Antichurn models
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Need
Predict the churn probability of each customer based solely on their browsing behavior outside the client’s website (wherever its banners are), without any socio-demographic or contract information. No distinction is made between recent and distant browsing behavior in this initial phase.
Approach
Implement a machine learning system enriched with sophisticated user profiling tailored to meet specific needs.
Result
A server-side solution that takes user profiles as input and, through data enrichment processes, returns churn probability and other KPIs. The last quantile shows a churn likelihood up to 400% higher than the first, confirming the model's ability to successfully differentiate high-risk customers.
Categories
Type
AI & Data Science
Need
Benefit
Business function
Industry
Telco, Media & Entertainment
, Utilities
, Publishers
, Finance
, Software
, Logistics
, Associations
, Goods Production & Distribution
, Retail Distribution
Business Model
Business Size
