a r t i c l e i n f o Keywords: Customer response predictive model Knowledge-based marketing RFM Neural networks Decision tree models Logistic regression Decision support techniques and models for marketing decisions are critical to retail success. Among different marketing domains, customer segmentation or profiling is recognized as an important area in research and industry practice. Various ...

Companies’ interest in customer relationship modelling and key issues such as customer lifetime value and churn has substantially increased over the years. However, the complexity of building, interpreting and applying these models creates obstacles for their implementation. The main contribution of this paper is to show how domain knowledge can be incorporated in the data mining process for ch...

In the telco industry, attracting new customers is no longer a good strategy since cost of retaining existing much lower. Churn management becomes instrumental in industry. As there limited study combining churn prediction and customer segmentation, this paper aims to propose an integrated analytics framework for management. There are six components framework, including data pre-processing, exp...

student withdrawal in higher education is one the important challenges in universities. this paper considers the admission of fee paid students as a business and their withdrawals as customer churn. the aim is to investigate the attrition and predicted risk of attrition to adapt interventionist polices deterrent. this study is a descriptive an applicable technique that uses quantitative and qua...

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by u...

Media companies aggressively compete for their share of the pay-per-view television market. Such share can only be kept or improved by avoiding customer defection, or churn. The analysis of customers’ data should provide insight into customers’ behavior over time and help preventing churn. Data visualization can be part of this analysis. Here, a database of pay-per-view television customers is ...

Customer segmentation is usually the first step towards customer analysis and helps to make strategic plans for a company. Similarity between customers plays a key role in customer segmentation, and is usually evaluated by distance measures. While various distance measures have been proposed in data mining literature, the desirable distance measures for various data sources and given applicatio...

Customer is an important asset in a company as it the lifeline of company. For to get new customer, will cost lot money for campaigns. On other hand, maintaining old customer tend be cheaper than acquiring one. Because that, able prevent loss customers from products we have. Therefore, churn prediction retaining customers. This paper discusses data mining techniques using XGBoost, Deep Neural N...

Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According the characteristics of longitudinal timelines multidimensional data variables B2C customers’ shopping behaviors, this paper proposes a loss model based on combination k-means segmentation support vector machine (SVM) p...

In order to accurately forecast and prevent customer churn in e-commerce, a customer churn forecasting framework is established through four steps. First, customer behavior data is collected and converted into data warehouse by extract transform load (ETL). Second, the subject of data warehouse is established and some samples are extracted as train objects. Third, alternative predication algori...