Thanks for being with us! This is our second blog on application of Data Science in Banking Sector.
Be it today or 10 years ago or anytime in future “Customer” is always at the center of business for all the sectors. Banks are no different and with the rise in competition with open markets Banks have to focus on their customers. 10 years back, any Indian was not able to think “State Bank of India” will have 60 + fully operating branches outside India. Today, I am proud that Bank of America is facing a stipe competition from SBI. See how dynamic the market is? You never know when you are going to face a competitor and what challenges will be there tomorrow. “Customer Relationship Management” itself is a vast subject and different marketers have dealt it differently. Just think of a brand “Nokia” today it’s a history. Let’s not go deep into this and instead focus on how application of Data Science for banks help them to be more customer oriented and how SeekACE does better segmentation of customers to address their requirements in a better way.
Customer Lifetime Value
Formally speaking, a Customer Lifetime Value offers a discounted value of the future revenues that are contributed by the customer. Banks are often required to predict future revenues based on past ones. Also, banks want to know the retention of customers and if they will help to generate revenues in the future as well. Banks want their customers to be satisfied and nurture them for the current as well as future prospects. Businesses like banking sectors are required to predict their customer lifetime value. Data Science in banking plays an essential role in this part.
With predictive analytics, banks can classify potential customers and assign them with significant future value in order to invest company resources on them. The classification algorithms help the banks to acquire potential customers. At SeekACE under the guidance of our academic group, the development team has worked on various classification algorithms to do the predictive analysis and continuously increase its efficiency. Banks require a comprehensive view of the customers to channel their resources in an optimized manner.
There are various tools that are used in data preprocessing, cleaning and prediction such as Classification and Regression Trees (CART), Generalized Linear Models (GLM), etc. This allows the banks to monitor their customers and contribute towards the growth and profitability of the company.
In customer segmentation, banks group their customers based on their behavior and common characteristics in order to address them appropriately. In this scenario, machine learning techniques like classification and clustering play a major role in determining potential customers as well as segmenting customers based on their common behaviors.
One popular clustering technique is K-means, that is widely used for clustering similar data points. It is an unsupervised learning algorithm, meaning that the data on which it is applied does not have any labels and does not possess an input-output mapping. Some of the various ways in which customer segmentation helps the banking institutions are –
Identification of customers based on their profitability.
Segmenting customers based on their usage of banking services.
Strengthening relationships with their customers.
Providing appropriate schemes and services that appeal to specific customers.
Analyzing customer segments to implement and improve services.
We have developed an education center “Study@SeekACE”, wherein various professionals from different fields like Mathematics, Statistics and Computer Science background collaborate and co-operate with each other to develop and refine solutions practically useable by banks.
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