An inclusive survey on machine learning for CRM: a paradigm shift

Singh, Narendra; Singh, Pushpa; Gupta, Maneesha

doi:10.1007/s40622-020-00261-7

Cited by 15 publications

(8 citation statements)

References 40 publications

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“…Therefore, analysts resort to DM and ML techniques as they can play a significant role in dealing with this massive amount of business data for supporting decision-making [6]. The application of ML techniques in business could bring a catalytic change in business [7]. A considerable number of studies investigate the application of ML techniques in BA [8][9][10][11][12][13][14][15][16][17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A considerable number of studies investigate the application of ML techniques in BA [8][9][10][11][12][13][14][15][16][17]. In their study, Singh et al [7] stated that Decision tree-based algorithms and support vector machine algorithms are the most utilized supervised learning algorithm in Customer Relationship Management. These algorithms can be utilized for customer identification, customer attraction, and customer retention.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Boosted Tree-Based Predictive Model for Business Analytics

Al-Omari¹,

Qutaishat²,

Rawashdeh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations' competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance. It is validated based on Accuracy, Precision, Recall, and F-score. The experiment findings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models. The model can also deal with a massive amount of business data and support corporations' decision-making process.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Boosted Tree-Based Predictive Model for Business Analytics

Al-Omari¹,

Qutaishat²,

Rawashdeh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…∂logP(v) ∂w ij = v i h j Data − v i , h j model (10) Here, the contrastive divergence learning function is applied to train the information, and the network parameters are updated using Equation (11).…”

Section: E(mentioning

confidence: 99%

“…Many studies have argued that traditional machine learning models require considerable time and cannot discover patterns in ideal situations, especially in large chunks of real-world data with different sources and formats. Traditional artificial intelligence (AI) models cannot fully exploit the large volumes of data available in the modern world [8][9][10]. This indicates the importance of exploring new, advanced methods of predicting customer behavior.…”

Section: Introductionmentioning

confidence: 99%

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Behavior Analysis Using Enhanced Fuzzy Clustering and Deep Learning

Altameem

Hafez

2022

Electronics

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Companies aim to offer customized treatments, intelligent care, and a seamless experience to their customers. Interactions between a company and its customers largely depend on the company’s ability to learn, understand, and predict customer behaviors. Customer behavior prediction is a pivotal factor in improving a company’s quality of services and thus its growth. Different machine learning techniques have been applied to gather customer data to predict behavioral patterns. Traditional methods are unable to discover hidden patterns in ideal situations and need to be improved to produce more accurate predictions. This work proposes a novel hybrid model comprised of two modules: a novel clustering module on the basis of an optimized fuzzy deep belief network and a customer behavior prediction module on the basis of a deep recurrent neural network. Customers’ previous purchasing characteristics and portfolio details were analyzed by applying learning parameters. In this paper, the deep learning techniques were optimized by applying the butterfly optimization method, which minimizes the maximum error classification problem. The performance of the system was evaluated using experimental analysis. The proposed approach was compared to other single and hybrid-model-based approaches and attained the highest performance in the respective metrics.

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Improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing

Filippou,

Tsiartas,

Hadjineophytou

et al. 2023

IFIP Advances in Information and Communication Technology

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Customer experience plays a critical role for a profitable organisation or company. A satisfied customer for a company corresponds to higher rates of customer retention, and better representation in the market. One way to improve customer experience is to optimize the functionality of its call center. In this work, we have collaborated with the largest provider of telecommunications and Internet access in the country, and we formulate the customer-agent pairing problem as a machine learning problem. The proposed learning-based method causes a significant improvement in performance of about 215% compared to a rulebased method.

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An inclusive survey on machine learning for CRM: a paradigm shift

Cited by 15 publications

References 40 publications

A Boosted Tree-Based Predictive Model for Business Analytics

A Boosted Tree-Based Predictive Model for Business Analytics

Behavior Analysis Using Enhanced Fuzzy Clustering and Deep Learning

Improving Customer Experience in Call Centers with Intelligent Customer-Agent Pairing

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