Machine learning (ML) can identify customer churn indicators prior to loss, allowing opportunity for the brand to intervene and maintain critical repeat business.
Because repeat customers are a significant revenue source, predicting customer churn in advance provides high-value actionable information. With ML algorithms identifying customers likely to churn within a specific timeframe, businesses can intervene with targeted messaging and incentives to at-risk customers. ML can identify numerous signals of customer behavior before loss, categorizing each by risk level and action required.
As many indicators for churn are lagging indicators, significant damage has occured before at-risk customers are identified. As a result, ML must employ more than classification models to identify churn/no-churn customer pools. Instead, unsupervised anomaly detection models can be used to identify attribute outliers most relevant for churn, and associate each by priorities for brand action.
Anomaly Detection using Local Outlier Factor
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