Solving Customer Churn with Machine Learning
Company Size
1,000+
Country
- United States
Product
- H2O
- Hadoop
- R
- Python
Tech Stack
- Machine Learning
- Predictive Modeling
- Data Science
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
Services
- Data Science Services
About The Customer
Paypal is a global payments platform operating in 203 markets. The company has 173 million active customer accounts and processed 4 billion payments in 2014. Paypal's revenue is primarily derived from service fees as a percentage of payments made through its platform. Therefore, the number of active customers directly impacts the company's revenue. Customer churn is a critical business metric for Paypal, and the company has been working to minimize churn through various marketing and product development programs.
The Challenge
Paypal, a global payments platform, was facing a significant challenge with customer churn. The company's previous approach to identifying churn was based on specific time increments, marking a customer as churned if they hadn't used the platform within that period. However, this method was not fully accurate and impacted the effectiveness of Paypal's marketing efforts to win back customers. The company needed a more precise way to predict if and when a customer would churn and the reasons behind it. This information was crucial for the operational teams to develop new programs aimed at customer retention.
The Solution
Paypal's Senior Data Scientist, Julian Bharadwaj, and his team developed a predictive model using H2O's powerful predictive modeling and machine learning capabilities. The team used transaction and behavioral variables as well as demographic data for customers who had churned. The models could be modified across multiple parameters and run multiple times very quickly, ensuring the validity of the output. Paypal now uses H2O on Hadoop to run a predictive modeling factory - large-scale, rapid modeling - that helps the company run more sophisticated and effective marketing programs to reduce churn.
Operational Impact
Quantitative Benefit
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