E-commerce customer churn prevention using machine learning-based business intelligence strategy

Shobana, J; Gangadhar, Ch.; Arora, Rakesh Kumar; Renjith, P.N.; Bamini, J.; Chincholkar, Yugendra devidas

doi:10.1016/j.measen.2023.100728

Cited by 25 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By analyzing data such as past usage patterns, service history, and customer feedback, companies can identify trends and patterns that indicate future behavior. For example, predictive analytics can help companies anticipate when a customer is likely to upgrade their service plan or when they may be at risk of churning (Shobana et al, 2023). This allows companies to proactively address customer needs and tailor their offerings accordingly.…”

Section: Methodologies For Personalized Service Offeringsmentioning

confidence: 99%

Enhancing Customer Service in Satellite Telecommunications: A Review of Data-Driven Insights and Methodologies for Personalized Service Offerings

Nneka Adaobi Ochuba,

Enyinaya Stefano Okafor,

Olatunji Akinrinola

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

Enhancing customer service in satellite telecommunications is a critical focus area for companies seeking to differentiate themselves in a competitive market. This Review reviews data-driven insights and methodologies aimed at delivering personalized service offerings to satellite telecommunications customers. Personalized service offerings leverage data analytics to tailor services to individual customer needs and preferences. By analyzing customer data, such as usage patterns, service history, and feedback, satellite telecommunications companies can gain valuable insights into customer behavior and preferences. These insights enable companies to offer customized services that meet the specific needs of each customer. Data-driven insights play a crucial role in enhancing customer service in satellite telecommunications. By analyzing customer data, companies can identify trends and patterns that help them understand customer needs and preferences better. This understanding enables companies to develop personalized service offerings that resonate with customers and drive customer satisfaction and loyalty. Methodologies for delivering personalized service offerings in satellite telecommunications include predictive analytics, machine learning, and artificial intelligence. Predictive analytics enables companies to forecast customer behavior and anticipate future needs, allowing them to proactively address customer needs. Machine learning algorithms can analyze large volumes of customer data to identify patterns and trends that would be difficult for humans to detect. Artificial intelligence can enhance customer service by enabling companies to automate processes and provide personalized recommendations to customers. In conclusion, enhancing customer service in satellite telecommunications requires a data-driven approach that leverages insights and methodologies to deliver personalized service offerings. By analyzing customer data and using advanced analytics and AI technologies, satellite telecommunications companies can tailor services to individual customer needs and preferences, driving customer satisfaction and loyalty. Keywords: Customer Service, Satellite, Telecommunications, Data-driven, Service Offerings.

show abstract

Section: Methodologies For Personalized Service Offeringsmentioning

confidence: 99%

Enhancing Customer Service in Satellite Telecommunications: A Review of Data-Driven Insights and Methodologies for Personalized Service Offerings

Nneka Adaobi Ochuba,

Enyinaya Stefano Okafor,

Olatunji Akinrinola

et al. 2024

Int. j. manag. entrep. res

View full text Add to dashboard Cite

show abstract

“…A. Customer Churn Prediction This sub-category has been extensively discussed in previous studies, with four studies addressing these topics [17], [18], [22], [25]. The urgency of customer churn remains relevant and continues to trend until this year, as evidenced by research published in 2023.…”

Section: Distribution Of Research Studies (Rq1)mentioning

confidence: 99%

“…The prevalence of ensemble usage indicates that the research aims to maximize performance through the combination of various models. Additionally, within the scope of supervised learning, established algorithms such as Support Vector Machine and Logistic Regression are revisited [22], [25]. Lastly, in the domain of unsupervised learning, the Hierarchical Agglomerative Clustering algorithm is utilized for clustering purposes [28].…”

Section: A Machine Learningmentioning

confidence: 99%

“…customer churn using ensemble methods, Random Forest (RF), and Light Gradient-Boosting Machine (LightGBM)[17] Sales Q1(Faritha Banu et al, 2022) Model for predicting and managing customer churn and non-churn in the business sector using advanced selection, optimalization, and classification techniques deep learning-based method to enhance the classification accuracy of sentiment analysis on product comments[19] Sales Q1(Qiu & Chen, 2022) Model for analysing environmental costs using Back Propagation Neural Network (BPNN) optimized by the NSGA-II algorithm[20] Financial Q2(Chen & Long, 2023) Model for predicting financial risks based on a neural network with a combination of Factor Analysis (FA), Particle Swarm Optimization (PSO), and Long Short-Term Memory (LSTM) techniques[21] customer churn using Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR)[22] analysis and goal extraction from customer feedback using a combination of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN)[23] Sales Q2Li et al, 2022) Implementation of Probabilistic Neural Network (PNN) for environmental cost control classification and Particle Swarm Optimization (PSO) algorithm as the decision-making system[24] Financial Q3(J et al, 2023) Customer churn prediction using a Support Vector Machine algorithm and Hybrid Recommendation Strategy to prevent churn[25] financial risks using an optimized structure combining Temporal Convolutional Network-Long (TCN) with Long Short-Term Memory (LSTM)[26] Financial Q4Wisesa et al, 2020) Model for predicting sales in the B2B context using various machine learning algorithms, with Gradient Boost performing the best[27] Sales ~(Sánchez-Torres et al, 2022) …”

mentioning

confidence: 99%

See 1 more Smart Citation

Unleashing the Potentials of Artificial Intelligence for Micro, Small, and Medium Enterprises: A Systematic Literature Review

Pamungkas,

Asyivadibrata,

Susilawati

et al. 2023

JTEKSIS

View full text Add to dashboard Cite

Artificial intelligence has undergone rapid evolution, reaching a level of sophistication where it is now considered a personal assistant, aiding humans in their daily tasks. The potential of artificial intelligence can be observed in almost every sector of life including the business sector. Limited resources and skill capabilities especially for micro, small, and medium enterprises tend to hinder progress. However, given the current advanced level of artificial intelligence, it has emerged as an innovative and cost-effective solution to support business actors. This research systematically examines and discusses the potential of utilizing artificial intelligence in the business sector from credible and scientific sources. A systematic literature review methodology is used for this study. The review encompasses Scopus-indexed journals published between 2019 and 2023, with additional open-access publications. Based on the research findings, 106 studies were found, however, after the screening process the number of studies is reduced to 13 articles. Customer churn management emerged as the most prominent utilization of artificial intelligence. On the other hand, from a technological perspective, optimization technique emerged as the most frequently addressed topic in the examined studies.

show abstract

“…The proposed method yielded F scores of 0.89, 0.96, 0.97, Matthews Correlation Coefficients of 0.89, 0.96, 0.97, and accuracies of 0.95, 0.97, 0.98. [3] presented a hybrid recommendation strategy based on SVM with targeted retention attempts for an e-commerce loss forecast. When the integrated forecasting model was used, the coverage rate, hit rate, removal rate, precision rate, and other metrics increased significantly.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based churn analysis for sellers on the e-commerce marketplace

Öztürk,

Tunç,

Akay

2023

International Journal of Mathematics and Computer in Engineering

View full text Add to dashboard Cite

The goal of this study is to develop churn models for sellers on the e-commerce marketplace by using machine learning methods. In this sense, three approaches were applied for developing the models. The dataset used in this study includes ten features, which are maturity type, maturity interval, city of the seller, total revenue of the seller, total transaction of the seller, sector type of the seller, business type of the seller, sales channel, installment option and discount type. Random Forest (RF) and Logistic Regression (LR) were used for churn analysis in all of the approaches. In the first approach, models were developed without applying preprocessing operations on the dataset. In the second and third approaches, under sampling and oversampling methods, respectively, were used to balance the data set. By using stratified cross validation on the dataset, F-Scores of the churn models were obtained. The results show that F-Scores were 0.76, 0.71 and 0.92 for the three approaches developed with RF, and 0.84, 0.68 and 0.69 for the three approaches developed with LR, respectively.

show abstract

E-commerce customer churn prevention using machine learning-based business intelligence strategy

Cited by 25 publications

References 7 publications

Enhancing Customer Service in Satellite Telecommunications: A Review of Data-Driven Insights and Methodologies for Personalized Service Offerings

Enhancing Customer Service in Satellite Telecommunications: A Review of Data-Driven Insights and Methodologies for Personalized Service Offerings

Unleashing the Potentials of Artificial Intelligence for Micro, Small, and Medium Enterprises: A Systematic Literature Review

Machine learning based churn analysis for sellers on the e-commerce marketplace

Contact Info

Product

Resources

About