediction model by suitably combining the Sample Mean Estimator (SME) simulation architecture with the multiple linear regression technique based training of feed-forward parameters. The developed model has the capability to overcome prediction inaccuracy, inconsistent forecasting, slow response due to computational complexity and scalability problems. The SME method is used to overcome the problems of uncertainty and non-linearity nature of the predictive variable as it’s always affected by economic and political factors. The implementation of the proposed currency exchange rate forecasting system is achieved through the use of a developed in-house Java program with Net Beans as the editor and compiler. Performance comparison between the present system and two baseline methods which are the Autoregressive Moving Average and the Deep Belief network techniques demonstrates that the present forecasting model out-performed the baseline methods studied. The experimental result shows that the precision rate of the present system are equal to or greater than 70%. Therefore, the present foreign exchange predictive system is capable of providing usable, consistent, efficient, faster and accurate prediction to the users consistently at any-time.Keywords- currency exchange,, feed-forward. Forecasting, Sample Mean Estimator, multiple linear regressions, prediction

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A Frame Work for Customer Relationship Management in Nigerian Banks Using Data Analytics

Cited by 3 publications

References 5 publications

Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review

Amalgamation of Customer Relationship Management and Data Analytics in Different Business Sectors—A Systematic Literature Review

Customer Relationship Management in Banking in the UK Industry: Case of Lloyds Bank

Currency Exchange Forecasting Using Sample Mean Estimator and Multiple Linear Regression Machine Learning Models

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