Without a doubt, data analytics is altering the traditional banking system. In fact, it would not be an exaggeration to say the time has come to re-assess the concept of a bank, as we know it. But that is not the subject of this post. Instead, we are going to look at data analytics in banking.
Big data has changed banking from a relation of pure finances to one of, well, financial relations, where the customer, be it retail or corporate, is at the center of this universe.
Financial Technology or FinTech combined with data analytics led by advances in tech today like machine learning has disrupted the banking industry. The focus is on the customer and augmenting the customer’s experience. The entry of non-banking players, especially at the retail level has forced traditional banks to adopt more innovative processes to deliver an enhanced customer experience.
From the early days, banks hold vast amounts of transactional data on millions of customers, much of it as part of regulatory compliance. Now, faced with competition and an eroding bottom line, banks are slowly waking up to the fact that they are sitting on a gold mine, and that by using machine learning and data science tools, this data can be transformed into an opportunity to generate new revenue opportunities.
For example, data analytics is now being used by banks to make their marketing more personalized. The more a consumer decides to share details about his life, the more he is enabling the bank to deliver personalized services.
Analytics is making banks smart, and customers smarter, because predictive analytics throws up estimated services that a customer may want.
As part of this proactive approach, data scientists utilize the behavioral, demographic, and historical purchase data points to predict the probability of a customer’s response to a promotion, for example.
Here’s what Capgemini has to say about this entire movement- from traditional to digital.
Augmenting customer experience has been the need of the hour as customers increasingly adopt digital products and services. In pursuit of more nimble processes and innovative approaches, traditional retail banks are enthusiastically investing in digital transformation and FinTech collaboration.
Banks can use data analytics for the following:
Customer Lifetime Value (CLV) prediction: Like in any other retail business, data analysis can be used by banks to understand all the value it can derive from their total relationship with a customer. This is getting to be a very important play for it helps banks understand in a scientific manner which customer relationship is worth nurturing in the long term.
Several analytics tools and approaches exist today to develop a CLV model, including ones like generalized linear models (GLM), Stepwise regression, Classification, and so on. Building a predictive model to determine future marketing strategies based on CLV is becoming important to maintain good customer relations.
Customer segmentation: In today’s analytical world that we live in, this is a given. The segmentation of customers for the successful allocation of marketing resources is a factor that cannot be over-stressed. Customer segmentation is designed to help in the retention of customers. It means the bunching together of a group of customers based on either their behavior, characteristics, and some stuff.
Customer support: This is one area where banks are still lagging. Superior customer support service is the foundation for a healthy, long-term relationship with clients. Banks, after all, are part of the service industry, which means responding to customers’ questions and complaints promptly. This is where data analytics can play a vital role in tracking complaints, suggestions to keep the customer happy.
Before you sign off, if you are an entity in the Business, Financial or Insurance sectors, Express Analytics has the right analytical solution for you. All you need is to get in touch with us.
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