Customer lifetime value is an estimate of the benefits a company will gain from their relationship with a customer. The significance of this measure is growing as it assists with making and maintaining helpful associations with selected customers, generating higher benefits and business development. Procuring and holding productive clients is a constant test for banks. As competition rises, banks need a 360◦ view of every client to effectively center their assets. As large quantities of data, such as customer’s securing and weakening, utilization of banking applications and services, their volume and profits, and customer attributes (geography, demography, market information, etc.) must be taken into account, that’s where data science comes in.

Real time analysis on banks’ huge quantities of customer data empowers them to convey exceptionally customized services that leverage knowledge derived from conduct, budget history, social networking, and information feedback. Banks can then use this data to create meaningful reports to plan new services and products. Data science teams can study customer behavior to find precisely when and where clients need the most guidance and support them with better administrations of managing money, social-segment patterns, area, etc.

In modern times, banks collect, analyze, and store data, but with the use of machine learning, this process has become more efficient. Thanks to the growing use of digital banking, there is a larger production of customer data storage. The only way to successfully complete this is with the use of machine learning. With access to information regarding customer behaviors, interactions, and preferences, companies can produce better results from customer interactions.