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How Big Data Can Help Reduce the Attrition Rate of Banks

According to a recent study, one in two customers is willing to change banks in the next six months. What does this mean? It means some backs are lack of personalized products and services. At a time when competition between banks is raging, it is essential that they change their methods to build a sustainable banking relationship and reduce the attrition rate, or churn rate, a term to determine a bank’s ability to retain customers.

Anticipate Disengagement with Big data

As the cost of acquiring a customer is higher than the cost of retention, the banks try to guard against an increase in the attrition rate.

In this effort, customer behaviour analysis can serve as an early indicator for banks. To do this, they are looking for a 360-degree global view of them 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 stopping automatic payments. This will allow them to take specific steps to avoid unsubscription.

However, increasing the volume, variety, and velocity of the data 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, and also allowing banks to benefit from real-time interactions with their customers.

Customizing the Offer

Given the commoditization of financial services, banks must seek to differentiate and retain customers in other ways. It is here all the interest of the Big Data that to be able to contribute to the construction of a perennial and adequate relation with its customers.

To improve the customer experience, it is important to analyze the mass of structured and unstructured data (publication, email, etc …). This makes it possible to have a much more detailed knowledge of the customers and thus to propose them the right product at the right moment by the most suitable distribution channel.

Classical marketing is based on partial and often obsolete information. Predictive marketing makes it possible to solve this drawback since it is based on the search for “fresh” data on the web, on social networks and via data, which data are then combined with those of customer files. It is therefore preferable for banks to leverage the predictive marketing and Big Data since these little wind to double or even triple the effectiveness of business development .

Minimize Financial Risks

The impact of Big Data is particularly expected for PFM (Personal Financial Management) and scoring applications for loans.

With the wealth of data collected, 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 quickly become a business imperative to provide not only solutions to the challenges of developing the banking relationship, but also the transformation of the processes and organization of banks. With the volume and variety of information available, banks have the advantage of having a lot of data. This allows them to gain holistic insight into their customers by unlocking slices of information held in multiple silos, turning them into the 360-degree customer intelligence.

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