According to a study conducted by Efma, one in two customers is willing to change banks in the next six months. The reason being? The lack of personalized products and services. At a time when competition between banks is raging, it is essential that they change their methods in order to build a sustainable banking relationship and reduce their churn rate.
PREDICT DISENGAGEMENT THROUGH BIG DATA
As the cost of acquiring a customer is higher than the cost of retention, the banks try to protect themselves against an increase in the churn rate.
In this effort, customer behavior analysis can serve as an early indicator for banks. To do so, they are looking to get a 360-degree global view of their customers and their interactions on different exchange channels such as banking visits, customer service calls, web transactions or mobile banking. This allows them to detect warning signs, such as reducing transactions or cancellation of automatic payments. This will allow them to take specific steps in avoiding unsubscriptions.
However, increasing the volume, variety, and velocity of the data needing to be exploited has made it nearly impossible to store, analyze, and retrieve useful information through traditional data management technologies.
Big Data technologies address these challenges by solving data management problems by storing, analyzing and retrieving the massive volume and variety of structured and unstructured data while evolving elastically as data increases. Also allowing banks to benefit from real-time interactions with their customers.
CUSTOMIZATION OF THE OFFER
Given the commoditization of financial services, banks must seek to differentiate and retain customers in other ways. This is big data’s main purpose, to be able to contribute to the construction of a perennial and adequate relation with its customers.
And to improve the customer experience, it is important to analyze the masses of structured and unstructured data (social media posts, email, etc …). This makes it possible to have much more detailed knowledge of the customers and thus to offer them the right product at the right moment wirh the most suitable distribution channel. This is essential as, according to the Deloitte1 study, « only 8% to 12% of clients feel that they have benefited from adapted support from their bank during what is considered an important period « .
As one of the managers of Data Publica explains it, « classical marketing is based on partial and often obsolete information. Predictive marketing makes it possible to solve this drawback as it is based on the search of « fresh » data from the web, from social networks and from already existing data; all these different sources of data are then being processed and combined with those of customer files. It is therefore preferable for banks to bet on predictive marketing and big data since they can double or even triple the efficiency of commercial prospecting.
The financial industry seems to be fully aware as 58% of 100 global banking institutions surveyed by Efma and Infosys believe that these technologies will play a crucial role in improving the knowledge of their customers. Moreover, the financial industry has spent 105 billion euros in the cloud and big data. And that should rise by 30% in 2019.
MINIMIZING FINANCIAL RISKS
The impact of big data is particularly expected for PFM (Personal Financial Management) and scoring applications for loans.
Thanks to the huge amounts of collected data, banks can draw a very precise portrait of loan applicants. Thus, they minimize financial risks by means of predictive models affecting scoring by borrower and suggesting associated financing conditions.
Big data technologies have rapidly become a business imperative, providing not only solutions to the challenges of developing banking relationships, but also to transforming bank organization and processes. With the volume and variety of available information, banks have the advantage of having a lot of information. This allows them to gain holistic insight into their customers by unlocking slices of information held in multiple silos, giving them 360-degree customer intelligence.