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Use Case

Predict Customer Churn


No company likes to see customers leave, but it’s important to know which ones have left and even more important to try to understand why they’ve left. Predictive churn modeling makes use of the data that departed customers have generated over time and helps companies understand and keep other customers from leaving.

Risk Reduction

Prevent churn instead of just mitigating by uncovering the factors that lead to churn

Bottom-Line Returns

Focus retention efforts based on churn factors you’re most able to impact

Workforce Upskilling

Empower business with predictive model skills to understand impact of retention efforts over time

Business Problem

Churn rate describes the rate at which customers abandon a product or service. Churn rates are studied most closely in subscription-based products and services — everything from traditional cable or gym memberships to streaming entertainment and gaming devices. For example, churn is a top-of-mind metric for wireless carriers, especially as they go head-to-head with wireline companies offering internet access and video.

The antidote for churn is high customer satisfaction. But different things satisfy different customers. You can’t prevent every churning customer from departing, but you can try to better understand why they’ve departed in the hope of preventing more churn by other customers. Unless you can use data to reliably determine what prompts customers to stay or leave and which groups of customers are likely to churn in the future, your company is at a competitive disadvantage.

Alteryx Solution

The role of analytics in addressing customer churn is to use historical data to predict which users are likely to leave so the company can induce them to stay. The best model connects the right inducement with the right customer at the right time, using algorithms such as support vector machines, random forest, or k-nearest neighbors. It also balances the trade-off between precision (correctly predicting a churning customer) and recall (number of predictions that were actually successful).

Alteryx Designer allows the creation of a churn prediction model directly in a workflow. Utilizing Alteryx for data science with Python, users can train models using existing customers that have already left. Logistic regression can be used to separate out customers that churned based on business failures. This model can then be used to create a list of high-risk customers and action can be taken to prevent churn.


1 – Data Access

Load in customer churn data from Salesforce or other Customer Relationship Management Tools (CRM)

2 – Data Prep

Utilize Boosted Model tool from the Predictive Analytics Toolset to classify customers

3 – Automated Results

Export model and create actions to address factors that most impact churn


Additional Resources

Analytics Automation for Customer Intelligence
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Starter Kit for Marketing Analytics
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Starter Kit for Marketing Analytics for Adobe
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Customer Journey Analytics
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