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 reassess the concept of a bank, as we know it. But that is not the subject of this post. Instead, we will look at data analytics in banking.
Big data has changed banking from a purely financial relationship to an economic one, where the customer, whether retail or corporate, is at the center of this universe.
Financial Technology (FinTech), combined with data analytics enabled by advances in technology today, such as machine learning, has disrupted the banking industry. The focus is on the customer and on augmenting their 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 have held vast amounts of transactional data on millions of customers, much of it for 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 opportunities to generate new revenue.
For example, data analytics is now used by banks to personalize their marketing. The more a consumer shares details about his life, the more he enables 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 use behavioral, demographic, and historical purchase data 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.
Applications of Data Analytics in the Banking Industry
Banks can use data analytics for the following:
Customer Lifetime Value (CLV) prediction: As in any other retail business, data analysis can help banks understand the value they derive from their entire relationship with a customer.
This is becoming a significant play, as it helps banks understand, in a scientific manner, which customer relationships are worth nurturing in the long term.
Several analytics tools and approaches exist today for developing a CLV model, including generalized linear models (GLM), Stepwise regression, Classification, and others.
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Building a predictive model to inform future marketing strategies based on CLV is becoming increasingly important for maintaining good customer relations.
Customer segmentation: In today’s analytical world, this is a given. The segmentation of customers for the successful allocation of marketing resources cannot be overstressed.
Customer segmentation is designed to help retain customers. It means the grouping of customers based on their behavior, characteristics, or other factors.
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 promptly to customers’ questions and complaints. This is where data analytics can play a vital role in tracking complaints and 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 to do is get in touch with us.
