1. Customer management
Financial institutions also have a lot of valuable data, such as business order data, user attribute data, user income data, customer query data, wealth management product transaction data, user behavior data, etc., and a user label system can be established by opening user accounts. On this basis, combined with risk preference data, customer occupation, hobbies, consumption patterns and other preference data, the machine learning algorithm is used to classify customers, and the existing data tags and external data tags are used to portrait users. Furthermore, providing different products and service strategies for different types of customers can improve customer penetration, customer conversion rate and product conversion rate. In other words, through big data applications, financial institutions can gradually achieve fully personalized customer service goals.
2. Product management
Through the big data analysis platform, financial institutions can obtain customer feedback information, understand, acquire and grasp customer needs in time, and set up products more reasonably through in-depth analysis of data. Through big data, financial institutions can quickly and efficiently analyze the functional characteristics and favorite status of products, the value of products, the reasons for customer preferences, the life cycle of products, the profits of products and the customer base of products. If handled well, the appropriate products can be delivered to the customers in need, which is an important link in customer relationship management.
3. Sales management
With the help of big data analysis platform, various forms of user data (basic information data, wealth information data, education data, consumption data, browsing data, purchase path, customer's Weibo, customer's WeChat, customer's purchase behavior) are mined, tracked and analyzed to improve the level of precision marketing.
trait
1. Network demo. In the era of big data finance, a large number of financial products and services are displayed through the network, including fixed and mobile networks.
2. Risk management concepts and tools based on big data. In the era of big data finance, risk management concepts and tools will also be adjusted accordingly.
3. Information asymmetry is greatly reduced. In the era of big data finance, the degree of information asymmetry between consumers and providers of financial products and services is greatly reduced.
4. High efficiency. Big data finance is undoubtedly efficient. Many processes and actions are initiated and completed online, and some actions are realized automatically.
5. The service boundary of financial enterprises has expanded. First of all, as far as a single financial enterprise is concerned, its most suitable business scale has expanded. Due to the improvement of efficiency, its operating costs will be reduced. The cost curve of financial enterprises will also change.
6. Controllability and acceptability of products. Financial products presented through the Internet are controllable and acceptable to consumers.