- Customer Segmentation using Machine learning Techniques
Keywords
E-commerce, Machine learning, customer segmentation, Data mining
Our study carried out a customer segmentation process by using e-commerce data. We employed ensemble method by utilizing Support vector Machine, Logistics Regression, K Nearest Neighbors, Decision Tree, Random Forest, AdaBoost Classifier and Gradient Boosting Classifier to categorize the customers. As a consequence, ensemble framework of aggregated Random Forest, Gradient Boosting and k-Nearest Neighbors achieved highest outcomes.
- Customer Segmentation in Retailing using Machine Learning Techniques
Keywords
Product Segmentation, Clustering, Business, Clients
To split the customers and goods into various groups by considering several factors is the main goal of our research. We can able to find out the true and loyal customers and interpret the consumer’s buying patterns through the splitting of customers by using various methods. We performed cluster analysis to enhance the efficiency in making decisions and to develop the precise framework. We compared several clustering methods to find out the optimal one.
- Machine Learning based prototype for Customer Segmentation using RFM
Keywords
Customer relationship management, Recency, Frequency, Monetary, K-Means, Davies-Bouldin Index
An innovative approach is suggested in our article by integrating K-Means and the Davies-Bouldin Index (DBI) to overcome the existing research problems and to find out the appropriate RFM value. We can easily evaluate the RFM values with the help of enormous amount of data. We utilized K-Means technique to generate the optimal RFM value that is based on the best K values of K-Means technique.
- A Novel Approach for Customer Segmentation and Product Recommendation to Boost Sales using Machine Learning
Keywords
K-Means Clustering algorithm, Recommendation Systems, Ensemble Learning, Manifold learning
Customer communication information and previous sales data are examined in our project to obtain the appropriate factors. We employed an unsupervised ML method named K -Means clustering to acquire various customer clusters and we utilized voting ensemble-related recommendation model to suggest top products to the customers. Our ensemble model comprises of user-based collaborative filtering, LightFM, KNNWithMeans, and neural network methods.
- Customer Segmentation Analysis leveraging Machine Learning Algorithms
Keywords
Cohort Analysis, Pareto Principle, RFM Analysis, Segmentation
Our study described several types of customer segmentation with applications and its actual world utilization. To understand the customers and offers various suggestions, we utilized several methods such as Cohort Analysis, RFM Analysis and Pareto Principle. We implemented our approach by employing various clustering techniques. In that, Spectral clustering technique provides greater end results.
- Customer Segmentation Using Machine Learning
Keywords
Libraries, Classification, Elbow method, Within-Cluster-Sum of Squared Errors (WCSS)
By utilizing Streamlit and K-Mean method, we built a web application for customer segmentation to attract the clients. We performed our task by using python with ML methods. The data is loaded and presented in a bar plot and relevant variables are detected. We employed Elbow method to acquire enormous amount of clusters. We input the variables into K-Means and the plot will describes about customers and assist the industries to enhance their profits.
- A review on Churn Prediction and Customer Segmentation using Machine Learning
Keywords
Churn Prediction, Feature Selection, Telecom
To accomplish an unsupervised task of customer segmentation, we preprocessed, examined and prepared the data to train the model. We selected relevant features by utilizing methods like PCA, information gain and correlation attribute ranking filters and LDA. Then, we carried out supervised binary categorization task named Churn forecasting. By employing various methods, we developed churn forecast and customer segmentation frameworks.
- A Hybrid Machine Learning Approach for Customer Segmentation Using RFM Analysis
Keywords
Online retail, RFM model, Decision tree, Gini index
Customer segmentation process is carried out in our paper based on the historical purchase pattern of the customers. We cluster the customers by utilizing recency, frequency, monetary (RFM) score and K-Means clustering algorithm. We generate the sub-division of each cluster by employing DT method named Gini Index to improve the segmentation performance. As a result, our suggested integrated framework offers better outcomes in segmentation process.
- Customer Segmentation Using Machine Learning
Keywords
Unsupervised data, Unsupervised Learning, Customer Base
An aim of our study is to classify customers by considering several factors like age, annual income, expenditure etc, through the use of an unsupervised ML method named K-means clustering. We developed a framework to cluster the customers into groups. We carried out processes like data collection, preprocessing, extraction of features, K-Means implementations, recommended sales plan. We utilized Elbow method to detect the ideal number of groups.
- Customer Segmentation in Tourism Industry using Machine Learning Models
Keywords
Analysis, Bayesian, regression, unsupervised, propagation, accuracy
A decision-making approach is generated in our article by using histogram, pie charts, and heatmaps. We efficiently make decisions in tourism industries by employing methods like Bayesian Inference, Descriptive Basic and LR Analysis. We produced the clusters of customers by utilizing clustering-based ML framework such as K-means, DBSCAN, Affinity Propagation, Mini Batch K-means and Optics method. In that, Mini Batch K-means outperformed others.