It is important to maintain a customer from a costly perspective rather than acquiring a new customer. If you can prevent churn by knowing in advance which of your existing customers are likely to churn, the effect can be greater than acquiring new customers. You can analyze customer attributes, behavior, engagement, and external factors to help predict the most likely customers to leave and prevent churn. Customer churn prevention can optimize corporate profitability. By predicting customer churn, financial services companies can increase customer satisfaction as well as increase customer lifetime value.
|Data||Data type||Content||Use mode|
|Input data||CSV||Customer data(gender, age, duration of use, number of accounts, credit card holdings, credit score, salary, etc.)
Customer log data(page login/out time, time to stay on page, logout one page, occurrence event, purchase or departure, customer departure)
|Output data||CSV||Customer churn prediction
|Payment||Subscription method||Attached file upon application|
|Prepaid charge||Online||Customer data required for model creation|
|API Setting||$1,800 ~|