2007年1月1日 · We then review general approaches to customer segmentation, with an emphasis on the most powerful and flexible analytical approaches and statistical models. This begins with a discussion of...

Abstract—Customer segmentation has seen major growth in all sectors in the last decade. Several techniques have been devised to analyze customer behavior through loyalty, purchases, recency, frequency and monetary to develop eficient marketing strategies that cater to …

2022年4月19日 · In this paper, we have studied the behaviour of past customer using segmentation process, RFM model, k means clustering and build a voting classifier model to predict what item a customer...

Customer segmentation using the Gaussian Mixture Model (GMM) is a powerful technique that leverages statistical models to identify underlying patterns within diverse customer data. GMM assumes that the data is generated by a mixture of several Gaussian distributions, allowing for a flexible representation of complex customer behavior.

Customer segmentation, a fundamental aspect of modern marketing and business strategy, involves classifying customers into distinct groups based on shared characteristics and behaviors. This project leverages the power of machine learning, specifically the K-means clustering algorithm, to achieve this goal.

2023年9月8日 · Every customer's Recency, frequency and monetary (RFM) scores are computed based on the available data. A churn metric that indicates whether or not the customer has made a transaction in a...

A customer segmentation model is a method of dividing a large population into manageable groups based on their common characteristics. Depending on what your business does and who your customers are in general, there are several ways to segment your larger customer base into these smaller subgroups. Types of customer segmentation models:

outlines a comprehensive framework for customer segmentation that integrates traditional clustering algorithms with advanced machine learning methods. The hybrid model merges both supervised and unsupervised learning approaches to optimize segmentation results, leveraging both labeled and unlabeled data to improve accuracy

Customer seg-mentation can also help organizations to understand their customers better and to find latent business opportunities. There are many different approaches to customer segmentation in the consumer markets, but the four major approaches are behavioural, demographic, geographic, and psychographic segmentation.

In this paper, we propose an R+FM model which configures the segmentation according to the business changes and clusters customers using K-Means. We applied our model on Digikala company, the biggest E-Commerce in Middle East, and compared our model with the Digikala's previous RFM model which used Customer Quantile Method.