In an industry dictated by the enrollment of customers in a particular plan or membership, the ability to retain customers directly impacts the bottom line of company performance.
Identifying driving attributes of customers likely to churn, provides the business actionable insight to make modifications or adjustments in their outreach attempts with higher risk features. Additionally, identifying which services or features of a particular customer profile that lead to high retention drive understanding of product offerings, services and customer profiles that are best suited.
Based on the last six months of customer activity, will the customer cancel in the next 30 days?
Source of data - Kraken ML Platform
Customer transaction record detail; with a single customer represented in each row of the data set.
Subsequent features or attributes of each customer are represented in the columns proceeding the account ID (customer) with a target column of cancelled included; this will be the value we will predict on.
The "History" file is used to train the model, and the "PredictMe" file is used to create predictions from the trained model. The "PredictMe" data file is identical to the "History" file, except that all values in the target column are blank.