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https://pdfs.semanticscholar.org/08dc/701ea5fa4e0f8082119de426977527bc2fc9.pdf
data is unsuitable. They point out that using demographical data for the basis of churn prediction creates a churn analysis that is dependent on the customer rather than the contract. It is also suggested that demographic data held by some companies is very limited, restricting the suitability of many existing churn-prediction systems.
https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0191-6
Mar 20, 2019 · Various researches studied the problem of unbalanced data sets where the churned customer classes are smaller than the active customer classes, as it is a major issue in churn prediction problem. Amin et al. compared six different sampling techniques for oversampling regarding telecom churn prediction problem. The results showed that the ...Cited by: 7
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.1271
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract—The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in ...
https://www.researchgate.net/profile/Dymitr_Ruta/publication/283992554_Churn_Prediction_Does_Technology_Matter/links/5bd1622145851537f5990142/Churn-Prediction-Does-Technology-Matter.pdf
customer complaints and repairs data for churn prediction. The best variables are identified and neural networks, ... suitability for churn prediction using this type of data.
https://www.researchgate.net/publication/331908752_Customer_churn_prediction_in_telecom_using_machine_learning_in_big_data_platform
We went through one more paper "Customer churn prediction in telecom using machine learning in big data platform" Abdelrahim Kasem Ahmad* , Assef Jafar and Kadan Aljoumaa [3] they have used ...
https://jfin-swufe.springeropen.com/articles/10.1186/s40854-016-0029-6
Aug 22, 2016 · Developing a prediction model for customer churn from electronic banking services using data mining ... Guo-en and Wei-dong focused on building a customer churn prediction model using SVM in the telecommunication industry. They compared this method with other techniques such as DT, artificial neural networks, naïve Bayesian (NB) and logistic ...Cited by: 10
https://www.ibm.com/support/knowledgecenter/en/SSCJHT_1.0.0/com.ibm.swg.ba.cognos.pci_oth.1.0.0.doc/c_pci_pm_insur_churn.html
The Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data. The inputs for the Churn prediction model are customer demographic data, insurance policies, premiums, tenure, claims, complaints, and the sentiment score ...
https://www.sciencedirect.com/science/article/pii/S004579061200167X
Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies ... R. Roy, D. RutaChurn prediction using complaints data. Int J Intell Technol, 13 (2006), pp. 158-163. Google Scholar.Cited by: 83
https://towardsdatascience.com/machine-learning-powered-churn-analysis-for-modern-day-business-leaders-ad2177e1cb0d
Oct 24, 2018 · Data scientists these days are using a multi-model or an ensemble approach to tackle complex business problems in predictive analytics such as customer churn. In this approach, two or more algorithms are used on the same dataset and the results are …Author: Anupam Kundu
https://www.datasciencecentral.com/profiles/blogs/using-machine-learning-to-predict-customer-behaviour
Apr 10, 2016 · Using Machine Learning to Predict Customer Behaviour. Posted by Alex Marandon on April 10, 2016 at 10:47pm; ... Complaints Management, Customer Upsell and Customer Retention. ... The algorithm choice is ultimately based on the data scientist talent and his belief the relationship between behaviour and predictors might be. The trade off between ...
https://www.sciencedirect.com/science/article/pii/S1568494614004062
Using Kernel functions, the input data are transformed into a high dimensional feature space and in many complex cases it makes the feature space easy to classify and in the case of telecommunication churn prediction, number of features are numerous and it is a good idea to …Cited by: 103
https://www.ibm.com/support/knowledgecenter/en/SSCJHT_1.0.0/com.ibm.swg.ba.cognos.pci_oth.1.0.0.doc/c_pci_pm_insur.html
In the Insurance sample, customers are profiled based on their financial sophistication. Predict churn in the Insurance case study The Churn prediction model predicts a customer's propensity to churn by using information about the customer such as household and financial data, transactional data, and behavioral data. Understand Customer ...
https://dspace.lib.cranfield.ac.uk/bitstream/handle/1826/3508/Hadden_J_2008.pdf?sequence=3&isAllowed=y
to a minimum. The proposed methodology incorporates time element in the prediction of customer churn for maximising future churn capture by identifying a potential loss of customer at the earliest possible point. Three case studies are identified and carried out for validating the proposed methodology using repairs and complaints data. Finally, the
https://www.thedatascientists.com/data-solutions/customer-churn/
Using data prediction to identify when a certain customer is at high risk of churn allows a company to do something to prevent the churn. This will prevent the loss of revenue from that customer. Data prediction is also cheaper to implement than the marketing costs …
https://www.tellius.com/machine-learning-reduce-customer-churn/
Jun 12, 2018 · Customer churn is the term used when an existing customer stops using a company’s services and/or stops buying their products. In other words, the customer chooses to cut his ties with the company. A few types of churn can’t be avoided – e.g. churn due to death. Such churn is categorized as non-addressable churn.
https://www.analyticbridge.datasciencecentral.com/forum/topics/how-to-develop-churn-prediction-model-for-telecom-company
Aug 22, 2013 · thanks Erik, You are right, the most important place to dig is in Customer Care system or better say CRM database. What I want is that what are the steps in an order way to design the prediction model and of course which model best suits for analyzing telecom data.
https://academic.oup.com/comjnl/article/60/3/410/3063777
Feb 01, 2016 · Churn prediction in telecom is a challenging data mining task for retaining customers, especially, when we have imbalanced class distribution, high dimensionality and large number of samples in training set. To cope with this challenging task of churn prediction, we propose a new intelligent churn prediction system for telecom, named FW-ECP.Cited by: 5
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.678.348&rep=rep1&type=pdf
Churn Prediction using MAPREDUCE S. Ezhilmathi Sonia, Prof. S. Brintha Rajakumar, Prof. Dr. C. Nalini ... the churn analysis using the data mining technique can be used to extract hidden predictive information in the huge ... customer complaints sample data is given below. Table 1: Customer Complaints sample data
https://addepto.com/customer-anti-churn-use-ai-to-predict-customer-churn/
Jul 05, 2019 · And, therefore, it will be much easier to identify churn probabilities and deal with them. Analyzing the collected data on your own doesn’t make a lot of sense — a human will never be as efficient here as artificial intelligence. So use it to reach the desired results on customer churn prediction.5/5
https://www.atlantis-press.com/journals/ijcis/25885044/view
A Idris, M Rizwan, and A Khan, “Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies”, Computers & Electrical Engineering, Vol. 38, No. 6, 2012, pp. 1808-1819.Cited by: 1
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