Customer value management is aimed at pre-loan access stage and post-loan silence stage, to provide a set of customers’ loan demand analysis service for credit agencies. The service employs big data technology to analyze and build models through customers’ historical borrowing behaviors, internet behaviors and financial attributes with the combination of the characteristics of the credit products, and assess their current borrowing necessity in a comprehensive way.
Credit agencies can mobilize interest radar product in real time and check customers’ responding scores concerning borrowing necessity. The size of the scores directly reflects the current demand condition of customers.
Tongdun’s interest radar/silence activating product is in support of checking customers’ responding scores concerning borrowing necessity by the product segment (small sum of cash borrowing, large sum of cash borrowing, consumption installment, car mortgage, etc.) and the scenario segment (new customers, and customers in stock, etc.).
Tongdun’s risk pre-screening product can be combined to identify whether the customer falls into the category of high-risk group or not through checking the massive list of risk data, and help the credit agency to identify the high-quality target group.
Tongdun’s responding scores concerning customer borrowing necessity has covered hundreds of millions of population in the dimension of mobile phone number and device serials, which enjoys a high degree of coverage of on-line population.
Tongdun’s interest radar/silence activating product support on-line (real-time) mobilization mode, which helps credit agencies and media platforms realize fast docking and conduct precision marketing onto customer groups with high willingness.
The customer value management is based on Tongdun’s accurate, reliable and widely-covered credit behavior data, the trans-industry behavior data and relevant data concerning device attributes. The refresh data can immediately reflect the cash necessity of the customers of the credit agency, by means of which, detailed guidance can be given to the agency on the refinement operation of new and old customers.
The customer value management is based on machine learning algorithms to predict the likelihood of customers’ response to credit products as well as activeness characteristics extracted on the basis of customer behaviors, and it enjoys sound comprehensive predictive performance and rule-based interpretability.