User churn has become an important KPI for product operations
“50% of users worldwide have been replaced or are preparing to replace the banks they use, and in the US and Canada, the percentage of consumers changing their banks is on the rise.” ——Global Consumer Banking Survey 2012, Ernst & Young.
User churn and user engagement have become one of the most important issues for most banks
“The cost of developing a new customer is 3-8 times that of an old customer, and an old customer contributes more than ten times the amount of new customers.” For every 5% decline in user engagement, the profit of the firm will Down 25%. The cost of acquiring new users is much higher than the retention of existing users, and the re-acquisition of the lost users is more expensive. In fact, after a series of tests and studies confirmed that the loss of users is the greatest damage to the company’s profits.
Recently, Forbes magazine published an article on the company’s leadership on the lack of understanding of the customer, “the lack of positive, sustained from the enterprise or brand-related user experience, will lead to business lost 20% of the amazing year income is the reason why banks’ needs, preferences, emotions, actions, and the replacement of banks have become the most important thing for banks.
How does the socialized localization (SoLoMo) affect the user experience and the loss?
In today’s era of all things interconnected, in the explosive social media, bad news spreads surprisingly. After investigation, the survey showed that nearly 63% of users use online personal networks and social networking sites as a source of reliable bank product information. And, 45% of users in the social media to get their services to evaluate. So through the data, tracking the user’s ideas and make timely decisions to provide customers with better service and reasonable pricing strategy.
However, the user’s emotional and user experience information in different channels exists in a variety of structured and unstructured data, which may lie; more unfortunately, there is no information between the various data, there are information islands; The situation makes the bank a comprehensive understanding of the customer, the bank would like to get early customer early warning signal and start the retention measures become very difficult.
The most important thing is to understand the customer and predict the loss
In order to be able to identify potential user churn as early as possible, first of all need to analyze your user’s behavior and have a comprehensive understanding. Need to understand how the bank’s customers use banking services, call customer service calls, transactions on the site or mobile banking, or interactive on social media? These historical data can allow banks to understand earlier some of the early warning signals, such as reduced trading volume, automatic payment suspension, or any other negative experience for the user, according to these early warning to take specific measures to remedy to reduce the occurrence of the loss.
However, we also mentioned earlier that the customer’s information is not through, which makes the first time to monitor the early warning signal and take measures become very difficult; the result is that the bank from different fragments of incomplete information to develop and implement the strategy , resulting in easy loss of customers, suffered heavy losses.
How does big data help predict the potential for loss?
The rapid increase in the number, type, and speed of user data generation makes it impossible to store and not provide real-time analysis and valuable information using traditional data management techniques.
Now big data can help us solve these difficulties and balance structured and unstructured data. Such as bank access, customer call logs, web interaction logs, credit card record transaction data, and customer interaction data on social media.
Big data technology to solve the data with the increase in data can be flexible expansion, which allows banks to access the user’s real-time behavior, can better provide loss warning. In addition, the superb data matching ability to link customers in the various channels of interactive data, the establishment of management issues, through the solution to storage, analysis, retrieval of a large number of diversified structured unstructured data, and from a comprehensive 360-degree portrait. A comprehensive understanding of the customer will be converted into executable data decisions.
Establish a predictive loss model
360-degree customer portrait, for banks to predict the potential loss of customers is enough? In order to make full use of the user’s information, it is necessary to establish a feasible model for predicting the loss. The high predictions of the effective customer churn model help identify customers with high risk of loss and be able to filter the “wool party” and to construct an effect lift curve for each loss model, visualizing the use of models that are less Loss of the model played a role in upgrading.
In addition, if the banking industry cannot be targeted for a single customer marketing program, then even if the ability to accurately predict the loss of customers is not enough. Those common marketing programs based on a wide range of customer categories will lead to a decline in the rate of recovery. We need to be more refined, have a clear purpose, and targeted the development of different marketing programs to save high-risk users, reduce the wastage rate. For example, the use of collaborative filtering such machine learning algorithm can effectively provide personalized solutions.
In short, business process-based user intelligence management, combined with large data technology and sophisticated machine learning technology, will allow banks to predict and prevent the loss of users, the implementation of personalized recommendations and improve customer loyalty to achieve a new, more competitive progress.
West Bridge Technology | Big Data Precision Marketing Solutions
West Bridge Technology is a leading provider of big data products and services, is committed to providing a complete enterprise based on big data user behavior analysis of one-stop solution. Has been in Min Sheng Bank, Industrial Bank, Jiangsu Bank and other landing applications.