Empowering CRM Through Business Intelligence Applications

Al-Zadjali, Mohamed; Al‐Busaidi, Kamla Ali

doi:10.4018/ijkm.2018100105

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…New concepts were introduced to the current work in its second phase and the third phase, the new approach was implemented by a Brazilian telecommunications operator. Authors in [6] used hypothesis testing to study the impact of business intelligence on CRM from the perspective of the employees of telecommunication companies in Oman. Tong et al discussed a way of increasing customer loyalty through the use of net promoter data mining (NPS) which depended on information gained on decision tree model and k-means clustering [7].…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Saha¹,

Tripathy²,

Masmoudi³

et al. 2022

IJACSA

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In the telecommunication industry, being able to predict customers' behavioral pattern to successfully design and recommend a suitable tariff plan is the ultimate target. The behavioral pattern has a vital connection with the customers' demographic background. Different researches have been done based on hypothesis testing, regression analysis, and conjoint analysis to determine the interdependencies among them and the effects on the customers' behavioral needs. This has presented us with ample scope for research using numerous classification-based techniques. This work proposes a model to predict customer's behavioral pattern by using their demographic data. This model was built after investigating various types of classification-based machine learning techniques including the traditional ones like decision tree, k-nearest neighbor, logistic regression, and artificial neural networks along with some ensemble techniques such as random forest, adaboost, gradient boosting machine, extreme gradient boosting, bagging, and stacking. They are applied to a dataset collected using a questionnaire in India. Among the traditional classifiers, decision tree gave the best result of 81% accuracy and random forest showed the best result among the ensemble learning techniques with an accuracy of 83%. The proposed model has shown a very positive outcome in predicting the customers' behavioral pattern.

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Section: Related Workmentioning

confidence: 99%

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Saha¹,

Tripathy²,

Masmoudi³

et al. 2022

IJACSA

View full text Add to dashboard Cite

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“…In addition, ICT can support innovation as well as research and development (R&D) in the business, government, scientific and social fields. For instance, knowledge management systems and business intelligence systems significantly improve innovation (Al-Zadjali and Al-Busaidi, 2018;Costa and Monteiro, 2016). Similarly, Wiseman and Anderson (2012) demonstrated how ICT can be used to create national innovation systems and research capacity in GCC countries.…”

Section: Information and Communications Technology For The Development Of The Innovation Pillarmentioning

confidence: 99%

Fostering the development of Oman’s knowledge economy pillars through ICT

Al‐Busaidi

2020

VJIKMS

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Purpose Information and communications technology (ICT) is the driving force and key enabler of a knowledge economy. The purpose of this study is to identify the critical ICT indicators that foster the development of the knowledge economy and its main pillars (education, innovation and economic and institutional regimes) in Oman. Design/methodology/approach The study used a qualitative approach and conducted four Delphi studies on four groups of experts (ICT experts, educators, innovation experts and economists) in Oman. Findings The results indicated that the most commonly top-listed ICT indicators of a boost in the country’s knowledge economy are related to the national level (total research and development expenditure on ICT, ICT patents as a percentage of national total and ICT as an overall priority for the government), firm level (the proportion of businesses using the internet, the proportion of businesses using computers and the proportion of businesses with a Web presence), and inhabitant level (mobile phone subscribers per 100 inhabitants, internet subscribers per 100 inhabitants and personal computers per 100 inhabitants). Originality/value ICT is the driving pillar of a knowledge economy. The literature indicated that most of the ICTs for development studies are conducted in developed countries; hence, there is a great need for investigations in the context of less developed economies such as Oman. This study can provide insights for the country on how to develop and exploit ICT to boost the development of the overall knowledge economy and its pillars and to provide guidance for exploiting ICT to gain economic value. Oman’s vision for 2020 and 2040 aims at economic diversification; the knowledge economy is a critical aspect of the country’s economic diversification. In addition, the literature indicated that the relationship between ICT and development is still not clear; hence, this study provided some insights into the context of knowledge economy development.

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Data Mining

Olson

2024

Encyclopedia of Optimization

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Empowering CRM Through Business Intelligence Applications

Cited by 5 publications

References 25 publications

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

A Machine Learning Model for Personalized Tariff Plan based on Customer’s Behavior in the Telecom Industry

Fostering the development of Oman’s knowledge economy pillars through ICT

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