- Customer Churn Prediction: A Global Performance Study - May 13, 2020.
This article details an automated machine-learned approach to predict customer churn and its results across selected communication service providers around the globe.
- Random Forest® vs Neural Networks for Predicting Customer Churn - Dec 26, 2019.
Let us see how random forest competes with neural networks for solving a real world business problem.
- KDnuggets™ News 19:n19, May 15: Data Scientist – Best Job of the Year!; How (not) to use Machine Learning for time series forecasting - May 15, 2019.
"Please, explain." Interpretability of machine learning models; How to fix an Unbalanced Dataset; Data Science Poem; Customer Churn Prediction Using Machine Learning; A Complete Exploratory Data Analysis and Visualization for Text
- Customer Churn Prediction Using Machine Learning: Main Approaches and Models - May 14, 2019.
We reach out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn prediction using Machine Learning.
- Data Science Projects Employers Want To See: How To Show A Business Impact - Dec 4, 2018.
The best way to create better data science projects that employers want to see is to provide a business impact. This article highlights the process using customer churn prediction in R as a case-study.
- Combating Customer Churn with AI - Nov 29, 2018.
Businesses today can use the power of AI to help determine which customers are more likely to churn, and what actions to take to keep them. In this DataRobot webinar on Dec 10 @ 1 PM EST, learn how to combat customer churn with AI.
- Make Your Data Mean More With Derived Variables - Oct 4, 2017.
The chapter begins with modeling customer attrition in the cell phone industry, moves to a review of several classic variable combinations, and offers guidelines for the creation of derived variables.
- Using Machine Learning to Predict and Explain Employee Attrition - Oct 4, 2017.
Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.
- The Challenges of Building a Predictive Churn Model - Mar 8, 2017.
Unlike other data science problems, there is no one method for predicting which customers are likely to churn in the next month. Here we review the most popular approaches.
- What is Customer Churn Modeling? Why is it valuable? - Mar 1, 2017.
Getting new customers is much more more expensive than retaining existing ones, so reducing churn is a top priority for many firms. Understanding why customers churn and estimating the risks are powerful components of a data-driven retention strategy.
- The Top 5 KPIs to Consider When Measuring Your Campaign - Feb 28, 2017.
When it comes to measuring marketing campaign performance or analysing customers in any business, below top 5 Key Performance Indicators (KPIs) needs to be used to strategically drive the business.
- How to Use Cohort Analysis to Improve Customer Retention - May 2, 2016.
Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.
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- Mode Playbook for Open Source Analytics - Dec 5, 2014.
Mode Analytics is open-sourcing their internal analysis and data visualizations which can be tailored to common data structures in SQL databases.
- WCAI Research: Desktop Software Subscription Analysis - Oct 27, 2014.
A new dataset on when and how customers renew software licenses is now available for research into software purchase behavior. Register for Nov 21 webinar and submit proposals by Dec 8.
- Top KDnuggets tweets, Sep 26-28: Any data scientist worth their salary will say you should start with a question - Sep 29, 2014.
CNN embarrassing lack of "Data Quality" - this #Scotland Independence poll adds; Statistical & Machine learning with R; Any data scientist worth their salary will say you should start with a question; Automotive Customer Churn Prediction using SVM and SOM.
- Automotive Customer Churn Prediction Results, part 2 - Sep 29, 2014.
Learn how to apply neural networks and self-organizing maps to visualize the macroscopic relationships between clients and the maintenance evolution of cars over the years.
- Automotive Customer Churn Prediction using SVM and SOM - Sep 27, 2014.
A Case Study of predicting customer churn using Life Time Cycle approach and advanced machine learning methods including SVM and Self-Organizing Mapping.
- Employee Churn 202: Good and Bad Churn - May 4, 2014.
This post extends the “quantitative scissors” approach to employee churn and examines the factors that underlie attrition cost.